Multi-body simulations are given more emphasis over physical tests owing toenvironmental, financial, and time requirements in the competitive automotive industry. Thus,it is imperative to develop models to accurately predict and analyse the system's behaviour.This thesis focuses on developing an air suspension model with Electronic Level Control thathas the ability to communicate with other air springs in a pneumatic circuit thus replicating thepneumatic connection in actual truck and regulate the ride height of the vehicle.To accomplish this, a comprehensive literature study is performed to identify an effectivecontrol variable to manipulate the air springs. This is done by understanding the working andthermodynamic principles of air suspension, understanding various Scania pneumaticconfigurations, and decrypting the working of the Electronic Level Control.Different methods for implementing the model through the identified control variable arediscussed. A brief explanation of the necessary physical tests performed to validate the modelis given. An extensive description of implementation of the static and dynamic model inADAMS through command batch script coding is provided.The developed static model is validated by comparing the results from simulations and the testdata. The axle weights have an error of maximum 6% and the pressure in the air springs havean error of maximum 9% which can be owed to neglection of hysteresis in the air springcharacteristics and using mean values to compare the data. The dynamic model is validated byaltering the ride height level and observing the response of the model. The results obtainedindicate the developed Electronic Level Control is able to regulate the ride height at the desiredlevel.The robustness of the model is validated by modifying the developed model for longitudinalpneumatic connection and for a truck with trailer model. The results indicate the developedmodel is capable to perform satisfactorily and conform to the Scania tolerance limits.Thus, an appropriate control variable for the air springs model is identified. Static and dynamicmodel to identify the suitable pressure in the air springs and thus, the force in the air springs isdeveloped which helped in drastically reducing the manual iterative work that was required.

In early-stage vehicle design, there is a significant lack of knowledge about the impact of design requirements on the design of subsystems, theresulting knock-on effects between subsystems and the vehicle’s overall performance. This leads to a sub-optimal vehicle design with increaseddesign iterations. To mitigate this lack of knowledge, a cross-scalar design tool consisting of an induction motor model is presented in this paper.The tool calculates the motor’s attributes, namely its volume, mass, and the performance it can deliver to satisfy a given drive cycle’s requirements.This is achieved by breaking down the drive cycle requirements into motor parameters from which the various power losses are derived. Thesekey losses are then utilised to develop the torque/speed curve. Furthermore, it is proposed that the motor’s attributes can be used to design othersubsystems and consequently analyse their interaction effects. For example, the motor’s attributes can be used to design regenerative brakes andconsequently analyse their influence on brake wear, lifetime, and energy savings. Thus, the design tool enables the design of efficient vehicles withminimised design iterations by analysing the influence of design requirements on the subsystem’s design and the consequent interaction effectsamong the subsystems and on the vehicle’s overall performance.

The paper aims at describing the activities developed within the project ASTONRAIL (Advanced approaches and practices for rail training and education to innovate rail study programmes & improve rail higher education provision), funded by ERASMUS+ Programme. The project target is to provide a portfolio of new rail skills development strategies, approaches and professional practices in order to strengthen and modernize Europe’s current rail higher education system.

Direct numerical simulations, performed with a high-order spectral-element method, are used to study coherent structures in turbulent pipe flow at friction Reynolds numbers Re-tau = 180 and 550. The database was analysed using spectral proper orthogonal decomposition (SPOD) to identify energetically dominant coherent structures, most of which turn out to be streaks and quasi-streamwise vortices. To understand how such structures can be modelled, the linear flow responses to harmonic forcing were computed using the singular value decomposition of the resolvent operator, using the mean field as a base flow. The SPOD and resolvent analysis were calculated for several combinations of frequencies and wavenumbers, allowing the mapping out of similarities between SPOD modes and optimal responses for a wide range of relevant scales in turbulent pipe flows. In order to explore physical reasons behind the agreement between both methods, an indicator of lift-up mechanism in the resolvent analysis was introduced, activated when optimal forcing is dominated by the wall-normal and azimuthal components, and associated response corresponds to streaks of streamwise velocity. Good agreement between leading SPOD and resolvent modes is observed in a large region of parameter space. In this region, a significant gain separation is found in resolvent analysis, which may be attributed to the strong amplification associated with the lift-up mechanism, here understood as nonlinear forcing terms leading to the appearance of streamwise vortices, which in turn form high-amplitude streaks. For both Reynolds numbers, the observed concordances were generally for structures with large energy in the buffer layer. The results highlight resolvent analysis as a pertinent reduced-order model for coherent structures in wall-bounded turbulence, particularly for streamwise elongated structures corresponding to near-wall streamwise vortices and streaks.

In this paper, an integrated artificial neural network (ANN) and multi-objective genetic algorithm (GA) are developed to optimize the resistance spot welding (RSW) of AZ61 magnesium alloy. Since the stability and strength of a welded joint are strongly dependent on the size of the nugget and the residual stresses created during the welding process, the main purpose of the optimization is to achieve the maximum size of the nugget and minimum tensile residual stress in the weld zone. It is identified that the electrical current, welding time, and electrode force are the main welding parameters affecting the weld quality. The experiments are carried out based on the full factorial design of experiments (DOE). In order to measure the residual stresses, an X-ray diffraction technique is used. Moreover, two separate ANNs are developed to predict the nugget size and the maximum tensile residual stress based on the welding parameters. The ANN is integrated with a multi-objective GA to find the optimum welding parameters. The findings show that the integrated optimization method presented in this study is effective and feasible for optimizing the RSW joints and process.

An important facet of creating high-quality vehicles is to create components that are quiet and smooth under operation. In reality, however, it is challenging to measure the sound that some automotive components make under load because it requires specialist facilities and equipment which are expensive to acquire. Furthermore, the motors used in testbeds drown out the noise emitted from much quieter components, such as a Power Transfer Unit (PTU). This thesis aims to solve these issues by outlining the steps required to virtually estimate the acoustic radiation of a PTU using the Transmission Error (TE) as the input excitation via multi-body dynamics (MBD). MBD is used to estimate the housing vibrations, which can then be coupled with an acoustic tool to create a radiation analysis. Thus, creating a viable method to measure the acoustic performance without incurring significant expenses. Furthermore, it enables noise and vibration analyses to be incorporated more easily into the design stage.

This thesis analysed the sound radiated due to gear whine which arises due to the TE and occurs at the gear mesh frequency and its multiples. The simulations highlighted that the TE can be accurately predicted using the methods outlined in this thesis. Similarly, the method can reliably obtain the vibrations of the housing. The results from this analysis show that at 2000 rpm the PTU was sensitive to vibrations at 500, 1000 and 1500 Hz, the largest amplitude being at 1000 Hz. Furthermore, the Sound Power Level (SWL) was proportional to the vibration amplitudes in the system. Analytical calculations were conducted to verify the methods and showed a strong correlation. However, it was concluded that experiments are required to further verify the findings in this thesis.

In this contribution, we present a robust and efficient computational framework capable of accurately capturing the dynamic motion and large deformation/deflection responses of highly-flexible rods interacting with an incompressible viscous flow. Within the partitioned approach, we adopt separate field solvers to compute the dynamics of the immersed structures and the evolution of the flow field over time, considering finite Reynolds numbers. We employ a geometrically exact, nonlinear Cosserat rod formulation in the context of the isogeometric analysis (IGA) technique to model the elastic responses of each rod in three dimensions (3D). The Navier–Stokes equations are resolved using a pressure projection method on a standard staggered Cartesian grid. The direct-forcing immersed boundary method is utilized for coupling the IGA-based structural solver with the finite-difference fluid solver. In order to fully exploit the accuracy of the IGA technique for FSI simulations, the proposed framework introduces a new procedure that decouples the resolution of the structural domain from the fluid grid. Uniformly distributed Lagrangian markers with density relative to the Eulerian grid are generated to communicate between Lagrangian and Eulerian grids consistently with IGA. We successfully validate the proposed computational framework against two- and three-dimensional FSI benchmarks involving flexible filaments undergoing large deflections/motions in an incompressible flow. We show that six times coarser structural mesh than the flow Eulerian grid delivers accurate results for classic benchmarks, leading to a major gain in computational efficiency. The simultaneous spatial and temporal convergence studies demonstrate the consistent performance of the proposed framework, showing that it conserves the order of the convergence, which is the same as that of the fluid solver.

Combustion engines in boats cause several environmental problems, such as greenhouse gas emissions and acidication of oceans. Most of these problems can be reduced by replacing the combustion engines with electric boats. The limited range is one of the main constraints for electric boats, and in order to decrease the energy consumption, applicable resistance prediction methods are necessary in the hull design process. X Shore, which is a start-up company in the electric boat sector, lacks a systematic way of predicting resistance in an early design phase. In this study, four well-known methods - CFD, Holtrop & Mennen, the Savitsky method and model test - have been applied in order to predict resistance for a test hull. The study is limited to bare hull resistance and calm water conditions. CFD simulations are applied using the software ANSYS FLUENT 19:0. The simulations were based on the Reynolds Average Navier-Stokes equations with SST k-ω as turbulence model together with the volume of fluid method describing the two-phased ow of both water and air surrounding the hull. The semi-empirical methods, Holtrop & Mennen and the Savitsky method, are applied through a program in Python 3, developed by the authors. The results from each method have been compared and since model tests have been conducted outside of this study, the model test results will serve as reference. To evaluate the methods, a number of evaluation criteria are identied and evaluated through a Pugh Matrix, a systems engineering tool. Holtrop & Mennen predicts the resistance with low accuracy and consistency, and the error varies between 2:2% and 70:6%. The CFD simulations result in acceptable resistance predictions with good precision for the speeds 4 - 6 knots, with an average deviation of the absolute values as12:28% which is slightly higher than the errors found in previous studies. However, the method shows inconsistency for the higher speeds where the deviation varies between 1:77% and - 43:39%. The Savitsky method predicts accurate results with good precision for planing speeds, but also for the speeds 7 and 8 knots. The method is under-predicting the resistance for all speeds except for 7 knots, where the total resistance is 10:7% higher than for model tests. In the speed range 8 - 32 knots, the average error is an under-estimation of 17:58%. Furthermore, the trim angles predicted by the Savitsky method correspond well with the trim angles from the model test. In conclusion, the recommendation to X Shore is to apply the Savitsky method when its applicability criteria are fulfilled, and CFD for the lowest speeds, where the Savitsky method is not applicable.

Herein, lanthanide ion (Gd+3) doped Bismuth Molybdate (Bi2MoO6) integrated on the rGO sheets has been prepared as a novel photocatalyst (Gd@Bi2MoO6/rGO) for the photocatalytic treatment of toxic pollutants. Different physiochemical, optical, electrical, thermal, and electrochemical properties of Gd@Bi2MoO6/rGO, along with its counterparts (Bi2MoO6 and Gd@Bi2MoO6) were studied through XRD, SEM/TEM, FT-IR, UV/Vis, I-V, TGA, Mott-Schottky, and EIS measurements. Photocatalytic experiments revealed that Gd@Bi2MoO6/rGO exhibited significantly enhanced photocatalytic activity, achieving 96.2 % photo-degradation of Methylene Blue with 120 min of irradiation, which is 6.5 and 3.1 times higher compared to Bi2MoO6 (40.9 %) and Gd@Bi2MoO6 (64.8 %), respectively. Moreover, Gd@Bi2MoO6/rGO demonstrated a notable photocatalytic efficiency of 81.7 % towards Ciprofloxacin, significant as per the existing literature benchmark. The enhanced photocatalytic activity is ascribed to the in-built Gd+3 redox centers, high electrical conductivity (7.35 × 10−3 S/m), favorable flat band potential (-0.81 V), and low semiconductor impedance (Rct = 51.71 Ω and Rs = 0.90 Ω). Additionally, the electron-capturing ability of lanthanide dopant ions and S-C heterojunction of Gd@Bi2MoO6/rGO facilitates the separation of photo-generated e-/h+ pairs and favors high concentrations of ROS. The results obtained highlight the potential of Gd@Bi2MoO6/rGO for applications in photocatalysis and wastewater treatment.

Innovations in high-speed rail (HSR) have had substantial effects on different stakeholders within and outside the railway system. As part of the European Shift2Rail research programme, several innovative solutions are developed for, among others, improving the HSR infrastructure. The Joint Undertaking behind this research program has set objectives for these innovations in terms of punctuality, capacity, and life cycle costs. With a focus on infrastructure-related innovations for HSR, this paper aims at assessing their impacts in relation to these targets. We review the relevant research literature about the effects of HSR innovations and their assessment. The paper presents a hybrid assessment methodology combing different approaches to assess capacity, punctuality, and cost effects. This contributes to reducing the existing gap that is found in the research literature. Based on a reference scenario for HSR line and collected data from different stakeholders, the results indicate that infrastructure innovations in HSR, being developed within the European Shift2Rail research programme, can contribute to reaching the target set for punctuality. Further innovations in HSR infrastructure and/or other railway assets may be needed to reach additional targets and for more accurate improvement values giving more insights into their impacts.

The work in this thesis investigates bolted joints in fibre reinforced composites with particular focus on a novel insert concept. The concept is characterised by replacing all composite plies with stacked metal patches, locally around a bolt hole, so that they jointly form a solid metal reinforcement. An extensive experimental study is presented together with finite element analysis of the studied cases.

Reinforcing bolt holes with high-strength metals improves the bearing load capacity of the composite laminates. True enhancement of the joint performance however requires that the open-hole tensile strength is improved as well. The work started with tests of pin-loaded and open-hole tensile specimens with inserts, and significant improvement of the bearing load capacity was found. The initial tests enabled more informed design, and insert configurations having sufficient open-hole tensile strength could thereby be manufactured and tested. In parallel, composite-metal joints were numerically modelled to simulate and analyse the mechanical performance of the joints and gain a better understanding of the governing damage mechanisms.

The performance of the joints was eventually investigated by means of experiments on single-shear, single- and double-bolt specimens, with and without inserts. The allowable bolt distance and the influence from the bolt tightening torque were also examined.

The initial samples had inserts of stainless steel. Later, specimens with titanium alloy inserts were also included in the test series. Various insert configurations were designed to study the effects of different features in the composite-metal bond lines. The numerical simulations of the composite--metal interfaces were performed with two types of models, one joining the two materials directly to each other, without modelling any adhesive film in between, and the other including an elastic representation of the adhesive layer. The experimental results were then used to support verification of the results from the simulations.

The final assessment of the concept was performed on insert configurations designed either for pure tensile loading or for more general (bi-directional) loading conditions, and the bearing load capacity, open-hole tensile strength and the performance of bolted joints were compared for cases with different inserts. While higher bearing strength improvement was achieved when the holes were reinforced with inserts of stainless steel, reinforcement with inserts of titanium was even more successful since it improved virtually all studied aspects of the joints considerably.

A new insert concept that interlaces metal inserts into composite laminates has earlier been shown to improve the relatively poor bearing strength of holes in fibre reinforced polymer composites, and it is here further and more thoroughly investigated. The concept was invented to increase the efficiency of joints with mechanical fasteners in composite materials and this work presents experiments on double-bolt joints with inserts made of either stainless steel or a titanium (Ti) alloy. In particular the work compares different implementations of the insert concept by reinforcing one or two holes in double bolt joints, and the effect of using different metals in the inserts. Some complementary tests on pin-loaded specimens and open hole tensile specimens are also performed and compared, partly with results that were reported previously. Considerable improvements of the bearing load capacity are attained, i.e. 50%-60% for steel and 35%-45% for Ti, compared to references. The open-hole tensile strength is also improved considerably (almost 30%) when the holes are reinforced with Ti inserts. The fact that the inserts can improve not only the bearing strength but also the performance in open-hole tension implies that the Ti inserts bring nothing but positive effects to the strength of the joints. The test results from single-shear double-bolt specimens with inserts at one hole showed improved strengths of 30% and 20% for specimens with steel and Ti inserts, respectively. Finally, an impressive strength improvement of 40-45% is achieved for single-shear double-bolt specimens having both holes reinforced with inserts of either steel or Ti.

A new insert concept that interlaces metal inserts into composite laminates has earlier been shown to improve the relatively poor bearing strength of holes in fibre reinforced polymer composites, and it is here further and more thoroughly investigated. The concept was invented to increase the efficiency of joints with mechanical fasteners in composite materials and this work presents experiments on double bolt joints with inserts made of either stainless steel or a titanium (Ti) alloy. In particular the work compares different implementations of the insert concept by reinforcing one or two holes in double bolt joints, and the effect of using different metals in the inserts. Some complementary tests on pin-loaded specimens and open hole tensile specimens are also performed and compared, also with some results reported previously.                    Considerable improvements in the bearing load capacity, i.e. 50%-60% or 35%-45%, is attained. The open-hole tensile strength is also improved considerably (almost 30%)  when the holes are reinforced with Ti inserts. The fact that the inserts can improve not only the bearing strength but also the performance in open-hole tension implies that the Ti inserts bring nothing but positive effects to the strength of the joints. The test results from single-shear double-bolt specimens with inserts at one hole showed improved strengths of 30% and 20% for specimens with steel and Ti inserts, respectively. Finally, an impressive strength improvement of 40-45% is achieved for single-shear double-bolt specimens having both holes reinforced with inserts of either steel or Ti.

An insert concept for reinforcing bolt-holes with high strength metals was previously introduced by the authors, where inserts are anchored in composite laminates through interlacement of composite plies and thin metal patches. The resulting finger-joints must be strong enough to avoid composite-metal debonding happening before bearing failure at the bolt-hole. The strength of the composite-metal interfaces is thus crucial for successful implementation of the insert concept. The paper presents an experimental study investigating the strength of various interface geometries between a prepreg composite material and stainless steel or titanium alloy inserts. In addition to the experimental work, finite element simulations are performed to analyse the stresses at the interfaces. The results indicate that the stress concentrations at multi-material corner points govern the failure and that the strength can be enhanced by expedient design.

Metal inserts are occasionally used to improve bearing load capacity of bolted joints in laminated composite materials. This paper investigates a new reinforcement concept where inserts are built by locally replacing composite plies with metal patches of various diameters, surrounding the holes. The inserts are built during composite manufacturing by alternately placing the metal patches through the thickness of the laminate at locations where holes are to be drilled after consolidation. An extensive experimental study including pin‐ loaded, open–hole tensile, and single‐shear testing of bolted specimens is presented. Considerable improve- ment of the bearing strength – 50‐60% – is attained for pin‐loaded specimens with inserts, demonstrating the potential of the reinforcement concept. The open–hole tensile tests show that the by‐pass strength can be maintained or even improved with up to 20% if the inserts are properly designed. Finally, the results from the single‐shear tests of bolted joints show more than 25% improvement in strength for reinforced single‐ and double‐bolt specimens. It is possible that the inserts would maintain clamping pressure over time, which could then almost double the imrovement (47%) for bolted joints.

Fracture of martensitic AISI 52100 steel with 12% retained austenite was experimentally studied at temperatures below the tempering temperature by K-Ic tests and at extremely low loading rates. Depending on temperature, K-Ic and J

In principle, there is a municipal planning monopoly in Sweden, and the planning system is therefore basically designed for the municipalities. All municipalities must have a comprehensive plan that covers their entire area of responsibility. Although the plan is not binding, it must be kept up to date. The detailed development plan is a legally binding, executive planning instrument - a legal agreement between the municipality, the public and landowners - that makes it possible for the intentions of the comprehensive plan to be implemented. Special area regulations are also binding, and this form of planning is used within limited areas to guarantee compliance with certain comprehensive plan goals. A property regulation plan may be used to facilitate implementation of the detailed development plan. For the planning of matters that are of mutual interest to several municipalities, the national government may appoint a regional planning body with the task of monitoring regional questions and providing basic planning data for municipalities and Government authorities.

During its lifetime, a beverage package is subjected to a complex loading history where the loading, in general, is multiaxial. Hence, to optimize the performance of the package, knowledge of the material’s anisotropic multi-axial deformation and subsequent failure is necessary. This report presents a way to investigate the anisotropic loading and fracture of thin paper using bi-axial testing and to analyse the bi-axial test using the Finite Element Method.

Getting thin paper to fracture in the central region, where the deformation and loading are bi-axial, proved difficult. To overcome this challenge, a new bi-axial specimen, with a specific sample preparation technique, is proposed and implemented in this specific project. This cruciform-shaped thin paper specimen was reinforced with laminating plastic everywhere but in the central region. This was done to avoid material failure in the notched radius area or the clamps, rather to facilitate a material failure in the central region of the material specimen. In order to simulate the bi-axial test, a Hill elastic-plastic material model was calibrated, and the material parameters were obtained from uniaxial tensile tests.

When subjected to bi-axial loading, the proposed cruciform-shaped specimen fractured systematically and repetitively for the different load cases studied. Moreover, the Hill material model captured the force vs. displacement curve from the experimental results well but overestimated its values. The overestimation was mainly due to sliding during the experimental tests.

The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithium-ion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles. 

Tissue failure and damage are inherent parts of vascular diseases and tightly linked to clinical events. Additionally, experimental set-ups designed to study classical engineering materials are suboptimal in the exploration of vessel wall fracture properties. The classical Compact Tension (CT) test was augmented to enable stable fracture propagation, resulting in the symmetry-constraint Compact Tension (symconCT) test, a suitable set-up for fracture testing of vascular tissue. The test was combined with Digital Image Correlation (DIC) to study tissue fracture in 45 porcine aorta specimens. Test specimens were loaded in axial and circumferential directions in a physiological solution at 37 & DEG;C. Loading the aortic vessel wall in the axial direction resulted in mode I tissue failure and a fracture path aligned with the circumferential vessel direction. Circumferential loading resulted in mode I-dominated failure with multiple deflections of the fracture path. The aorta ruptured at a principal Green-Lagrange strain of approximately 0.7, and strain rate peaks that develop ahead of the crack tip reached nearly 400 times the strain rate on average over the test specimen. It required approximately 70% more external work to fracture the aorta by circumferential than axial load; normalised with the fracture surface, similar energy levels are, however, observed. The symconCT test resulted in a stable fracture propagation, which, combined with DIC, provided a set-up for the in-depth analysis of vascular tissue failure. The high strain rates ahead of the crack tip indicate the significance of rate effects in the constitutive description of vascular tissue fracture.

The mechanics of vascular tissue, particularly its fracture properties, are crucial in the onset and progression of vascular diseases. Vascular tissue properties are complex, and the identification of fracture mechanical properties relies on robust and efficient numerical tools. In this study, we propose a parameter identification pipeline to extract tissue properties from force-displacement and digital image correlation (DIC) data. The data has been acquired by symconCT testing porcine aorta wall specimens. Vascular tissue is modelled as a non-linear viscoelastic isotropic solid, and an isotropic cohesive zone model describes tissue fracture. The model closely replicated the experimental observations and identified the fracture energies of 1.57±0.82 kJ m−2 and 0.96±0.34 kJ m−2 for rupturing the porcine aortic media along the circumferential and axial directions, respectively. The identified strength was always below 350 kPa, a value significantly lower than identified through classical protocols, such as simple tension, and sheds new light on the resilience of the aorta. Further refinements to the model, such as considering rate effects in the fracture process zone and tissue anisotropy, could have improved the simulation results. Statement of significance: This paper identified porcine aorta's biomechanical properties using data acquired through a previously developed experimental protocol, the symmetry-constraint compact tension test. An implicit finite element method model mimicked the test, and a two-step approach identified the material's elastic and fracture properties directly from force-displacement curves and digital image correlation-based strain measurements. Our findings show a lower strength of the abdominal aorta as compared to the literature, which may have significant implications for the clinical evaluation of the risk of aortic rupture.

Self-driving technology may lead to a paradigm shift for the transport industry with shared cars available to every-one. However, this vision has increasingly been challenged as too optimistic and unsubstantiated. In this study we explore societal impacts of using this technology for both cars and public transport and investigate differences depending on geography and trip purpose. Four scenarios were designed through workshops with 130 transport experts, modelled using a conventional four-step model for Stockholm, Sweden and evaluated in terms of changes to mode choice, number of trips and person kilometres.We find larger increases for non-commuting trips, i.e. service and leisure trips, than for commuting trips, questioning the view of the 'productive work trip' as self-driving technology's main impact on society. As these trips are primarily made outside of rush hours, this may lead to a changed transport system. Geographic differences are substantial and heavily dependent on the cost model for car alternatives, even indicating a reduction in car travel in rural areas if private ownership would be replaced by shared cars. Furthermore, walking and cycling levels decreased in all scenarios while enhancing public transport using self-driving technology had a limited impact on ridership.These results show that the impacts of self-driving technology may have varied societal impacts even within a region and may lead to increased car travel, especially off-peak. These conclusions stress the need for policies that are sensitive to both geography and time.

Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors. 

This thesis focuses on the optimization of the electric aircraft propeller in order to increaseflight performance. Electric aircraft have limited energy, particularly the electricmotor torque compared to the fuel engine torque. For that, redesign of the propeller forelectric aircraft is important in order to improve the propeller efficiency. The airplanepropeller theory for Glauert is selected as a design method and incorporated with Brattimprovements of the theory. Glauert theory is a combination of the axial momentum andblade element theory. Pipistrel Alpha Electro airplane specifications have been chosen asa model for the design method. Utilization of variable pitch propeller and the influence ofnumber of blades has been investigated. The obtained design results show that the variablepitch propellers at cruise speed and altitude 3000 m reducing the power consumptionby 0.14 kWh and increase the propeller efficiency by 0.4% compared to the fixed pitchpropeller. Variable pitch propeller improvement was pretty good for electric aircraft. Theoptimum blade number for the design specifications is 3 blades.

This thesis focuses on understanding the mechanical behavior of fiber-based materials by utilizing various modeling approaches. Particular emphasis is placed on their structural variability, anisotropic properties, and damage behavior. Furthermore, the study explores moisture diffusion phenomena within these materials, leveraging machine learning techniques. The research employs a blend of multiscale modeling, experimental investigation, machine learning, and continuum modeling to enhance the predictive capabilities for modelling fiber-based materials.

In Paper I, the work investigates the impact of stochastic variations in the structural properties of thin fiber networks on their mechanical performance. A multiscale approach that includes modeling, numerical simulation, and experimental measurements is proposed to assess this relationship. The research also considers the influence of drying conditions during production on fiber properties. The study finds that spatial variability in density has a significant impact on local strain fields, while fiber orientation angle with respect to drying restraints is a key influencer of the mechanical response. In Paper II, the research delves into the investigation of anisotropic properties and pressure sensitivity of fiber network materials. It draws a comparison between the Hoffman yield criterion and the Xia model, which are widely utilized for simulating the mechanical response in fiber-based materials. The study performs a detailed analysis of these models under bi-axial loading conditions, assessing their numerical stability and calibration flexibility. Further supporting the research community, the paper provides open-source access to the user material implementations of both models and introduces a calibration tool specifically for the Xia model, thereby promoting ease of usage and facilitating further research in this domain. In Paper III a novel thermodynamically consistent continuum damage model for fiber-based materials is introduced. Through the integration of elastoplasticity and damage mechanisms, the model employs non-quadratic surfaces comprised of multi sub-surfaces, augmented with an enhanced gradient damage approach. The model’s capability is demonstrated by predicting the nonlinear mechanical behavior under in-plane loading. This study provides valuable insights into the damage behavior of fiber-based materials, showcasing a range of failure modes from brittle-like to ductile. In Paper IV, the study examines moisture penetration in fiber-based materials and the resultant out-of-plane deformation, known as curl deformation, using a combination of traditional experiments, machine learning techniques, and continuum modeling. The paper compares the effectiveness of two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), in predicting the gradient of the moisture profile history. The study finds that the RNN model, which accounts for temporal dependencies, provides superior accuracy. The predicted gradient moisture profile enables simulating the curl response, offering a deeper understanding of the relationship between moisture penetration and paper curling.

The stochastic variations in the structural properties of thin fiber networks govern to a great extent their mechanical performance. To assess the influence of local structural variability on the local strain and mechanical response of the network, we propose a multiscale approach combining modeling, numerical simulation and experimental measurements. Based on micro-mechanical fiber network simulations, a continuum model describing the response at the mesoscale level is first developed. Experimentally measured spatial fields of thickness, density, fiber orientation and anisotropy are thereafter used as input to a macroscale finite-element model. The latter is used to simulate the impact of spatial variability of each of the studied structural properties. In addition, this work brings novelty by including the influence of the drying condition during the production process on the fiber properties. The proposed approach is experimentally validated by comparison to measured strain fields and uniaxial responses. The results suggest that the spatial variability in density presents the highest impact on the local strain field followed by thickness and fiber orientation. Meanwhile, for the mechanical response, the fiber orientation angle with respect to the drying restraints is the key influencer and its contribution to the anisotropy of the mechanical properties is greater than the contribution of the fiber anisotropy developed during the fiber sheet-making.

In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.

The anisotropic properties and pressure sensitivity are intrinsic features of the constitutive response of fiber network materials. Although advanced models have been developed to simulate the complex response of fibrous materials, the lack of comparative studies may lead to a dubiety regarding the selection of a suitable method. In this study, the pressure-sensitive Hoffman yield criterion and the Xia model are implemented for the plane stress case to simulate the mechanical response under a bi-axial loading state. The performance of both models is experimentally assessed by comparison to bi-axial tests on cruciform-shaped specimens loaded in different directions with respect to the material principal directions. The comparison with the experimentally measured forces shows the ability of the Hoffman model as well as the Xia model with shape parameter k≤2 to adequately predict the material response. However, this study demonstrates that the Xia model consistently presents a stiffer bi-axial response when k≥3 compared to the Hoffman model. This result highlights the importance of calibrating the shape parameter k for the Xia model using a bi-axial test, which can be a cumbersome task. Also, for the same tension-compression response, the Hill criterion as a special case of the Hoffman model presents a good ability to simulate the mechanical response of the material for bi-axial conditions. Furthermore, in terms of stability criteria, the Xia model is unconditionally convex while the convexity of the Hoffman model is a function of the orthotropic plastic matrix. This study not only assesses the prediction capabilities of the two models, but also gives an insight into the selection of an appropriate constitutive model for material characterization and simulation of fibrous materials. The UMAT implementations of both models which are not available in commercial software and the calibration tool of the Xia model are shared with open-source along with this work. 

This study presents a thermodynamically consistent continuum damage model for fiber-based materials that combines elastoplasticity and damage mechanisms to simulate the nonlinear mechanical behavior under in-plane loading. The anisotropic plastic response is characterized by a non-quadratic yield surface composed of six sub-surfaces, providing flexibility in defining plastic properties and accuracy in reproducing material response. The damage response is modeled based on detailed uniaxial monotonic and cyclic tension-loaded experiments conducted on specimens extracted from a paper sheet in various directions. To account for anisotropic damage, we propose a criterion consisting of three sub-surfaces representing tension damage in the in-plane material principal directions and shear direction, where the damage onset is determined through cyclic loading tests. The damage evolution employs a normalized fracture energy concept based on experimental observation, which accommodates an arbitrary uniaxial loading direction. To obtain a mesh-independent numerical solution, the model is regularized using the implicit gradient enhancement by utilizing the linear heat equation solver available in commercial finite-element software. The study provides insights into the damage behavior of fiber-based materials, which can exhibit a range of failure modes from brittle-like to ductile, and establishes relationships between different length measurements.

Additive manufacturing (AM), also known as 3D printing, has emerged as a rapidly growing manufacturing technique with numerous advantages over traditional methods. This thesis project investigates the application of AM in suspension design. The aim is to explore the advantages of AM, suitable product selection, and the potential for gaining a competitive edge by leveraging AM effectively.

Through this research, a printable part specifically designed for AM will be developed. The project's results demonstrate the advantages of AM when the technique is harnessed effectively. Merely switching manufacturing techniques without considering AM's value-added aspects is unlikely to yield the desired benefits. However, designing components with AM in mind from the initial stages can unlock numerous advantages.

The findings of this thesis project contribute to understanding how AM can be leveraged to optimize mountain bike suspensions. By evaluating the advantages and disadvantages of the designed parts, valuable insights are provided for Öhlins and the wider biking industry. This knowledge enables informed decision-making for strategic integration of AM in future product development and manufacturing processes.

This research underscores the significance of thoughtful design considerations and effective integration of AM to harness its full potential in enhancing the performance, cost-efficiency, and functionality of mountain bike suspension.

With the advent of hybrids and electric vehicles, the need for lightweight and highperformancematerials is growing. Sheet molding compound (SMC) is a compositemade of short and randomized  bers that o ers a substantial weight reduction andgood mechanical properties while meeting the demand for large volume production.This thesis aims to develop a constitutive FE model of the SMC used in the bodyin black of an autonomous vehicle.To extract its properties, several physical tests were performed on specimens madeof the above-mentioned material. Both the tensile and three point bending testsresults show that the material is not homogeneous and that its properties vary fordi erent directions. The damping ratio extracted from the vibration test is muchlower than in conventional structural materials like aluminum and steel.In the FE analysis, the material was modeled both as isotropic and orthotropic.After adjusting the Young's modulus, the isotropic model shows accurate resultsuntil 1200 Hz. On the other hand, without knowing in which directions the propertiesoccur, the orthotropic model is very limited.In conclusion, even though the properties were tailored speci cally for the specimen,the model might not correctly represent the material's behavior, being itsproperties not the same for di erent components. Therefore, it is more reasonableto use average data instead.

An electric circuit replacement model is proposed to simulate the key nonlinear dynamics of electro-responsive liquid crystal polymer networks (LCNs). LCNs are known for having great potential to be integrated into smart functional surfaces due to their ability to generate various surface patterns. However, due to their complex molecular dynamics, low-order dynamic models that can accurately describe and predict their dynamic behavior are still lacking. In light of this research gap, we develop a lumped-parameter replacement model based on the observed dynamics in the experimental data and the physics of LCN dielectric properties. The unique assembly of lumped parameters in its simplest form describes the transformation of a high-frequency input voltage to a relatively slow increase in the local height of the LCN coating in between the electrodes, serving as an excitation mechanism to induce height change. The nonlinear dynamics of this height increase, as a function of both excitation frequency and voltage, is described by the proposed model. Furthermore, the comparison of the simulation results with the experimental data from LCN shows that key LCN response characteristics are captured well by the model. This model makes it possible to accurately predict and control the response of the electro-responsive LCN surfaces to obtain a predefined desired deformation pattern, which is a vital requirement for integrating them in haptic and smart surface devices.

The electrification era has been marked up by an increase in volume of electric vehicles which are directly or indirectly powered by electricity. Railways, roadways and airways are being electrified as we speak at their own respective rate. In addition to that upcoming concepts for transport solution such as hyperloop also described as the fifth mode of transportation will be electrified. The current thesis work is based on developing the model and control of the propulsion system of a scaled Hyperloop pod designed by student team KTH Hyperloop representing KTH. The team competes in Hyperloop competition organized by Spacex and the goal is to achieve the highest possible speed in a given distance and track designed by SpaceX. In order to achieve the goal of being the fastest, the scaled pod uses a Double Sided Linear Induction Motor (DSLIM) as mentioned in the subsequent chapter. The motor modelling is done on Simulink and is similar to a rotary induction motor (RIM). However the presence of end effect in DSLIM makes it different from RIM and has been discussed subsequently. The control strategy uses a synchronous frame PI control for the current control and sensor based speed control for controlling the speed of the pod.The speed control output is a reference current which is used as an input to the current controller which finally gives voltage as the control output. The corresponding bandwidth for the various loops have been calculated based on motor parameters as discussed in the method section. The validation of the motor model and the corresponding controller has been discussed in the result section, where the accuracy of the controller for the designed modelled is discussed.

Stora investeringar och omdaningar planeras i vårt transportsystem. Transporterna väntas öka starkt i framtiden och mera kapacitet måste skapas på ett hållbart sätt. Diskussionerna om vilka transportmedel som ska prioriteras, såväl som vilka objekt som vi ska satsa på, är livliga.

En viktig fråga är satsningen på Nya Stambanor avsedda för snabba persontransporter i de redan idag hårt belastade stråken Stockholm‒Göteborg och Stockholm‒Malmö, med ett stort antal mellanliggande orter. Denna typ av järnvägar finns redan eller planeras i de flesta av världens ledande ekonomier. Syftet med att bygga nya stambanor är att öka den totala kapaciteten för person- och godstrafik på järnväg, öka punktligheten och öka tillgängligheten genom korta restider. Det ger också förutsättningar för större regionala arbetsmarknader och ökat bostadsbyggande utanför storstäderna samt en bättre miljö. Nuvarande stambanor avlastas och lämnar plats för bl a effektivare godstransporter.

Denna rapport behandlar först järnvägens egenskaper. Järnvägen är det energieffektivaste transportmedel vi känner till, den tar liten plats och är mycket trafiksäker. Moderna tåg på modern bana är vårt snabbaste transportmedel till lands. Tåg kan bereda plats och komfort för arbete och avkoppling under resan. Enligt författarnas uppfattning bör dessa egenskaper göra järnvägen till ett förstahandsalternativ för effektiva och hållbara transporter i de segment där järnvägen är eller kan bli konkurrenskraftig.

Prognoser och analys, samt erfaren­heter från utlandet, visar att trafikunderlaget i Sverige är tillräckligt för nya stambanor. Med de förslagna banorna väntas järnvägens totala kapacitet öka till mer än det dubbla i de mest belastade stråken. En viktig faktor är att den snabba och långsamma tågtrafiken separeras. Denna åtgärd ger ökad kapacitet, utöver vad de dubblerade spåren ger, eftersom tågen kan köra tätare efter varandra och störningarna i tågtrafiken minskar.

Restiderna för orterna längs de nya stambanorna minskar kraftigt, i regel mellan 30 och 65%. Tillsammans med ökad turtäthet och minskade störningar ger det stora ökningar av tågtrafiken. De officiella prognoserna lider dock av ett antal allvarliga brister, varför både trafikökningen och den samhällsekonomiska lönsamheten beräkningsmässigt framstår som mindre än vad den enligt KTH:s prognoser och internationell erfarenhet borde vara.

Författarna anser att anläggningskostnaderna är rimliga i relation till nyttorna och jämfört med vad andra omställningar i samhällets transportsystem kostar. Detsamma gäller den engångs ”klimatskuld” som uppkommer vid de flesta satsningar för framtiden inom alla trafikslag. Nya transportslag i ett tidigt utvecklingsskede (elflyg, magnettåg, Hyperloop etc) är mycket osäkra beträffande när eller om de överhuvudtaget kommer att bli tillgängliga för användning i stor skala. I flera fall skulle krävas stora tekniska genombrott som vi idag inte känner till. Vi anser att man rimligen inte idag kan besluta att satsa på helt nya tekniska system för vilka framtiden är mycket osäker. Vi kan inte heller ”vänta och se”, eftersom ytterligare kapacitet behövs redan idag och ledtiderna är långa.

Sammanfattningsvis är de nya stambanorna ett samhällsbyggnadsprojekt och en del i transportsektorns nödvändiga omställning. De ger korta restider och effektiva transporter mellan våra största städer, liksom till och från ett stort antal mellanliggande orter, med omnejd. Godstransporterna kan också få plats på spåren och de kan utvecklas och effektiviseras. Det handlar om hållbar mobilitet för människor och gods i framtiden.

The increasing reliance on space infrastructure and its rapid expansion necessitate the development and enhancement of tools for space debris and fragmentation research. Accurate prediction of the risks associated with satellite fragmentation requires comprehensive understanding of the dynamics involved. To address this need, the widely used NASA Standard Breakup Model (SBM) is employed in this thesis to predict fragment characteristics resulting from breakup events. Additionally, a novel method is introduced to determine the direction of these fragments, something not directly covered by the SBM. Furthermore, the principle of kinetic gas theory is applied to calculate the overall, long-term collision risk between debris and a predetermined satellite population. The results from this reveal the limitations of the SBM in accurately simulating fragmentations for certain satellite types. However, the newly implemented fragment directionality method aligns well with observed data, suggesting its potential for further research. Similarly, the risk model exhibits strong correspondence with ESA's MASTER, a model used for assessing collision risks with debris, with the deviations likely due to different impact velocity models used. Finally, the validated fragmentation and risk models are combined, and the combined model is used to analyse a real-world fragmentation event.

Admission of applicants to higher education in a fair, reliable, transparent, and efficient way is a real challenge, especially if there are more eligible applicants than available places and if there are applicants from many different educational systems. Previous research on best practices for admission to master’s programmes identified the key question about an applicant’s potential for success in studies, but was not able to provide an answer about how to rate the merits of the applicants. In this study, indicators for study success are analysed by comparing the study performance of 228 students in master’s programmes with their merits at the time of admission. The null hypothesis was that the applicant’s average grade at the time of admission is the only indictor for study success. After testing for potential bias using almost 20 possible other indicators, the null hypothesis had to be rejected for four indicators (in order of importance): (i) university ranking, (ii) length of bachelor’s studies within subject, (iii) English language test and (iv) subject matching between bachelor’s and master’s education. Evaluation of quality of prior education is tricky and results from this study clearly indicate that students from higher ranked universities possess better knowledge and stronger skills for our master’s programmes. Work is ongoing to improve the merit rating model by involving more master’s programmes at KTH and analysing performance data from a larger number of students.

The rapid growth of plastic waste from food packaging around the world demands renewable substitutes, such as natural fibers and biocomposites. Wood fibers are natural fibers extracted from trees and are commonly used in packaging. In order for renewable alternatives to compete against plastics and other non-renewable materials, a better understanding of the mechanical properties of single fibers at the micro-scale are necessary. A great deal of previous research into the mechanical properties of single wood fibers has focused on their tensile behavior, however, little work has been published about their compressive behavior. It is difficult to measure the compressive strength of single fibers directly due to fiber buckling.

The purpose of this study is to investigate how post-buckling of single wood pulp fibers affects the mechanical properties of fibers in tension. Two alternative hypotheses were tested through experiments in The Odqvist Laboratory for Experimental Mechanics at KTH. The major part of the thesis process has been invested in developing components called grippers, and testing methods for the Single Fiber Testing System, in order to be able to perform the experiments. The existing grippers were tested and alternative grippers were developed, as well as an alternative testing method without grippers, called the Paper frame method (PFM). PFM was used in the final experimental work to test the hypotheses.

The main finding from this study is that there is not enough evidence to suggest that the tensile strength or tensile stiffness of single wood fibers are significantly reduced by post-buckling damage. This finding is mostly relevant in the research and development of fibrous material with larger distances between individual fibers, such as low-density fiber network materials. The main findings from the single fiber testing methods development were that the existing grippers cannot prevent fiber slippage. Furthermore, the alternative gripper 22A with its arc design generates higher grip force than previous grippers but lacks surface friction in the contact region in order to prevent fiber slippage. PFM has an experimental success rate of over 80 % for trained users and easy usage for the operator. The testing equipment Single Fiber Testing System displays several systematic errors occurring in the post-processing process of tests with cyclic loads.

Driverless multipurpose vehicles (DMVs) are an emerging vehicle concept for urban heavy-duty transport. However, little is known about their effect on urban road transport systems. Thus, the aim of this study is to analyse the total fleet energy consumption of DMVs for specific transport operations in urban logistics compared to heavy- duty battery and combustion vehicles. A novel electric vehicle routing problem was used to simulate in total 96 case-studies of operations with varying network and vehicle fleet properties. We found that the combustion vehicle fleets consumed significantly more energy for the same operation compared to the electric vehicle fleets. Although the DMV fleet and battery electric vehicle fleet showcased similar energy consumption for most case-studies, there were several operations where the DMV fleet consumed less energy and required a smaller fleet size. This study highlights the potential benefits of DMV fleets in urban logistics operations in terms of reducing total fleet energy consumption and fleet size.

Real vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

Noise generation from underwater activities propagates into the marine environment.For marine vessels the propulsion system generates the most noise during itsoperations. Naval vessels that want to operate without being detected want to controlthe sound generating properties of the vessel. To control the sound generatingproperties this project has been looking into the existing propeller of the submergedcraft Carrier Seal that is produced by James Fisher Defense. Then a new and bespokenpropeller has been developed with theories applied to minimize its noise generatingproperties. The properties of the propeller that have been altered is the number ofblades, blade area ratio, pitch and skew angle. These properties have been alteredwith aid of the open-source software for Matlab named Openprop. From the finalpropeller design a prototype was later produced, tested and compared to the existingpropeller of the Seal Carrier. To test and compare these two propellers a test procedurewith inspiration from NATO and the Swedish Defense and Research Agency (FOI) wasdeveloped. The results from the comparison show that the sound pressure level fromthe propeller spectrum could be lowered with 3 dB re 1 μP a for the vessels design speedand several blade tones could be eliminated entirely. Simultaneously the efficiency ofthe vessel is increased throughout its speed range.In conclusions the recommendation to JFD is to change their existing propeller tothis bespoken propeller as it has proven itself to better in every way during thesetrials.

Space surveillance and tracking is paramount to enable collision avoidance alert systems and avoid fragmentation events and the consequent generation of debris. This thesis work was performed within the framework of the EU SST, a network of sensors and processing capabilities of the European Union dedicated to space tracking. Using tools developed at CNES, a performance analysis of a correlation assessment algorithm based on the Lambert’s problem was carried out. The algorithm was tested on a simulated population orbiting the GEO region using different dynamical models. The performance of the algorithm was also tested for varying values of the sensor noise. A modification to the algorithm accounting for orbit perturbations was proposed and tested, showing improved results under certain conditions. Finally, the catalogue initialization capabilities of the algorithm were assessed in terms of correlation assessment and orbit determination accuracy by testing it on real data collected by the TAROT network of telescopes.

Remote operation of highly automated vehicles (HAVs) may include occasional assistance from a human remote operator that is located outside the HAVs. Remote assistance typically delegates only high-level guidance tasks to the remote operators such as authorizing a driving maneuver or specifying a new driving path. As remote assistance is fairly unexplored, there are still several research challenges. These challenges were discussed by experts from academia and industry in a multidisciplinary workshop at the 2023 IEEE Intelligent Vehicles Symposium. As a result of the workshop, this paper presents a list of most pressing research questions in the following areas: human-machine interaction and human factors, design of the remote station, design of the HAVs. It also outlines a roadmap for future research on remote assistance of HAV, thereby informing interdisciplinary studies and facilitating the benefits of HAVs before full autonomy can be reached.

Welds play a crucial role in the product portfolio of GKN Aerospace. For ease of manufacturing and repairability, butt welds are preferred in the aerospace industry. Weld regions undergo local distortions during manufacturing due to the external heat-input, and the resulting local geometries are complex and stochastic in nature. Under operational loads, the distorted geometry affects the local stress field around the weld region, and this has a significant impact on the fatigue life.

Traditional design calculations of welds resort to idealization of the local weld geometry. In this thesis, the influence of real weld geometry on the computed fatigue life is investigated. Linear elastic fracture mechanics principles are utilized to calculate the fatigue life of a weld starting from a pre-defined initial crack. The influence of important weld geometric parameters, namely, – edge offset and weld toe radii, on the fatigue life is investigated in detail. A statistical analysis approach, using transfer functions and Monte Carlo simulation, is devised to study the effect of variation in the different weld geometric parameters. Different edge offset measures from a real geometry are identified and these measures are investigated as potential candidates to obtain conservative life estimates using the idealized geometry.

Investigations in this thesis show that the real geometry has a significant effect on the weld fatigue life. In all cases of local weld geometry, the edge offset has the largest influence on life. For a real weld geometry, the root toe radius has a significant influence on life. In an idealized geometry, for the same normalized edge offset, an increase in the plate thickness leads to a decrease in life. A new edge offset measure is proposed that leads to conservative life estimates when used in conjunction with the idealized geometry, thus enabling computationally efficient design calculations.

Due to the increase in demand on freight transportation it becomes necessary to avoid delays to ensure that the goods reach its destination on time. The main factors causing disturbances in the traffic on the mainline is the breakdown of vehicles mainly due to damaged wheels. The damaged wheels are identified through the wheel-rail impact force measurements provided by the wheel impact load detectors (WILD). This calls for the optimal schedule of maintenance of wheelsets and wagons in general. During the maintenance, the officials manually check for defective wheels and the exchange of wheelsets is performed based on the type of damage. The classification of wheel damages plays a vital role in providing ease of damage identification and insights to deduce a strategy for wheelset exchange. In this study, an attempt to classify the damaged wheelsets is done by analysing the wheel-rail impact force data from the wayside detectors. The data from the detectors are acquired from PredgeAB, a Luleå based startup pioneering in providing decision support on optimal maintenance schedules and predictive maintenance of rail wheels. Through their detection and prediction solutions it was observed that of all the wheels marked as damaged by the detectors 10% were undamaged. The source of the deviation in the impact force readings could help Predge make better estimations in damage detection and prediction. In this study, the sources contributing to the deviation is studied using multi-body simulations in GENSYS. A new method for modelling wheel damage is developed to overcome the shortcomings of the software. The findings of this study can then be used appropriately to make classifications of wheel damages.

This paper presents the work done during a Master Thesis at Airbus Helicopters company in theaccident investigation team. The focus of that internship was the hydraulic systems on AS350 helicoptersthat assist flight controls and may have an impact on accidents. It led to a "lessons learned" analysis basedon accident rates and protective/ corrective measures taken by Airbus Helicopters after hydraulics relatedaccidents. A detailed protocol for hydraulic circuit expertise has been written and a tool-case for functionalpost-crash hydraulic tests has been developed. This paper will present how helicopters fly, are controlledand how hydraulics are involved in flight controls. The above cited missions will then be explained.

The aim of this thesis work is to investigate the possibility of applying a sensor fusion algorithm with a focus on estimating vehicle dynamic states, mainly the vehicle body accelerations. Modern passenger vehicles have several mechatronic systems such as active safety, comfort, driver assistance etc., which are highly dependant on accurate knowledge of such states. This work focuses on the mechatronic suspension system, which makes use of the body accelerations measurements to control the dynamics of the vehicle body in order to provide an improved driving experience. This work can be split up into two major parts, the first being the identification of available onboard sensors for measuring the vehicle body accelerations. Five different sensor combinations are considered and compared with each other. The next part is to develop a sensor fusion algorithm, in this case, a Kalman Filter (KF) based algorithm, which uses vehicle dynamic modelling knowledge to obtain accurate, reliable and less uncertain estimates of the states. Specifically, an Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) were built and compared with each other. Two different vehicle dynamic models, a vehicle planar dynamic model and a full car suspension model, were implemented to capture both the effects of road disturbances and drivingmanoeuvres on the vehicle body dynamics. Both these fusion algorithms were tested using simulation data and logged data and validated by comparing with an ideal sensing method to measure the body accelerations used currently at Volvo Car Corporation.

Highly automated driving is approaching reality at a high speed. BMW is planningto put its first autonomous driving vehicle on the road already by 2021. The path torealising this new technology is however, full of challenges. Not only the transverseand longitudinal dynamic vehicle motion play an important role in experiencedcomfort but also the requirements and expectations of the occupants regarding thevertical dynamic vibration behaviour. Especially during long trips on the motorwaywhere the so far active driver becomes the chauffeured passenger, who reads, worksor sleeps in his newly gained time. These new use-cases create new requirements forthe future design of driving comfort which are yet to be fully discovered.This work was carried out at the BMW headquarters and had the aim to usedifferent machine learning models to investigate and identify patterns between thesubjective comfort values reported by participants in a study, on a comfort scale of 1-7 and the mechanical vibrations that they experienced, measured inm/s2. The datawas collected in a previous independent study and statistical methods were used toinsure the quality of the data. A comparison of the ISO 2631-1 comfort ratings andthe study’s findings is done to understand the need for a more sophisticated model to predict comfort in autonomous driving. The work continued by investigating different dimensionality reduction methods and their influence on the performance of the models. The process used to build, optimise and validate neural networks and other models is included in the method chapter and the results are presented. The work ends with a discussion of both the prediction results and the modelsre-usability. The machine learning models investigated in this thesis have shown great po-tential for detecting complex pattern that link feelings and thoughts to mechanical variables. The models were able to predict the correct level of comfort with up to50% precision when trying to predict 6 or 7 levels of comfort. When divided into high versus low discomfort, i.e. predicting one of two comfort levels, the models were able to achieve a precision of up to 75.4%.Excluded from this thesis is the study of differences in attentive vs inattentive state when being driven in an autonomous driving vehicle. It became clear shortly before the start of this work, that the experiment that yielded the data used for it failed to find a statistically significant difference between the two states.

Power companies in the geothermal industry face well-known challenges when exploiting high temperature geothermal reservoirs. To extract the geothermal energy from the reservoirs, geothermal wells are drilled to retrieve the geothermal steam to produce both electricity and hot water for district heating. The geothermal steam can be both highly corrosive and cause high thermal expansions in the steel casings which can result in high repair costs. The Icelandic R&D company Gerosion is developing a protective casing for high temperature wells, called the Sacrificial Casing. The product is a multi-layered design and can protect the other casing strings from corrosion and high thermal expansions. This thesis project aims to design and optimize a new fastening mechanism for the Sacrificial Casing, a so called slip mechanism. The material selection and geometrical dimensions of the mechanism are explored based on computational results. The most feasible design for installation of the mechanism is then used to construct both 2D and 3D FE-models where the mechanism is subjected to high temperatures and pressure and the load-carrying capabilities of the design analyzed. The results indicate that thermal expansions cause extremely high stresses in the mechanism and as a result the S960 high strength steel grade does not offer sufficient strength to withstand the combined loading the mechanism is subjected to.

Engineering materials that can store electrical energy in structural load paths can revolutionize lightweight design across transport modes. Stiff and strong batteries that use solid-state electrolytes and resilient electrodes and separators are generally lacking. Herein, a structural battery composite with unprecedented multifunctional performance is demonstrated, featuring an energy density of 24 Wh kg−1 and an elastic modulus of 25 GPa and tensile strength exceeding 300 MPa. The structural battery is made from multifunctional constituents, where reinforcing carbon fibers (CFs) act as electrode and current collector. A structural electrolyte is used for load transfer and ion transport and a glass fiber fabric separates the CF electrode from an aluminum foil-supported lithium–iron–phosphate positive electrode. Equipped with these materials, lighter electrical cars, aircraft, and consumer goods can be pursued.