A unique series of flyover tests was conducted during the pandemic to study how variations commonly found in standard approach procedures primarily affect the noise level on the ground. The pandemic created unique conditions as the reduced air traffic provided the opportunity to perform these tests and collect noise measurements that are almost clear from background noise. The tests were performed with two A321neo aircraft one morning in April 2021 at Arlanda Airport, Stockholm. This work takes advantage of this database to develop an understanding of the influence of variations in configuration, speed, and altitude on the environmental impact. Interdependencies between noise, CO2, and non-CO2 emissions are closely studied, and the significant findings are presented and discussed. It is shown that, for early configurations, before landing gear deployment, there is a substantial tradeoff between CO2 and non-CO2 emissions, and careful consideration of the latter should be taken to avoid areas of critically low idle power. Noise in these configurations is mainly affected by the impact of the aircraft-microphone distance on the measurements. For the configurations that follow, CO2 emissions become more relevant as the fuel flow increases due to the increase in drag, which also results in an increased noise level that, in these configurations, shows a significant dependency on aircraft speed with an increase of 1 dB for every 10 knots. Based on these findings, some actions are proposed to minimize the impact of each configuration.

This article analyzes aircraft noise measurements from the Airbus A321neo at 7.5 and 5 nautical miles from the runway threshold. Using correlation, analysis of variance, and hierarchical regression analysis, we assessed the influence of flight data recorder variables and meteorological parameters on the measured sound level variations. A combination of aircraft speed and configuration of the high lift devices can predict approximately 60% of the sound level variations. Sound level dependence on speed ranges between 0.5 and 1.5 dB/10 kn for different configurations and landing gear deployment had a +3 dB impact on sound levels. At the same time, weather and wind conditions accounted for a relatively small proportion of the variation. Overall, this study sheds light on the factors contributing to aircraft noise during the final approach and provides insights into potential noise reduction strategies.

This article presents the results from a series of aircraft approach trials that were conducted with the aim to investigate noise reduction procedures within the boundaries of a normal ILS approach. The significant decline in air traffic at Stockholm Arlanda, that occurred during the pandemic meant that the empty airspace and the availability of grounded aircrafts could be utilized to perform controlled flights - something that would have been difficult to achieve during normal traffic conditions. The approach trials were performed by two Airbus A321, which alternately carried out interrupted landing procedures starting 17 nautical miles (nm) from the runway threshold. During the trials, the aircraft speed and configuration (high lift devises and landing gear) were varied according to a predetermined schedule. To capture these variations, flight data (FDR) were recorded while the noise on ground was measured at positions approximately once every nautical mile along the flight track. Results suggest that speed and configuration recommendations can be effective to reduce noise, especially for the final 7 nautical miles of the flight track. However, whether a low speed is to be advocated during the entire approach is currently unclear.

As industries hold the opportunity to embrace artificial intelligence (AI) driven innovation, their success to a significant extent will depend on the value the new technology generates for different business stakeholder groups. This is in turn dependent upon how management can embrace these techniques and change as companies will frequently need to transform both internal processes and offerings to customers in order to reap the benefits of AI. AI is a growing research area currently concentrated around technology and modeling of techniques and yet only few examples and limited research are available, on how AI technology enables new capabilities that can impact the value delivered as well as radically transform it. We thus need to understand what new capabilities these technologies bring about and how they are used. Based on three concrete empirical quasi-experiments, interviews conducted with start-ups and a Swedish industrial manufacturing firm dealing with outdoor power products (like grass-cutters, chain-saws, concrete-saws, etc.) for professional and consumer use and using an analytical framework derived from the Resource Based View, this paper explores capabilities enabled through Edge AI and the competitive advantage these may offer. Specific capabilities (self-calibration, enhanced-sensing, selective-capture and reputation) are identified and implications for theory are discussed, pointing out the importance to consider this type of technology not only as a resource, but rather as a dynamic capability in itself.

The characteristics of an aircraft as a noise source changes as the pilots slow the aircraft down and extend flaps and landing gear in preparation for landing. In the OPNOP project, the possibility to use this variation in noise generation to minimize noise at a specified location is examined. Such analysis requires an increased understanding about aircraft noise generation as the aircraft changes configuration and speed during the approach, where theoretical models available can be overly simplistic and of little use for this purpose. Using flight data from 113 Airbus A321 flights, and corresponding noise measurements on the ground, this study reports on the findings forming the foundation on which further analysis will be conducted. Findings relate to: a comparison between models and actual measurements, the distance variability to the runway for various flap selections and extension of the landing gear as well as a comparison between flight data and on-ground noise measurements. Captured data suggest that it should be possible to use speed and configuration recommendations to reduce noise over selected approach areas. Future research will include scenario generation and incorporate flight data from an earlier study to increase validity.

Most industries are shifting from product-orientedbusiness models towards services to step up the value chain andengage in long-term relationships with their customersthroughout the service lifecycle. Digital technologies arecontributing to servitization in many ways by creating andenabling capabilities like connectivity, IoT, data generation andassessment, etc., for new value generation, distribution, andcapture. Because value is subjective, dynamic, and changes duringthe service lifecycle, service providers need to examine closely thevalue perceptions of customers to constantly provide better valueand remain relevant with the competition. Through a consumersurvey and a longitudinal study of thirteen customers, this paperuses qualitative and quantitative assessment to identify the valuedimensions that play a major role for customers being onboardedon a digital enabled service, and also highlights how customervalue dimensions change over the course of the service lifecycle.One important finding is that change in customer value perceptiondoes not follow a pattern and is highly individual and personal.This opens a discussion regarding the need for hyperpersonalizationin successful servitization, and the role of digitaltechnologies towards the same.

This paper investigates noise annoyance from wind turbines of different sizes and in different acoustic surroundings. A listening test was conducted where wind turbine noises were rated alone and together with background sounds from a deciduous forest, a busy city and road traffic. A magnitude production procedure was implemented which showed high correlation between repeated measurements and the results were analysed using A-weighted sound levels, signal-to-noise ratios and time varying loudness and partial loudness. Ratings for wind turbine sound heard alone showed no coherent statistically significant differences between wind turbine types, neither for A-weighted sound levels nor loudness. The masking test indicate that road traffic noise is a superior masker compared to forest sound. However, these effects where only statistically significant at low sound levels, below the range 35-45 dB(A), where noise guidelines for wind turbine noise usually are stipulated.

An explicit algebraic and differential Reynolds stress models (EARSM and DRSM) are used to investigate the influence of homogeneous shear and compression on the behaviour of turbulence in the limit of rapid distortion theory (RDT). EARSM is shown to give realizable results and to preserve RDT regime, unlike the eddy-viscosity model (EVM). The DRSM version of our model is in reasonable agreement with RDT theory. 

Large-eddy simulation (LES) of turbulent channel flow are performed with a new subgrid-scale (SGS) stress model. The simulations show that with this model we can well predict turbulent wall flows at coarse resolutions and moderately high Reynolds numbers. The commonly used dynamic Smagorinsky model fails at coarser resolutions.