Simulation Modeling Framework Research Articles

Development of a multi-layer green-ampt infiltration based modeling framework for hydrologic process simulation and design of various low impact development measures for mitigating urban flooding

ABSTRACT Low Impact Development (LID) measures have proven to be effective in reducing the runoff volume and the peak of runoff at small spatial scales. Assessment of the hydrological impacts of LID measures at river basin scales is currently limited by the absence of adequate physically-based process models that can simulate LID processes at such scales. In this study, an attempt has been made to develop LID modules to simulate the water balance and hydrological processes based on the Multi-Layer Green-Ampt (MLGA) infiltration method, which is an advancement to the existing LID modeling techniques. The performance of the MLGA-based LID modules was benchmarked with the process-intensive numerical tool HYDRUS 1D for about 5000 soil samples across the soil textural classes. The results indicated that the MLGA-based model simulates the hydrological processes in close agreement with HYDRUS for soils with moderate-to-high hydraulic conductivity values, which are amenable for LID implementation.

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds

Abstract. Previous studies have found that low-level Arctic clouds often persist for long periods even in the face of very low surface cloud condensation nuclei (CCN) concentrations. Here, we investigate whether these conditions could occur due to continuous entrainment of aerosol particles from the free troposphere (FT). We use an idealized large eddy simulation (LES) modeling framework, where aerosol concentrations are low in the boundary layer (BL) but increased up to 50× in the free troposphere. We find that the tests with higher tropospheric aerosol concentrations simulated clouds, which persisted for longer and maintained higher liquid water paths (LWPs). This is due to direct entrainment of the tropospheric aerosol into the cloud layer, which results in a precipitation suppression from the increase in cloud droplet number and in stronger cloud-top radiative cooling, which causes stronger circulations maintaining the cloud in the absence of surface forcing. Together, these two responses result in a more well-mixed boundary layer with a top that remains in contact with the tropospheric aerosol reservoir and can maintain entrainment of those aerosol particles. The surface aerosol concentrations, however, remained low in all simulations. The free-tropospheric aerosol concentration necessary to maintain the clouds is consistent with concentrations that are frequently seen in observations.

Artificial neural network-based home energy management system for smart homes

<p>Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.<strong></strong></p>

Multi-Agent Multimodal Transportation Simulation for Mega-cities: Application of Los Angeles

This paper presents a comprehensive modeling framework for large-scale agent-based multimodal transportation simulation tailored to the complex urban environments of megacities, with a specific focus on the Greater Los Angeles (LA) area. The framework leverages the Multi-Agent Transport Simulation (MATSim) toolkit, which addresses the evolving challenges posed by urbanization and technology-driven shifts in human mobility patterns. The framework enables the generation of explicit travel trajectories for a representative 10% sample population of LA County, comprising over one million individuals. To ensure the model’s validity, an efficient network calibration algorithm is proposed with rigorous validation using publicly available traffic count data. This paper underscores the development and configuration of the agent-based simulation model for LA, providing a robust foundation for large-scale urban mobility simulations and advancing our understanding of the intricacies of megacities.

Investigation of modeling challenges of PEM fuel cells cold start operation

In this study a one-dimensional transient isothermal model was implemented in MATLAB to conduct an in-depth evaluation of available modeling framework for cold start simulation of PEMFC. In this regard, several shortcomings were addressed and a combination of modifications introduced by other researchers and assumptions made in this study was used to improve modeling accuracy; reaction kinetics variation with temperature and ionomer water content was investigated and incorporated in the model; dynamics of nucleation and subcooling was described by Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Classical Nucleation theories; effect of temperature on ECSA loss was examined and assumptions were made regarding electro-osmotic drag in catalyst layer under low load conditions. The results showed high fidelity to experiments in the early stage of cold start and the model was able to capture the effect of all three influential parameters, i.e. temperature, initial ionomer water content, and current density on performance evolution and failing time in isothermal cold starts with acceptable accuracy. Moreover, the linear performance drop at low subcooling and the nonlinear relation between cold start duration and initial ionomer water content were properly reproduced by the model.

Development of a component-based integrated modeling framework for urban flood simulation

Development of a component-based integrated modeling framework for urban flood simulation

LES investigation of soot formation in a turbulent non-premixed jet flame with sectional method and FGM chemistry

This study proposes a detailed soot modeling framework for large-eddy simulation (LES) to accurately predict soot formation and particle size distributions (PSD) in turbulent reacting flows. The framework incorporates Flamelet Generated Manifold (FGM) chemistry and a soot model based on the discrete sectional method (DSM) to predict both qualitative and quantitative sooting behavior while keeping the computational cost affordable. Two elementary modeling strategies are considered in the LES formalism for describing soot formation rates. These strategies rely on an a-priori tabulation of soot formation rates and their run-time computation. The LES formalism is applied to the simulations of a well-characterized, non-premixed, turbulent jet flame. A comparative analysis of strategies employed for filtered soot source term treatment is conducted to investigate their impact on the prediction of soot quantities and the evolution of soot PSDs. The LES results for the gas phase and soot phase are compared against the available experimental data. A good prediction of soot evolution is achieved with the two methodologies. The tabulation of soot formation rates leads to a significant reduction in computational cost compared to the model based on their explicit runtime computation. The LES results reveal that the modeling of filtered soot source terms has a significant impact on the quantitative prediction of soot formation. The possible reasons for the observed differences in the soot prediction are discussed. The run-time computation-based model provides a more consistent treatment of the non-linear interactions between the gas and soot phases in soot source terms compared to the tabulated soot chemistry approach. On the other hand, the tabulated soot chemistry model is an interesting and efficient modeling approach for predicting soot formation in turbulent conditions. Overall, both approaches have their strengths and limitations, and the choice of approach may depend on the specific needs of the application.

Комбинированная модель тепломассообмена в башенных градирнях

The priority directions of the scientific and technological policy of the Russian Federation include issues on research in the field of energy and resource saving technologies while generating heat and electric energy. One of the possible ways to implement it at thermal and nuclear power plants is to increase the efficiency of the operation of circulating cooling systems (COO). Modeling and optimization of circulating cooling systems with tower cooling towers is of particular relevance under modern conditions of a limited amount of low-mineralized water for charging circulating cooling systems. To determine the parameters of air flow in the tower cooling tower, a three-dimensional simulation model is developed. To describe heat and mass transfer, considering the phase transitions in coolants, a matrix model is used. The model is developed based on the mass and energy balance equations. A combined model of a circulating cooling system with cooling towers has been developed. It describes air flow within the framework of a three-dimensional simulation model and the process of heat and mass transfer, considering a possible phase transition in coolants within the framework of a matrix model. Comparison of modeling results and normative data is carried out. Adequate description of the real-life process using the combined model is presented. The developed combined model allows setting and solving problems of choosing the optimal design and operating parameters of the COO equipment, as well as solving problems of diagnosing the state of the COO according to the readings of standard instruments. In the future, the proposed calculation method will allow optimizing the operating modes of combined water supply systems, which combine water cooling in a direct-flow scheme and water cooling in a reverse scheme with cooling towers.

Hybrid Methodology for Parametrisation of Proton Exchange Membrane Fuel Cell Model for Diagnostics and Control Applications

Abstract Electrochemical impedance spectroscopy (EIS) is a very powerful tool for the diagnosis and characterisation of fuell cells (FC). However, there is still a lack of physico-chemically consistent models that include parameters with a clear physical meaning and can be related to intrinsic parameters of FC. To fill this knowledge gap, this paper presents a novel, mechanistically-based and computationally-efficient FC modeling framework for time and frequency domain simulations. Furthermore, the model consistently handles forward and backward reactions, ensuring its validity at all current densities. These features enable the development of a hybrid methodology for parameterising the FC model in both domains, resulting in unprecedented accuracy in determining the internal states around which the EIS perturbation is applied. Furthermore, innovative modeling framework incorporates a 1D analytical solution of FC impedance that for the first time accounts for both electrodes, the membrane and individual effects of the electrodes coupled to the respective GDL and channel, all significantly impacting the accuracy of the model. This was confirmed by state-of-the-art reproduction of experimental data with R$^2$ values exceeding 0.965 for data not used in the parameterisation. The presented modeling framework thus provides a modelling basis for observer functionalities beyond the state-of-the-art.

A modelling framework for modern heritage buildings energy simulation

The latest European polices highlight the urgent need to rehabilitate the existing building stock, responsible for 40 % of the EU's total energy consumption. In this process, a key role is played by thermal simulations, assessing the effective energy performances. However, significant discrepancies between real and simulated consumptions are frequently outlined. Inaccurate results are particularly dangerous for modern buildings, which, rarely protected, are often altered by invasive retrofitting solutions, with little regard for their heritage and cultural value. This paper introduces a comprehensive framework for the building energy simulation, ensuring the necessary model credibility. It consists of dynamic modelling, calibration and validation, enhancing the usefulness of the final results. A validated model is in fact the premise to propose a well-balanced retrofitting scenario, improving the current energy performances, reducing the operational costs, and preserving the historical values of existing buildings. As operative case-study, Chauderon administrative building in Lausanne (1969–1974), designed by the Atelier AAA in collaboration with Jean Prouvé, has been selected. Today, the complex is well-preserved in its original materiality and represents an iconic example of the modern aesthetics, with an expressed need for retrofitting. Following the proposed framework, a reliable model in WUFIplus has been created and validated according to ASHRAE 14, allowing to reliably test the efficacy of future retrofitting scenarios. The final aim of this process is to minimize the risk of inappropriate interventions. For high-quality or recognized post-World War II building stock, the use of a calibrated and validated model is justified and recommended over the static or simplified modelling approaches still commonly employed today.

Differentiable hybrid neural modeling for fluid-structure interaction

Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics based solvers are inadequate to handle the increasing demand for large-scale and long-period simulations. The ever-increasing availability of data and rapid advancement in deep learning (DL) have opened new avenues to tackle these challenges through data-enabled modeling. The seamless integration of DL and classic numerical techniques through the differentiable programming framework can significantly improve data-driven modeling performance. In this study, we propose a differentiable hybrid neural modeling framework for efficient simulation of FSI problems, where the numerically discretized FSI physics based on the immersed boundary method is seamlessly integrated with sequential neural networks using differentiable programming. All modules are programmed in JAX, where automatic differentiation enables gradient back-propagation over the entire model rollout trajectory, allowing the hybrid neural FSI model to be trained as a whole in an end-to-end, sequence-to-sequence manner. Through several FSI benchmark cases, we demonstrate the merit and capability of the proposed method in modeling FSI dynamics for both rigid and flexible bodies. The proposed model has also demonstrated its superiority over baseline purely data-driven neural models, weakly-coupled hybrid neural models, and purely numerical FSI solvers in terms of accuracy, robustness, and generalizability.

An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska

The availability of supercomputers and state-of-science datasets has made it possible to conduct large-scale land simulations at an ultrahigh-resolution. This study reported a computational framework for land surface simulation using the E3SM land model (ELM) at an unprecedented resolution (1 km x 1 km gridcell). The ultrahigh-resolution ELM (uELM) simulation framework includes three parts: (1) high-resolution atmospheric forcing and surface properties dataset generation, (2) massive gridcell-based simulation, and (3) large-scale simulation results analysis. We implemented the uELM simulation framework and completed the first 1 km x 1 km terrestrial ecosystem simulation (from 1850 to 2014) over the Seward Peninsula in Alaska (78,000 km2). The experiment contained two phases: a spin-up simulation and a transient simulation, and required five weeks of calculations using 320 cores in a 44-node Linux HPC computer. It created approximately 1.3 TB of data from the transient simulation alone (1850 - present). We selected sample results (monthly and daily simulation outputs) to illustrate the temporal and spatial variations of several variables in high-latitude Arctic ecosystems’ water, energy, and carbon cycles. At last, we summarized the lessons learned and proposed new developments for full-scale uELM simulations over the entire North American continent (approximately 22,000,000 km2).

Improving volume-averaged simulations of matrix-stabilized combustion through direct X-ray µCT characterization: Application to NH3/H2-air combustion

Porous media combustion (PMC) relies on internal heat recirculation in an open-cell ceramic foam matrix to enhance the flame speed of fuels with poor combustion properties. Volume-averaged simulations are often used to study the combustion performance and pollutant emissions of such systems. However, due to the varying complexity of matrix geometries found in practical burners, as well as the wide range of closure models for the constitutive relations of the solid phase, contradicting statements about the predictive accuracy of these volume-averaged models can be found in the literature. In this work, we propose an open-source modeling framework for accurate volume-averaged PMC simulations by using first-principles methods to determine effective properties used in closure models. This framework relies on adequately characterizing the topology of the solid matrix, using commonly available X-ray computed microtomography. With this approach, significant improvements in accuracy are reported compared to empirical models from the literature. The framework based on first-principle evaluations of constitutive relations is compared against experimental measurements conducted on an interface-stabilized burner operated with premixed NH3/H2-air. The model shows good agreement for exhaust gas composition and stability limits. The proposed simulation framework performs significantly better than state-of-the-art techniques that employ commonly used empirical correlations for effective matrix properties. Statement of SignificanceWe present a new open-source simulation framework for improved characterization of porous media combustion. By utilizing µCT techniques, accurate effective matrix properties can be determined from first-principle simulations. These effective properties are used in closure models for 1D volume-averaged reacting flow simulations using appropriate sub-models for heat recirculation. This modeling framework is able to reliably predict stability limits while conventional closure models yield erroneous trends. Assessment of the resulting modeling framework is performed using experiments with exhaust gas characterization performed on a NH3/H2-air porous media burner.

Functional non-uniformity of periodontal ligaments tunes mechanobiological stimuli across soft- and hard-tissue interfaces

The bone-periodontal ligament-tooth (BPT) complex is a unique mechanosensing soft-/hard-tissue interface, which governs the most rapid bony homeostasis in the body responding to external loadings. While the correlation between such loading and alveolar bone remodelling has been widely recognised, it has remained challenging to investigate the transmitted mechanobiological stimuli across such embedded soft-/hard-tissue interfaces of the BPT complex. Here, we propose a framework combining three distinct bioengineering techniques (i, ii, and iii below) to elucidate the innate functional non-uniformity of the PDL in tuning mechanical stimuli to the surrounding alveolar bone. The biphasic PDL mechanical properties measured via nanoindentation, namely the elastic moduli of fibres and ground substance at the sub-tissue level (i), were used as the input parameters in an image-based constitutive modelling framework for finite element simulation (ii). In tandem with U-net deep learning, the Gaussian mixture method enabled the comparison of 5195 possible pseudo-microstructures versus the innate non-uniformity of the PDL (iii). We found that the balance between hydrostatic pressure in PDL and the strain energy in the alveolar bone was maintained within a specific physiological range. The innate PDL microstructure ensures the transduction of favourable mechanobiological stimuli, thereby governing alveolar bone homeostasis. Our outcomes expand current knowledge of the PDL's mechanobiological roles and the proposed framework can be adopted to a broad range of similar soft-/hard- tissue interfaces, which may impact future tissue engineering, regenerative medicine, and evaluating therapeutic strategies. Statement of significanceA combination of cutting-edge technologies, including dynamic nanomechanical testing, high-resolution image-based modelling and machine learning facilitated computing, was used to elucidate the association between the microstructural non-uniformity and biomechanical competence of periodontal ligaments (PDLs). The innate PDL fibre network regulates mechanobiological stimuli, which govern alveolar bone remodelling, in different tissues across the bone-PDL-tooth (BPT) interfaces. These mechanobiological stimuli within the BPT are tuned within a physiological range by the non-uniform microstructure of PDLs, ensuring functional tissue homeostasis. The proposed framework in this study is also applicable for investigating the structure-function relationship in broader types of fibrous soft-/hard- tissue interfaces.

An integrated agent-based simulation modeling framework for sustainable production of an Agrophotovoltaic system

The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic environmental conditions, it is challenging to design an APV system based on accurate estimation of its performance. To this end, this study aims to introduce an agent-based simulation (ABS) framework integrated with polynomial regression and ridge regression. In particular, two agent types are devised, as follow: (1) The photovoltaic agent calculates electricity produced via PV modules, and estimates its profits; and (2) the crop production agent calculates crop harvests underneath the PV modules, and estimates their profits. To validate the proposed framework, field experiment data with five types of crops (i.e., corn, sesame, soybean, mung bean, and red bean) at the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea with three different shading ratios of 21.3%, 25.6%, and 32.0% have been used. In addition, for the sustainable operation of an APV system in terms of electricity generation as well as crop production, three climate change scenarios based on the shared socioeconomic pathway (SSP) are considered. The proposed framework identifies that the agrophotovoltaic system with 32% sharing ratio increases up to 20% of the total profit of normal farmland. As a result, the proposed framework enables the performance of an APV system under dynamic climate conditions to be accurately estimated, so that APV system designers can utilize it to identify a profitable long-term APV system.

An integrated Monte Carlo track-structure simulation framework for modeling inter and intra-track effects on homogenous chemistry

Objective. The TOPAS-nBio Monte Carlo track structure simulation code, a wrapper of Geant4-DNA, was extended for its use in pulsed and longtime homogeneous chemistry simulations using the Gillespie algorithm. Approach. Three different tests were used to assess the reliability of the implementation and its ability to accurately reproduce published experimental results: (1) a simple model with a known analytical solution, (2) the temporal evolution of chemical yields during the homogeneous chemistry stage, and (3) radiolysis simulations conducted in pure water with dissolved oxygen at concentrations ranging from 10 μM to 1 mM with [H2O2] yields calculated for 100 MeV protons at conventional and FLASH dose rates of 0.286 Gy s−1 and 500 Gy s−1, respectively. Simulated chemical yield results were compared closely with data calculated using the Kinetiscope software which also employs the Gillespie algorithm. Main results. Validation results in the third test agreed with experimental data of similar dose rates and oxygen concentrations within one standard deviation, with a maximum of 1% difference for both conventional and FLASH dose rates. In conclusion, the new implementation of TOPAS-nBio for the homogeneous long time chemistry simulation was capable of recreating the chemical evolution of the reactive intermediates that follow water radiolysis. Significance. Thus, TOPAS-nBio provides a reliable all-in-one chemistry simulation of the physical, physico-chemical, non-homogeneous, and homogeneous chemistry and could be of use for the study of FLASH dose rate effects on radiation chemistry.

A Hybrid Modelling Framework for E-Commerce Supply Chain Simulation: Complex Adaptive Systems Perspective

A Hybrid Modelling Framework for E-Commerce Supply Chain Simulation: Complex Adaptive Systems Perspective

Numerical simulation of ozonation in hollow-fiber membranes for wastewater treatment

In this study, we developed a comprehensive modeling framework for simulation of ozonation process using combination of artificial intelligence and computational fluid dynamics (CFD). The process is carried out in a hollow-fiber membrane contactor in which the concentration data of ozone was obtained by solution of mass transfer equations, and then the results were used for artificial intelligence modeling. We used three different machine learning models to predict concentration of ozone (C) in a system based on its coordinates, i.e., r and z. The models were optimized using the Bat Algorithm (BA) and were trained on a dataset consisting of over 10,000 data points. The three models developed were Support Vector Regression (SVR), Decision Tree Regressor, and Orthogonal Matching Pursuit (OMP). These models were evaluated using three common metrics — Mean Squared Error (MSE), R-squared (R2), and Mean Absolute Error (MAE). Our results indicated that the SVR model overperformed the other two models in terms of all evaluation metrics. Specifically, the SVR model achieved an MSE of 0.003, an R2 of 0.998, and an MAE of 0.046. The Decision Tree Regressor and OMP models achieved less favorable results with MSEs of 0.007 and 0.221, R2 scores of 0.996 and 0.878, and MAEs of 0.056 and 0.359, respectively.

Biophysical Benefits Simulation Modeling Framework for Investments in Nature-Based Solutions in São Paulo, Brazil Water Supply System

In order to understand the hydrological impacts of the nature-based solutions in the Cantareira Water Supply System, this study evaluates six different land cover and land use change scenarios. The first and second consider the restoration of native vegetation in riparian areas, the third prioritizes restoration sites using biophysical characteristics (optimized restoration scenario derived from Resource Investment Optimization System—RIOS), the fourth considers best management practices and the fifth and sixth are hypothetical extreme scenarios converting all pasture to forest and vice versa. Two hydrological models were developed to represent the distributions of water and yields in the study watershed: HEC-HMS and SWAT. Simulation results indicate that when nature-based solutions are implemented, surface runoff is reduced and ambient storage increases during the rainy season (December–March); while the overall flow increases during the dry season (June–September). The combination of specific hydrologic components of RIOS-customized intervention scenario simulation outputs—namely surface flows and groundwater contribution to stream flows—indicate on average 33% increase in the overall water yield, or 206 hm3/year, across the study watershed when comparing against the baseline conditions. In the same modeling scenario, the water storage in the sub-watersheds adjacent to the reservoirs showed an increase of 58% (or 341 hm3/year). The results indicate that adopting NbS in the source watershed can mitigate the impacts of extreme drought conditions and contribute toward building long-term water security.