Detailed Physical Model Research Articles

Optimization of charging infrastructure and strategy for an electrified public transportation system

Government policies and incentives aimed at reducing the carbon footprint are increasingly focusing on the electrification of public transportation, particularly transit buses. However, electrification faces significant challenges, including optimizing the charging infrastructure, battery size and type, and charging strategies. Addressing these challenges is crucial for the effective deployment and operation of electric bus fleets. This study presents an innovative method for optimizing these aspects of electric bus systems under diverse route conditions. By leveraging general transit feed specification (GTFS) and GeoTIFF data, the developed approach ensures scalability and is grounded in real-world data. A detailed physical model, especially concerning battery degradation, adds a unique dimension to the study, providing more accurate and reliable results. The optimization method employed in this study is dynamic programming (DP), which allows for a comprehensive evaluation of various factors influencing the performance and efficiency of electric buses. The proposed approach has been validated through three distinct case studies. The findings of this study indicate that the optimized solution can lead to a substantial cost reduction of nearly 35% for operators compared to current state-of-the-art practices in Zurich, which underscores the potential of the proposed approach to contribute to more sustainable and cost-effective public transportation systems.

CFD analysis of rotation effect on flow patterns and heat transfer enhancement in a horizontal spiral tube heat exchanger

This study explores the enhanced thermal performance of heat exchangers utilizing spirally coiled tubes, particularly in applications such as heating saltwater in solar desalination plants, which require elevated heat transfer coefficients. A numerical investigation is conducted to assess the impact of mechanically rotating horizontal spiral tubes on flow patterns and temperature profiles along their length. A detailed physical model was developed using COMSOL Multiphysics software. The findings from computational fluid dynamics simulations indicate that mechanical rotation significantly modifies both velocity and temperature gradients at each cross-section of the tube. This rotation effectively reduces the formation of thermal hotspots in the outer regions, thereby improving and accelerating heat dispersion. Notably, substantial variations in velocity and temperature profiles occur at rotation speeds up to 4 rpm; however, these changes diminish beyond this speed threshold. The study reveals that rotation increases the Nusselt number of the heated flow within the tube by over 145 %. Furthermore, the effects of rotation are more pronounced in smaller diameter tubes compared to larger ones. Ultimately, the performance factor indicates that the benefits of enhanced heat transfer outweigh the increased pressure drops associated with tube rotation, validating the effectiveness of the proposed heating system.

A Model for Dry Electron Beam Etching of Resist

This paper presents a detailed physical model for a novel method of two- and three-dimensional microstructure formation: dry electron beam etching of the resist (DEBER). This method is based on the electron-beam induced thermal depolymerization of positive resist, and its advantages include high throughput and relative simplicity compared to other microstructuring techniques. However, the exact mechanism of profile formation in DEBER has been unclear until now, hindering the optimization of this technique for certain applications. The developed model takes into account the major DEBER phenomena: e-beam scattering in resist and substrate, e-beam induced main-chain scissions of resist molecules, thermal depolymerization of resist, monomer diffusion, and resist reflow. Based on the developed model, a simulation algorithm was implemented, which allowed simulation of the profile obtained in resist by DEBER. Experimental verification of the DEBER model was carried out, which demonstrated the reliability of the model and its applicability for theoretical study of this method. The ultimate DEBER characteristics were estimated by simulation. The minimum line width and the maximum profile slope that could be obtained by DEBER were approximately 300 nm and 70°, respectively.

Advanced frequency control schemes and technical analysis for large-scale PEM and Alkaline electrolyzer plants in renewable-based power systems

Integrating electrolyzers into power systems can significantly contribute to sustainable energy via the generation of green hydrogen while also enhancing frequency stability through effective regulation of the electrolyzers’ operating power. This study gives a comprehensive analysis of large-scale electrolyzer plants when providing frequency support to power systems. First, the authors present a model predictive control (MPC)-based secondary frequency controller, combined with a droop controller as the primary frequency controller and a virtual inertia controller. Additionally, the study introduces a universal system frequency response (U-SFR) modeling approach that enables high accuracy, low computation burden, and reduced initial parameters as a testbed. Finally, an in-depth analysis is conducted, focusing on different technical aspects of large-scale electrolyzer plants when providing frequency support services. Case studies integrating PEM and Alkaline electrolyzers into the modified IEEE 39-bus system with over 50% wind power penetration are conducted. It is found that the proposed U-SFR model achieves high accuracy with lower computational time compared to detailed physical models. Additionally, model predictive controllers improve frequency quality more effectively than PID and PID-FLC methods. PEM electrolyzers are found to be more efficient in providing grid frequency support than alkaline electrolyzers due to their technical characteristics. Finally, smaller hydrogen tanks may frequently breach storage constraints, negatively impacting the system’s frequency response capability.

Quantitative evaluation on the cavitation damage energy of metals via multiscale approaches

In-situ experimental observation cannot be an effective method in the impact process of metal cavitation erosion, and the impact damage energy prediction theory has not been established based on a detailed mathematical and physical model. Research has shown that the metal material can be regarded as a sensor to reflect the impact parameters. Soft metal materials such as industrial pure copper with a single α-phase microstructure can be a suitable sensor to detect cavitation erosion effects, revealing the impact behaviors of the cavitation jet through the depth, width and spatial distribution of cavitation pits on the surface. In this scenario, volume loss of cavitation pits on a macro/mesoscale are obtained by three-dimensional surface measurement. A coupled fluid-solid simulation model calculating the impact damage energy on a macro/mesoscale is established based on the Smoothed Particle Hydrodynamics (SPH) and the Crystal Plasticity Finite Element Method (CPFEM). Python programming is used to set the trajectory of SPH particles to determine the macro cavitation flow field. IFEM methods were used to construct the prediction theory of cavitation impact energy in a relatively simple exponential equation, establishing the quantitative relationship between cavitation impact damage energy and measured volume of cavitation pits. Compared with conventional measurement and simulation, the present multiscale approaches are assumed as a further step advancement towards calculating the impact damage energy of cavitation water jet.

Validation and parametric study of FFRD model in DRACCAR code

The recent revision of Emergency Core Cooling System (ECCS) regulations in Korea has necessitated the consideration of Fuel Fragmentation, Relocation, and Dispersal (FFRD) phenomena in nuclear reactor safety analyses. Consequently, the Korea Atomic Energy Research Institute (KAERI) has developed and integrated the FFRD model into the domestically licensed safety analysis code, SPACE. Globally, US NRC’s FRAPTRAN and IRSN’s DRACCAR are available for evaluating FFRD phenomena. The FRAPTRAN code incorporates the FFR model, developed by Quantum Technology, while the fuel dispersal model is currently not included. In contrast, the DRACCAR code functions as an integrated analysis platform capable of modeling multi-dimensional thermal, hydraulic, mechanical, and chemical phenomena during a Loss of Coolant Accident (LOCA). This study conducts a thorough examination of the FFRD model in the DRACCAR code and validates its applicability through analysis using the Halden IFA-650 tests. The results demonstrate satisfactory predictive capabilities. Furthermore, a parametric study of key FFRD model parameters enhances the understanding of the FFRD model in the DRACCAR code. The development of detailed physical models in the FFRD model could significantly enhance the performance of the DRACCAR code, warranting the establishment of a comprehensive framework for these advancements. In the future, code-to-code comparisons between the DRACCAR code and other domestically developed integrated analysis platforms will be conducted to investigate various phenomena in depth.

Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments

AbstractWith the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments.

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

AbstractWith the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.

Effects of non-continuous inverse Compton cooling in blazars

Blazar flares provide a window onto the extreme physical processes occurring in relativistic outflows. Most numerical codes used for modelling blazar emission during flares use a simplified continuous-loss description of particle cooling due to the inverse Compton (IC) process, neglecting non-continuous (discrete) effects that arise in the Klein-Nishina (KN) regime. The significance of such effects has not yet been explored in detail. In this study, we investigate the importance of non-continuous Compton cooling losses and their impact on the electron spectrum and spectral energy distribution (SED) of blazars during high flux states (flares), as well as in the low state. We solve the full transport equation numerically, accounting for large relative jumps in energy by extending our existing blazar flare modelling code EMBLEM. We perform a detailed physical modelling of the brightest gamma -ray flare of the archetypal flat-spectrum radio quasar (FSRQ) 3C\,279 detected in June 2015. We then compare results obtained using the full cooling term and using the continuous-loss approximation. We show that during flaring states of FSRQs characterised by high Compton dominance, the non-continuous cooling can lead to significant modification of the electron spectrum, introducing a range of distinct features, such as low-energy tails, hardening and/or softening, narrow and extended particle excesses, and shifts in the cooling break position . Such distortion translates to differences in the associated SED of up to sim 50<!PCT!>. This highlights the importance of non-continuous effects and the need to consider them in blazar emission models, particularly applied to extreme gamma -ray flares.

Design–operation gap caused by parameter variance in HVAC system control sequences: A simulation-based study on energy efficiency and temperature controllability

During the design phase, the annual energy consumption of heating, ventilation, and air-conditioning (HVAC) systems is frequently simplified or fixed under certain assumptions due to uncertain parameters within the control sequence, resulting in a performance gap between the intended and actual performances. This study assessed the impact of six uncertain parameters on system energy consumption and indoor temperature control under various updating scenarios. First, sensitivity analysis was conducted using the one-at-a-time method to identify the influential impact of the single uncertain parameter. Then, mutual impact analysis was conducted to showcase how different parameters affect each other, and mixed impact analysis was applied to evaluate how intended design performance converges to actual operating outcomes. A detailed physical model of a single-zone variable air volume system was developed to enable a comprehensive evaluation across different operational conditions. The results indicate that a 10% change in some parameters can lead to a 20∼30% increase in energy consumption, especially during low-load winter months. Properly evaluating the influence of one parameter is difficult without taking the effects of additional parameters into account, and some combinations of parameters can significantly decrease the system performance. These results underscore the necessity of considering the updating and combination scenarios of different parameters within the control sequences during the design phase.

The effect of yaw damper performance degradation on carbody hunting of high-speed trains

ABSTRACT The yaw damper is an integral and pivotal component in guaranteeing the carbody hunting stability of high-speed trains (HSTs). However, it is reported that the occurring incidences of low-frequency carbody hunting frequently have been observed in HSTs, a predicament attributed to the degradation of yaw damper performance resulting from the obstruction of the damping valve by foreign particulates. Given these circumstances, the objective of this study is to investigate the impact of yaw damper performance degradation on the intricate phenomenon of carbody hunting motion. A HST dynamic model is developed, incorporating a detailed physical parameter model of the yaw damper, which enables the reproduction of abnormal low-frequency swaying arising from the deterioration in yaw damper performance. The findings of this study demonstrate that the obstruction of the damping valve significantly augments the damping force and dynamic stiffness of the yaw damper, thereby potentially instigating the occurrence of severe low-frequency hunting motion and consequent deterioration in the ride comfort of HSTs.

Design and analysis of an electromagnetic energy conversion device

In this study,we introduces an innovative device designed for wave-heat-electricity conversion, incorporating a classical split-ring resonator (SRR) and a Bi2Te3 semiconductor strip. This configuration is adept at absorbing electromagnetic energy, transforming it into thermal energy, and facilitating an electrical response. The paper delves into the detailed physical modeling and operational principles of the device. We begin by employing an equivalent circuit model to analyze the SRR's transmission characteristics and its temperature response under specific conditions. The investigation further extends to examining how structural dimensions and material properties affect the development of a local hotspot, leading to the refinement of the SRR resonant unit. We then integrate a Bi2Te3 semiconductor strip beneath this hotspot, enhancing the device's conversion capabilities. Our findings indicate an absorption efficiency of 0.085 at 2.45 GHz, a remarkable thermal conversion efficiency of 172.8 °C/W, and an electrical conversion efficiency of 0.195 mV/°C, all measured at an input power of 7 W. This research not only demonstrates the efficacy of the proposed device in wave-heat-electricity conversion but also provides a comprehensive reference for future advancements in this field.

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning

AbstractThe energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of predictive driving functions. This paper investigates a method that uses system identification and neural networks with bidirectional long short‐term memory layers to predict the power requirements of the auxiliaries depending on information that is known prior to the trip. By using a self‐learning, data‐driven approach as well as data that can be measured without additional instrumentation, a prediction is made possible without the need to design detailed physical models in advance. Additionally, a rule‐based allocation of the training data based on environmental conditions is implemented, which serves to adapt individual models to different climatic modes of the thermal system. The potential of the method is demonstrated for three different systems showing a prediction accuracy of on average 3% to 8% in terms of energy, while the deviation of the predicted power consumption is on average about 500 watts. Due to the complete automation of the process, a further increase in prediction accuracy can be expected.

A Stackelberg Game Based Optimization Method for Heterogeneous Building Aggregations in Local Energy Markets

This paper proposes an optimal operational method and a customized pricing strategy for a Microgrid (MG) incorporating heterogeneous buildings from different communities in a local energy market (LEM). A customized pricing strategy in the LEM for the heterogeneous building aggregations (BAs) is proposed considering their heterogeneous insulation performances. In this regard, a hierarchical framework with two levels for optimal decision-making of the MG as the price maker and BAs as the price takers in the LEM are presented. At the upper level, the MG aims to maximize its profit by optimizing the customized prices and its energy schedules. At the lower level, a detailed physical model of the building with adjustable heating, ventilation, and air conditioning (HVAC) systems is developed while considering the building's thermal dynamics. The objective of each BA in each community is to minimize its operating cost while ensuring the users' thermal comfort according to MG's prices. The Stackelberg game is used to model the interactions between the MG (leader) as the price maker and multiple heterogeneous BAs (followers) as the price takers in the LEM. Then, the proposed optimization model is converted into a mixed integer linear programming using the Karush-Kuhn-Tucker conditions and strong duality theorem. Numerical results show that the hierarchical method benefits both the MG and heterogeneous BAs with customized pricing schemes. The HVAC of the building with good thermal insulation performance can make more adjustments to indoor temperature to reduce its operating costs. Moreover, it has a more significant impact on MG's electricity prices.

Large Eddy Simulation of Soot Formation in a Real Aero-Engine Combustor Using Tabulated Chemistry and a Quadrature-Based Method of Moments

Abstract Considering the increasingly stringent targets for aircraft emissions, computational fluid dynamics (CFD) is becoming a viable tool for improving future aero-engine combustors. However, predicting pollutant formation remains challenging. In particular, directly solving the evolution of soot particles is numerically expensive. To reduce the computational cost but retain detailed physical modeling, quadrature-based moments methods can be efficiently employed to approximate the particle number density function (NDF). An example is the recently developed split-based extended quadrature method of moments (S-EQMOM), which enables a continuous description of the soot particles' NDF, essential to consider particle oxidation accurately. This model has shown promising results in laminar premixed flames up to turbulent laboratory scale configurations. However, the application to large-scale applications are still scarce. In this work, the S-EQMOM model is applied to the Rolls-Royce BR710 aero-engine combustor to investigate the soot evolution process in practically relevant configurations. For this, the soot model is embedded into a high-fidelity simulation framework, consisting of large eddy simulation for the turbulent flow and mixing and the flamelet-generated manifold method for chemistry reduction. An additional transport equation for polycyclic aromatic hydrocarbons is solved to model their slow chemistry and the transition from the gaseous phase to the solid phase. Simulations are performed for different operating conditions (idle, approach, climb, takeoff) to validate the model using experimental data. Subsequently, the results are analyzed to provide insights into the complex interactions of hydrodynamics, mixing, chemistry, and soot formation.

Phase-change materials for storing heat extracted from heat pump condensers

This study aims to investigate the storage of heat from a heat pump's condenser in a well-insulated cylindrical tank filled with spherical capsules containing a phase-change material (PCM). Using a detailed physical model and the enthalpic method, the system's operation and phase change phenomena are simulated. The impact of various operational parameters, such as condenser power, water flow rate, storage volume, and PCM type, is examined. The results reveal that increasing condenser power significantly reduces the PCM loading time, achieving a 66 % reduction within the 5 kW to 15 kW power range. Additionally, decreasing the water flow rate enhances storage efficiency by 15 % when the flow rate drops from 2.3 m3/h to 1 m3/h. The use of a PCM with a melting temperature of 44 °C is deemed more suitable for the system compared to other options.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework – which we call HydroGraphs – for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

A wavefront adaptive sensing beamformer for ocean acoustic waveguides.

This paper addresses robust adaptive beamforming for passive sonar in uncertain, shallow-water environments. Conventional beamforming is still common in passive sonar because adaptive beamformers suffer from signal mismatch in complex multipath environments. Existing approaches to robust adaptive beamforming try to model and account for the uncertainty in the beamformer's hypothesized signal subspace by using additional linear or quadratic constraints. Doing so, however, reduces the adaptivity of the beamformer and is prone to insufficiently suppressing interference. Instead, this paper uses blind source separation methods to adaptively estimate the complex spatial wavefronts of both targets and interference without requiring detailed physical modeling of the channel. By exploiting the different temporal spectra and/or frequency-selective multipath fading of targets and interference, this approach constructs a "signal-free" covariance matrix without imposing directional gain constraints. In doing so, the wavefront adaptive sensing (WAS) beamformer is able to separate targets from strong interference that is within the conventional beam width of the target. Simulation results in a realistic shallow-water channel are presented as well as results using the SWellEx96 S59 data with an injected target to show that the proposed WAS beamformer outperforms conventional and minimum variance adaptive beamformers in a shallow-water scenario.

Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids

Low Carbon Technologies (LCTs), such as Photovoltaics (PVs), Electric Vehicles (EVs), and Heat Pumps (HPs), are expected to cause a huge electric load in future distribution grids. This paper investigates the grid impact in terms of over-loading and nodal voltage deviations in different distribution grids due to increasing LCT penetrations. The major objectives are the identification of the most severe LCT, grid impact issue, seasonal effect, and vulnerable distributional area, considering the physical models of the LCTs. It is concluded that Winter is the most hazardous for the future grid impact, characterized by nearly 3 times higher over-loading and 2.5 times higher voltage deviations during high HP penetrations, while suburban areas are the most vulnerable. Moreover, while HPs seem to have, in general, a greater impact compared to EVs, EVs cause more prolonged violations. While this work follows a bottom-up approach, using detailed physical models, aggregated national data has also been acquired, which is often used by top-down approaches. Different grid impact issues have been compared for the two approaches in terms of magnitude and duration. While bottom-up approaches generate more pessimistic results regarding the magnitude of the violations, results about the duration of the violations can be contradictory.

Characterization of charge spreading and gain of encapsulated resistive Micromegas detectors for the upgrade of the T2K Near Detector Time Projection Chambers

An upgrade of the near detector of the T2K long baseline neutrino oscillation experiment is currently being conducted. This upgrade will include two new Time Projection Chambers, each equipped with 16 charge readout resistive Micromegas modules.A procedure to validate the performance of the detectors at different stages of production has been developed and implemented to ensure a proper and reliable operation of the detectors once installed. A dedicated X-ray test bench is used to characterize the detectors by scanning each pad individually and to precisely measure the uniformity of the gain and the deposited energy resolution over the pad plane. An energy resolution of about 10% is obtained.A detailed physical model has been developed to describe the charge dispersion phenomena in the resistive Micromegas anode. The detailed physical description includes initial ionization, electron drift, diffusion effects and the readout electronics effects. The model provides an excellent characterization of the charge spreading of the experimental measurements and allowed the simultaneous extraction of gain and charge spreading information of the modules.