Solute Transport Research Articles

Driving sustainable mobility: a study of electric vehicle adoption in rural India

AbstractThe global push for sustainable and eco-friendly transportation solutions has made the adoption of electric vehicles imperative. In India, EV adoption has shown promise, fueled by diverse product offerings and supportive government policies. However, rural India lags behind in EV adoption, despite being a significant market. To unlock the untapped potential, understanding the needs, attitudes, and barriers of rural consumers is crucial. This research conducts a study of rural Indian consumers and offers valuable design insights for automotive companies to formulate effective future strategies.

Impact of sand feed rate on the damage of railway wheel steels

The global railway industry plays a pivotal role in economic development, offering efficient transportation solutions. However, railways operating in desert environments face unique challenges due to windblown sand. This study investigates the influence of sand feed rates on wheel-rail wear in desert conditions. Experimental tests were conducted using a bidisc apparatus to simulate sand feed rates. Results indicate that low feed rates lead to spalling and pitting, while high rates increase abrasive and fatigue wear. A critical transition point at 0.4 g/min suggests sand-induced abrasion of wheel surfaces. Moreover, the research highlights the crucial role of sand feed rates not only in wear but also in surface roughness, further emphasizing the complex interplay between sand transport rates, adhesion, and wear mechanisms. These insights provide valuable guidance for mitigating wear-related challenges in desert railway operations and optimizing maintenance strategies.

Numerical Prediction Uncertainty and Data Worth Analysis of Solute Transport in an Agricultural Clay Till Setting With Preferential Flow

AbstractWith modern agricultural practices, it is essential to quantify flow and solute transport fluxes by numerical models and associated predictions. A major challenge in modeling preferential flow settings is the ability to constrain the often numerous parameters needed to physically represent these systems. Following this, there is a lack of understanding of what parameters and observations carry the most worth for a model to reduce its prediction uncertainty. Here, first‐order second moment (FOSM) analyses were used for a heavily monitored clay till field with preferential flow to investigate which parameters and observation types contribute the most to reducing the uncertainty of bromide predictions at various depths. Using a multi‐objective regularized optimization approach, a 1‐D preferential flow model was calibrated and subjected to FOSM analyses. Key parameters contributing to the prediction uncertainty of bromide concentrations in 0.25–3 m were limited to the lower boundary condition, the mass transfer coefficient, the hydraulic conductivity of the micro‐ and macropore, the macropore porosity, and the water content at wilting point. The data with the largest worth and ability to reduce the pre‐calibration prediction uncertainty were concentration observations closest to the sought prediction depth, drain concentrations, and averaged water table measurements from the entire field. The post‐calibration prediction uncertainty was increased primarily by removing concentration observations closest to the prediction depths. While this study provided new insights into parameter importance and data worth further research is required to understand if these findings apply broadly to clay till settings (or any soil setting) with preferential flow.

A Study on Customer Satisfaction Towards Electronic Cars in Kerala

Abstract: This study delves into the intricate dynamics of customer satisfaction with electronic cars (e-cars) in Kerala, India. With a burgeoning interest in sustainable transportation solutions amidst mounting environmental concerns, understanding the factors contributing to customer satisfaction in this niche market becomes paramount. Through a meticulous cross-sectional survey approach, this research seeks to unearth the nuanced facets of satisfaction among e-car owners in Kerala. By elucidating the drivers of satisfaction and elucidating areas for improvement, this study aims to furnish actionable insights to industry stakeholders, policymakers, and environmental advocates alike.

Nhận thức giá trị xanh, đổi mới thương hiệu xanh và ý định sử dụng xe công nghệ xanh

This study focuses on the analysis related to Green Perceived Value, Green Brand Innovativeness, Green Brand Satisfaction, Green Knowledge, Green Brand Loyalty. Based on background theories and inherited research, the study conducted a survey in HCMC and collected 202 valid assessment samples. Collected data after analyzing using SPSS and Smart PLS 4 showed that Green perceived value, Green brand innovativeness, Green brand satisfaction have a positive impact on Intention to order green technology vehicles. Furthermore, study has not found the impact of Green Knowledge and Green Brand Satisfaction. The study also proposes some appropriate implications to help managers adjust business strategies to promote the intention to place green technology vehicles as a sustainable transportation solution, minimizing negative human impacts on the environment.

Hydrogen-Powered Bicycle for Sustainable Mobility

Abstract: In the pursuit of sustainable transportation solutions, the integration of hydrogen fuel cells presents at the bicycles. This paper explores the high-level design considerations for a hydrogen-powered bicycle, focusing on key components such as the fuel cell system, hydrogen storage, powertrain, and overall vehicle architecture. The fuel cell system is a critical component, converting hydrogen gas into electricity to power an electric motor. The hydrogen storage system must be lightweight, compact, and safe, utilizing advanced materials and technologies to store hydrogen at high pressures. The powertrain of the hydrogenpowered bicycle involves the integration of the electric motor with the bicycle's drivetrain, ensuring efficient power delivered to the wheels. The overall vehicle architecture considers the integration of these components into a functional, user-friendly, and aesthetically pleasing design. Additionally, the paper discusses the environmental impact of hydrogen-powered bicycles, comparing them to traditional bicycles and other forms of transportation. The potential benefits of hydrogen-powered bicycles include zero-emission operation, long-range capabilities, and rapid refuelling times. Overall, the development of hydrogenpowered bicycles has the potential to revolutionize urban mobility, offering a clean, efficient, and sustainable alternative to traditional transportation methods.

Wireless Charging of Electric Vehicle on Highways in Running Mode by Solar Energy

Abstract: The adoption of electric vehicles (EVs) is on the rise globally, driven by the need for sustainable transportation solutions to mitigate climate change and reduce dependence on fossil fuels. However, the infrastructure for charging EVs remains a significant challenge, hindering widespread adoption. In response, innovative approaches such as wireless charging technology, coupled with solar energy, have emerged to address these challenges. This paper presents an overview of wireless charging systems for electric vehicles, integrated with solar energy, aiming to provide a sustainable and convenient solution for EV owners. The integration of wireless charging and solar energy offers numerous benefits, including convenience, efficiency, and environmental sustainability. By eliminating the need for physical connections and harnessing the power of the sun, electric vehicles can be charged seamlessly and sustainably, reducing reliance on the grid and lowering carbon emissions. This paper discusses the principles behind wireless charging technology, the design considerations for integrating solar energy, and the potential impact on the future of sustainable transportation. Additionally, it addresses the challenges and opportunities associated with this innovative approach, highlighting the need for further research and development to realize its full potential. Overall, the convergence of wireless charging technology with solar energy represents a promising solution to the challenges facing the widespread adoption of electric vehicles, contributing to a cleaner and greener transportation ecosystem.

Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

In recent years, increasing concerns about vehicle emissions' environmental and public health impacts have led to the desire to use eco-friendly fuels as alternatives to traditional fossil fuels. Biofuels, hydrogen, and electric power offer lower greenhouse gas emissions and improved air quality, resulting in their development and adoption globally. Predicting vehicle emissions using these fuels is crucial for assessing their environmental benefits. This study proposes using artificial neural networks (ANN), a machine learning technique, to accurately predict vehicle emissions associated with eco-friendly fuels across different compositions and engine speeds. The ANN model has a strong correlation between predicted and observed emissions values, indicating the effectiveness of its model. The research underscores the importance of adopting innovative approaches to address environmental challenges and promote sustainable transportation solutions. This study contributes to reducing the adverse effects of vehicle emissions on air quality and public health by assisting policymakers, car manufacturers, and city planners in making effective decisions. It promotes environmental sustainability by providing valuable insights into vehicle emissions prediction and guiding the development of eco-friendly fuels for a more efficient transportation system.

A conservative first-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modeling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the Sn method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solves them using non-traditional techniques such as ray tracing. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low potential for optimization, as well as not being conservative. A new method has been developed that traces instead to a set of points on each of a zone's surfaces and computes the currents, before using these to obtain the flux. A comparison between these two ray tracing methods shows significant advantages to the new surface method, including inherent particle conservation, similar convergence rate and increased potential for optimization through ray sharing.

Numerical Simulation of a Thermal Management System Using Composite Flame-Retardant Resin and Its Effect on Battery Life Span

One of the obstacles to the adoption of electric vehicles as a future pollution-free transport solution is that the energy sources (batteries) have not yet become sustainable through a long-life span under the specific operating conditions. The problem that arises is that high temperatures inside the batteries represent a safety risk and have negative effects on the battery life span, which imposes the use of thermal management systems. The present article aims to analyze, by numerical methods, the effect of the use of a fireproof composite resin on the efficiency of the thermal management system, specifically on the degree of battery sustainability (measured by the effect on the life span). Five constructive variants are proposed and thermally analyzed. Based on the measured temperatures, the intensity of the chemical reactions that occur in a 18650-type Li-ion cell was calculated, and conclusions related to the impact on the life span were drawn. It has been found that the use of a fireproof composite resin leads to an increased heat transmission towards the outer environment and an increase in the life span by 22.2% compared to that noted for conventional air cooling. The results also recommend the use of heat exchangers associated with flame retardant resins, which leads to a 20.6% improvement in the heat transfer capacity of the battery’s thermal management system. When comparing the solutions in which the flame-retardant resin is used, the results show that adding 3 wt.% of nanomaterial leads to a significant life span increase of 11.7% when compared to the results for the resin-only case.

Investigation of short‐term effects of forage radish on water and solute transport through a tracer test and inverse modeling

AbstractThe benefits of using cover crops for improving soil and water quality are well known. Less clear is whether cover crops, especially those forming a taproot system, can favor solute transport down to the groundwater by modifying soil hydraulic properties and solute dynamics. In this study, we employed 12 lysimeters to conduct a comparative analysis between a taproot cover crop, specifically forage radish (FR), and bare soil (BS), under three water table management conditions. Our objective was to evaluate whether the enhancement of root‐derived macroporosity could have modified water and solute dynamics, and offset the benefits provided by FR that is commonly used to mitigate solute leaching. A tracer solution of bromide (Br−) was added to lysimeters, and solute flux concentrations were determined at different depths during a 25‐day test. Soil moisture and pressure heads were monitored. Water and solute transport parameters were estimated by inverse modeling using HYDRUS‐1D. A complementary laboratory experiment was performed to quantify the effect of FR root apparatus on the macropore structure by using noninvasive X‐ray microtomography (µCT). Results showed that the growth of FR within the lysimeters induced alterations in water and solute dynamics compared with BS. This is primarily attributed to its proficiency as solute scavenger, with an uptake capacity of up to 47% of the total injected tracer. Our comparative analysis instead revealed subtle differences in soil structure and hydraulic properties brought about by the presence of FR. Major changes were observed for the saturated hydraulic conductivity (Ks), which increased from an average of 8.4–49.8 cm day−1 within the 20–45 cm layer in BS and FR, respectively. Additionally, there was a difference in immobile water content (θim), with the values in FR averaging 21% lower than those in BS. These modifications can be attributed to the formation of fissures and channels, primarily concentrated in the proximity of taproot development, without extending into deep preferential flow pathways. These structural changes were supported by the nondestructive µCT analyses. Upon aggregating the effects observed, solute movement to groundwater was not affected by FR compared to BS conditions.

Laboratory and numerical simulations of infiltration process and solute transport in karst vadose zone

Groundwater in karst systems is a vital resource, which is often highly vulnerable to contaminants that infiltrate through the vadose zone. Understanding the processes of water and contaminant infiltration in the vadose zone is extremely important for protecting and managing karst water resources. A laboratory-scale physical model based on a typical conceptual model of the karst vadose zone was constructed to characterize the influence of the rainfall intensity and internal structure on the infiltration process and solute transport in the karst vadose zone under controlled conditions. Five factors, including the rainfall intensity, the surface slope, the degree of karstification, the thickness of the transfer zone, and the existence of epikarst, were considered in laboratory experiments. A corresponding lumped-parameter model was subsequently developed to further analyze and investigate the water and solute infiltration processes in the laboratory scale physical model. Recession- and breakthrough curves generated from the lab experiments and the water and solute loss from each reservoir generated from the simulation results were analyzed. The downward trend of breakthrough curves' peak value and the variation of solute loss's proportion in three rainfall events demonstrate that both the increase in thickness and karstification degree will intensify the buffer effect of the karst vadose zone, and this buffer effect is mainly affected by the slow flow system which can be inferred from the increasing percentage of cumulative discharge of slow reservoir (qS). With increasing karstification degree, the recession coefficient (α) exhibits opposite trends depending on the existence of epikarst or not, indicating the karstification degree has a more significant effect on hydraulic conductivity (K) when an epikarst is present while affecting effective porosity (φ) more noticeably in the absence of epikarst. The rainfall intensity affects the results of both experimental and numerical simulation most significantly. The percentage of discharge and solutes flowing from the slow reservoir (qS and msoil_S) have negative correlations with rainfall intensity, suggesting a greater influence on fast flow with increasing rainfall intensity.

The Folding Pathway of ABC Transporter CFTR: Effective and Robust

De novo protein folding into a native three-dimensional structure is indispensable for biological function, is instructed by its amino acid sequence, and occurs along a vectorial trajectory. The human proteome contains thousands of membrane-spanning proteins, whose biosynthesis begins on endoplasmic reticulum-associated ribosomes. Nearly half of all membrane proteins traverse the membrane more than once, including therapeutically important protein families such as solute carriers, G-protein-coupled receptors, and ABC transporters. These mediate a variety of functions like signal transduction and solute transport and are often of vital importance for cell function and tissue homeostasis. Missense mutations in multispan membrane proteins can lead to misfolding and cause disease; an example is the ABC transporter Cystic Fibrosis Transmembrane Conductance Regulator (CFTR). Even though our understanding of multispan membrane-protein folding still is rather rudimental, the cumulative knowledge of 20 years of basic research on CFTR folding has led to development of drugs that modulate the misfolded protein. This has provided the prospect of a life without CF to the vast majority of patients. In this review we describe our understanding of the folding pathway of CFTR in cells, which is modular and tolerates many defects, making it effective and robust. We address how modulator drugs affect folding and function of CFTR, and distinguish protein stability from its folding process. Since the domain architecture of (mammalian) ABC transporters are highly conserved, we anticipate that the insights we discuss here for folding of CFTR may lay the groundwork for understanding the general rules of ABC-transporter folding.

Fueling the Future: The Case for Heavy-Duty Fuel Cell Electric Vehicles in Sustainable Transportation

In the global pursuit of transportation decarbonization, this essay asserts that heavy-duty fuel cell electric vehicle (FCEV) technology represents a more compelling path forward than light-duty FCEVs. The inherent advantages of fuel cells, such as extended range, rapid refueling, and sustained performance, make them well-suited for demanding applications like trucking and public transit. Heavy-duty FCEVs offer significant potential for emissions reduction, integration with existing infrastructure, and economies of scale through commercial fleet adoption. While the upfront investment is substantial, the essay examines how operational benefits can gradually offset costs, emphasizing the importance of heavy-duty FCEVs in sustainable transportation solutions.

Bi-level path tracking control of tractor semi-trailers by coordinated active front steering and differential braking

In recent years, autonomous tractor semi-trailers have been considered a promising solution for transportation on highways, ports, and large logistics centers to improve safety and efficiency. However, due to the complicated dynamics introduced by articulation structure, there are still challenges in achieving high-precision path tracking and stability control for tractor semi-trailers, especially in critical scenarios. In this work, a bi-level path tracking control scheme for the autonomous tractor semi-trailer is proposed. At the higher level, a robust model predictive controller coordinating active front steering and differential braking is devised for addressing path tracking, stability control and robustness for an autonomous tractor semi-trailer. At the lower level, incorporating a logic switching mechanism, the designed optimal braking torque distribution module and the PID-based longitudinal control module prioritize the implementation of differential braking and target speed tracking. Simulation results demonstrate that the proposed control strategy offers remarkable path tracking performance in three designed testing scenarios.

Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Insights into Passenger Transport Choices: A Mumbai Case Study

Understanding and studying passenger preferences in passenger transportation systems is important to optimise transportation infrastructure and increase passenger satisfaction. This paper presents a comprehensive case study conducted in Mumbai, India, which aims to understand the diverse preferences of commuters using different modes of transport within the city. Adopting a mixed-methods approach, data were accumulated by means of surveys, interviews, and observational studies conducted across different demographic sectors and geographical locations of Mumbai. The study analysed factors influencing commuters' mode choice, including cost, comfort, convenience, travel time, safety, and environmental concerns. The findings revealed significant differences in the preferences of commuters, impacted by factors like socioeconomic status, occupation, travel distance, and access to infrastructure. While public transportation emerged as the dominant mode for daily commuting, preferences were nuanced, some demographics prefer private modes like cars and motorcycles. Additionally, the study identifies areas of improvement in the existing transport infrastructure and suggests measures to enhance the reliability, frequency, safety, and connectivity of public transport services. The knowledge gleaned from this research provides valuable guidance for policymakers, urban planners, and transportation authorities in Mumbai, offering opportunities to tailor transportation solutions that align with commuters' preferences, thereby promoting sustainable and efficient passenger transport systems in the city.

Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media

Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics.

State of the Art on Fe Precipitation in Porous Media: Hydrogeochemical Processes and Evolving Parameters

The mixing of terrestrial groundwater and seawater creates dynamic reaction zones in intertidal areas, where land-derived Fe(II) is oxidized to Fe(III) and then precipitates as Fe hydroxides at the groundwater–seawater interface. These hydrogeochemical processes contribute to the formation of iron bands at the saltwater wedge (SW) and beneath the upper saline plume (USP). This study provides a comprehensive review of physical and geochemical processes at field scale in coastal areas, explores the impact of mineral precipitation on pore structure at pore scale, and synthesizes reactive transport modeling (RTM) approaches for illustrating continuum-scale soil physio-chemical parameters during the evolution of porous media. Upon this review, knowledge gaps and research needs are identified. Additionally, challenges and opportunities are presented. Therefore, we reach the conclusion that the incorporation of observational data into a comprehensive physico-mathematical model becomes imperative for capturing the pore-scale processes in porous media and their influence on groundwater flow and solute transport at large scales. Additionally, a synergistic approach, integrating pore-scale modeling and non-invasive imaging, is equally essential for providing detailed insights into intricate fluid–pore–solid interactions for future studies, as well as facilitating the development of regional engineering-scale models and physio-chemical coupled models with diverse applications in marine science and engineering.

Shannon entropy of transport self-organization due to dissolution–precipitation reaction at varying Peclet numbers in initially homogeneous porous media

Abstract. Dissolution and precipitation processes in reactive transport in porous media are ubiquitous in a multitude of contexts within the field of Earth sciences. In particular, the dynamic interaction between the reactive dissolution and precipitation processes and the solute transport is of interest as it is capable of giving rise to the emergence of preferential flow paths in the porous host matrix. It has been shown that the emergence of preferential flow paths can be considered to be a manifestation of transport self-organization in porous media as these create spatial gradients that distance the system from the state of perfect mixing and allow for a faster and more efficient fluid transport through the host matrix. To investigate the dynamic feedback between the transport and the reactive processes in the field and its influence on the emergence of transport self-organization, we consider a two-dimensional Darcy-scale formulation of a reactive-transport setup, where the precipitation and dissolution of the host matrix are driven by the injection of an acid compound, establishing local equilibrium with the resident fluid and an initially homogeneous porous matrix, composed of a calcite mineral. The coupled reactive process is simulated in a series of computational analyses employing the Lagrangian particle-tracking (LPT) approach, capable of capturing the subtleties of the multiple-scale heterogeneity phenomena. We employ the Shannon entropy to quantify the emergence of self-organization in the field, which we define as a relative reduction in entropy compared to its maximum value. Scalability of the parameters, which characterize the evolution of the reactive process, with the Peclet number in an initially homogeneous field is derived using a simple one-dimensional ADRE model with a linear adsorption reaction term and is then confirmed through numerical simulations, with the global reaction rate, the mean value, and the variance of the hydraulic-conductivity distribution in the field all exhibiting dependency on the reciprocal of the Peclet number. Our findings show that transport self-organization in an initially homogeneous field increases with time, along with the emergence of the field heterogeneity due to the interaction between the transport and reactive processes. By studying the influence of the Peclet number on the reactive process, we arrive at a conclusion that self-organization is more pronounced in diffusion-dominated flows, characterized by small Peclet values. The self-organization of the breakthrough curve exhibits the opposite tendencies, which are observed from the perspective of a thermodynamic analogy. The hydraulic power, required to maintain the driving head pressure difference between the inlet and outlet of the field, was shown to increase with the increasing variance, as well as with the increasing mean value of the hydraulic-conductivity distribution in the field, using a simple analytic model. This was confirmed by numerical experiments. This increase in power, supplied to the flow in the field, results in an increase in the level of transport self-organization. Employing a thermodynamic framework to investigate the dynamic reaction–transport interaction in porous media may prove to be beneficial whenever the need exists to establish relations between the intensification of the preferential flow path phenomenon, represented by a decline in the Shannon entropy of the transport, with the amount of reaction that occurred in the porous medium and the change in its heterogeneity.