Elevated exposure to airborne pollutants such as NOx and SOx is known to be damaging to human health. A current approach to deal with such harmful gases is to trap them in ammonium salt particles. The present study presents the sensitivity analysis of the aerosol model for ammonium salt particle formation from NOx and SOx for low-temperature gas cleaning applications developed by Olenius et al. (2021). Starting from the acid gases derived from NOx and SOx (i.e. HNO3 and H2SO4), the model simulates the particle growth phenomena as the acids react with ammonia (NH3). This work presents, for the first time, a global sensitivity analysis of the aerosol model uncertainty. The first-and total-order effects of five different input variables on model outputs such as particle size distribution, pollutant removal effectiveness, ammonia slip, and total run time are reported. Furthermore, the range of input parameters for which the model is tested is made to emulate the conditions experienced by two-stroke marine diesel engine ships. Sources of uncertainty are reviewed in detail to provide a complete view of the knowledge gaps in the particle conversion process. For the conditions studied, we report that variations in particle sizes are influenced by HNO3, H2SO4 and temperature. Similarly, the degree of ammonia slip was observed to be driven by temperature and the ammonia ratio. Additionally, the removal efficiency of HNO3 was reported to be very high (above 99%) for the vast majority of conditions tested, and was not significantly influenced by the concentration of H2SO4. Finally, the model run time variability was observed to depend mainly on variations in temperature, relative humidity and the ammonia ratio.

This study examines the capabilities of a data-driven workflow for automated key feature identification in reactive flows. The proposed approach aims at expediting the analysis of chemical engineering datasets by generating an automatic and explainable classification of regions showcasing distinct physics. The three main steps of this process, i.e., dimensionality reduction, unsupervised clustering, and feature correlation are discussed. A previously published framework based on these steps is used to compare against our proposed workflow, which employs different and more modern algorithms. The theoretical and practical differences between the previous and current algorithms are demonstrated in full. Overall, the key feature identification capability of the updated workflow is shown to be faster, more accurate, more robust, and closer to human intuition than previous methods. Throughout this study no substantial knowledge of machine learning is required from the reader. This makes this work also double up as a tutorial for researchers aiming at applying these algorithms.

As epidemiological evidence continues to mount, it has become undeniable that exposure to high levels of airborne pollutants such as SO_x and NO_x are detrimental to human health. In recent years, millions of premature deaths by stroke, coronary heart disease, and lung cancer worldwide have been linked to poor outdoor air quality.

Unfortunately, not all polluting industries have faced the same stringent regulations. For instance, restrictions on harmful pollutant emissions from road vehicles have remained higher than those from marine transport. This discrepancy between sectors is expected to shrink as an increasing number of industries come into the spotlight of regulators. In this rapidly changing landscape, the demand for effective and innovative pollution abatement solutions is rising.

In the present work, our focus is on investigating the viability of a novel airborne pollutant removal concept. In this no-waste design, SO_x and NO_x are trapped into ammonium salt particles that can be then sold as an agricultural fertilizer. The gaseous pollutants are first oxidized by ozone, which is then mixed with ammonia in humid air to allow the ammonium particles to form and grow.

The study of this system requires analyzing the interplay between chemical reactions and the turbulent fluid dynamics that enables them through efficient mixing. To this end, numerical simulations are an invaluable tool that facilitates uncovering detailed knowledge where experimental studies may be intractable. Here, we leverage the use of high-fidelity large-eddy simulations to study reactive and non-reactive flow conditions relevant to this multi-pollutant removal solution. These investigations are supplemented by reactor modeling approaches to analyze specific key chemical processes. Finally, we implement and employ state-of-the-art data-driven methods that provide enhanced insight into our numerical datasets. For this purpose, we apply proper orthogonal decomposition, a machine-learning workflow for automated region identification, and global sensitivity analysis.

Triply Periodic Minimal Surfaces (TPMS) have promising thermophysical properties, which makes them a suitable candidate in the production of low-temperature waste heat recovery systems. A TPMS thermal performance is connected to the complex flow patterns inside the pores and their interactions with the walls. Unfortunately, the experimental study's design analysis and optimization of TPMS heat exchangers are complicated due to the flow pattern complexity and visual limitations inside the TPMS. In this study, three-dimensional steady-state, conjugate heat transfer (CHT) simulations for laminar incompressible flow were carried out to quantify the performance of a TPMS based heat exchanger. TPMS Lattices based on Schwartz D architecture was modeled to elucidate the design parameters and establishing relationships between gas velocity, heat transfer, and thermal performance of TPMS at different wall thicknesses. In this study, four types of lattices from the same architectures with varying wall thickness were examined for a range of the gas velocity, with one design found to be the optimized lattice providing the highest thermal performance. The results and methodology presented here can facilitate improvements in TPMSheat exchangers' fabrication for recycling the waste heat in low pitch thermal systems.

Limiting gaseous nitrogen oxide (NOx) emissions is a major global concern due to their harmful effect on human health and the environment. During the past decade, low-temperature oxidation by ozone (O3) has emerged as a promising solution for NOx removal in the transportation and energy generation sectors. In the present study, three-dimensional (3D) large eddy simulations (LES) of the turbulent reacting flow inside a NOx-O3 reactor are performed. It is the first publication using LES and detailed finite rate chemistry for such a reactor. Additionally, plug-flow reactor (PFR) simulations are employed to identify the best performing chemical kinetic mechanisms among those available in the literature over the range of conditions studied. Furthermore, results from different experimental works are reviewed to analyze the variability in the literature. Additionally, this will aid in devising a strategy for validation. Time-averaged results obtained from PFR and combined LES-PFR simulations are observed to agree well with experimental results. The species correlation, distribution, and overall flow uniformity in the pure LES simulations are also assessed and the results highlight the benefits of a high mixing efficiency reactor geometry. Moreover, relevant features of the unsteady flow throughout the present reactor are studied. In particular, the analysis of coherent structures and the existence of regions with non-reacted gases and their relation to mixing and NOx oxidation efficiency are evaluated. The present simulations enable a novel understanding of the interplay between mixing and chemistry and highlight, for the first time, details of the oxidation of NOx by O3.

Although the mean flow features of the turbulent jet in counterflow have been studied in the past, the large-scale dynamics of this flow configuration remain unexplored. The present work presents the modal analysis, through proper orthogonal decomposition (POD), of large eddy simulations (LESs) of counterflowing jets at different jet-to-counterflow velocity ratios (α=2.2,3.4,5.1) reported in detail by Rovira, Engvall, and Duwig [Phys. Fluids 32, 045102 (2000)PHFLE61070-663110.1063/5.0003239]. In the present study, a qualitative investigation of the three-dimensional (3D) turbulent structure of this jet configuration is performed by vortex identification. Additionally, a simplified description of the origin and development of these coherent structures is presented. Planar two-dimensional (2D) POD results for the case with α=3.4 are directly compared and found to be in close agreement with the results available literature. In this case, over an equal time interval, temporal and spatial resolutions are observed to have a minor effect on mode energy content. All three cases are also analyzed with 3D POD and the evolution of the peak mode frequency with α is studied. Additionally, by employing a wavelet transform, the intermittent behavior of the fundamental mode dynamics is evidenced for the first time. Finally, the jet in counterflow case with α=5.1 is analyzed with a 3D spectral POD. Varying jet penetration, precession, and a stretching and contracting motion are found to be the most dominant modes. 

For many industrial applications, jets with high mixing performance are essential. While effective, the complexity of active control for enhanced mixing makes it less desirable than passive techniques. To that extent, the jet in counterflow is a relatively unexplored alternative, which has been shown to improve mixing compared to other jet configurations (Yoda and Fiedler in Exp Fluids 21:427–436, 1996 [1]). 

A turbulent jet in counterflow is a lesser-studied jet configuration which exhibits great potential for mixing applications in sustainable energy production. In this paper, a comprehensive literature review of the research in counter-flowing jets is performed. Experimental and numerical results for mean and turbulent quantities are reviewed, and similarities and differences between datasets are discussed. Additionally, large eddy simulations (LESs) are carried out in order to study a turbulent jet in counterflow at several jet-to-counterflow velocity ratios (alpha = 2.2, 3.4, 5.1). The effect of two different jet inflow conditions is investigated. The LES results are directly compared to the available literature, and the subsequent analysis sheds light on the differences seen in the review. Finally, a set of recommendations and best practices are provided in order to aid future studies of jets in counterflow.

This study presents the sensitivity analysis of the aerosol model for ammonium salt particle formation from NOx and SOx pollutants for low-temperature gas cleaning applications developed by Olenius et al. (2021). Starting from the acid gases derived from NOx and SOx (i.e. HNO3 and H2SO4), the numerical model simulates different particle growth phenomena as the acids react with ammonia (NH3). In the present study, the aerosol model uncertainty is analyzed systematically for the first time through a global sensitivity analysis employing the Sobol' method. The first- and total-order effects of five different input variables on model outputs such as particle size distribution, pollutant removal effectiveness, ammonia slip, and total run time are reported. Furthermore, the range of input parameters for which the model is tested is made to emulate the realistic operations experienced by low-speed two-stroke marine diesel engine ships burning fuel with high sulfur content. The sources of uncertainty are reviewed in detail to provide a holistic yet more complete view of the knowledge gaps in the particle conversion process.