Dynamic Model Simulation Research Articles

Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient boosting machine (LightGBM), a modern machine-learning algorithm, we present a framework for inferring the density-dependent nuclear symmetry energy from observables in heavy-ion collisions on the event-by-event analysis. The ultrarelativistic quantum molecular dynamics (UrQMD) model simulations are used as training data. The symmetry energy slope parameter extracted with LightGBM event-by-event from test data also by UrQMD has an average spread of approximately 30 MeV from the truth, and is found to be robust against variations in model parameters. In addition, LightGBM can identify features that have the greatest effect on the physics of interest, thereby offering valuable insights. Our study suggests that the present framework can be a powerful tool and may offer a new paradigm to study the underlying physics in heavy-ion collisions.

КЛИМАТООБРАЗУЮЩАЯ РОЛЬ ЭМИССИИ МЕТАНА НА ШЕЛЬФЕ МОРЕЙ ВОСТОЧНОЙ АРКТИКИ

Satellite data on tropospheric methane and dynamical permafrost model simulations were used to analyze the methane emission in the subaquatic permafrost on the East Siberian Arctic shelf. The hypothesis of potential methane release from the dissociation of submarine gas hydrates was studied. It was demonstrated that the methane emission on the Arctic shelf is governed mainly by geological factors and is not related to contemporary climate change. Numerical experiments with the INM-CM48 Earth system model showed that there is no feedback between the methane emission on the shelf and global air temperature. A conceptual model of methane emission on the East Siberian Arctic shelf was developed.

Comparing climate time series – Part 4: Annual cycles

Abstract. This paper derives a test for deciding whether two time series come from the same stochastic model, where the time series contains periodic and serially correlated components. This test is useful for comparing dynamical model simulations to observations. The framework for deriving this test is the same as in the previous three parts: the time series are first fit to separate autoregressive models, and then the hypothesis that their parameters are equal is tested. This paper generalizes the previous tests to a limited class of nonstationary processes, namely, those represented by an autoregressive model with deterministic forcing terms. The statistic for testing differences in parameters can be decomposed into independent terms that quantify differences in noise variance, differences in autoregression parameters, and differences in forcing parameters (e.g., differences in annual cycle forcing). A hierarchical procedure for testing individual terms and quantifying the overall significance level is derived from standard methods. The test is applied to compare observations of the meridional overturning circulation from the RAPID array to Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Most CMIP5 models are inconsistent with observations, with the strongest differences arising from having too little noise variance, though differences in annual cycle forcing also contribute significantly to discrepancies from observations. This appears to be the first use of a rigorous criterion to decide “equality of annual cycles” in regards to all their attributes (e.g., phases, amplitudes, frequencies) while accounting for serial correlations.

Fired Heater Simulation,Modelling and Optimization

In this study a dynamic model Simulation was carried out using ASPEN HYSY for industrial refinery fired heater, it was found that Increasing the number of the tube rows in convection bank from 2 to 3 allows us to recover approximately 5% of the overall efficiency, the duty furnace has increased from 65.9MW to 68.1MW and the fuel flow has increased from 5597kg/h to 5807kg/h moreover Adding more rows has a reverse return as we start to notice increase on the flue gas temperature. Furthermore, sensitivity analysis was conducted with HYSYS to determine one of the most important parameters that affect the performance of the heater based on the data generated from the simulation. MATLAB code was generated for efficiency calculation and for parameter manipulation. well-stirred model was used for mathematical model calculations and for optimization of furnace operation, the models were validated with ASPEN Exchanger Design and Rating

Deep Reinforcement Learning for Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking

Designing controllers for skid-steered wheeled robots is complex due to the interaction of the tires with the ground and wheel slip due to the skid-steer driving mechanism, leading to nonlinear dynamics. Due to the recent success of reinforcement learning algorithms for mobile robot control, the Deep Deterministic Policy Gradients (DDPG) was successfully implemented and an algorithm was designed for continuous control problems. The complex dynamics of the vehicle model were dealt with and the advantages of deep neural networks were leveraged for their generalizability. Reinforcement learning was used to gather information and train the agent in an unsupervised manner. The performance of the trained policy on the six degrees of freedom dynamic model simulation was demonstrated with ground force interactions. The system met the requirement to stay within the distance of half the vehicle width from reference paths.

Longitudinal Trim and Stability Analysis of Generic Air-Breathing Hypersonic Vehicle using Bifurcation Method

The Air-breathing Hypersonic Vehicle (AHV) can meet the futuristic goals of rapid global travel, to Low-Earth Orbit and into space in near-term decades. This paper presents the study of the trim and stability characteristics of the 3DOF longitudinal model of the generic AHV. Longitudinal 3DOF AHV simulation is developed with the aerodynamic model for trim analysis considering the complete flight envelope for Mach number from M=0 to 24. Dynamic model simulation of the 3DOF AHV is performed for the trim conditions and stability of the AHV flight for the complete envelope. Bifurcation Analysis is implemented with the longitudinal 3DOF AHV model for trim analysis and stability for the complete AHV flight envelope.

Solitary Wave Interaction with a Floating Pontoon Based on Boussinesq Model and CFD-Based Simulations

A mathematical model of solitary wave interaction with a pontoon-type rigid floating structure over a flat bottom is formulated based on Boussinesq-type equations under weakly nonlinear dispersive waves. Based on the higher-order Boussinesq equations, the solitary wave equation is derived, and a semi-analytical solution is obtained using the perturbation technique. On the other hand, brief descriptions of the application of wave2Foam and OceanWave3D on the aforementioned problem are presented. The analytical solitary wave profiles in the outer region are compared with Computational Fluid Dynamics (CFD) and OceanWave 3D model simulations in different cases. The comparison shows a good level of agreement between analytical, wave2Foam, and OceanWave3D. In addition, based on the wave2Foam and coupled OceanWave3D model, the horizontal, vertical wave forces, and the pressure distributions around the pontoon are analysed. Further, the effect of the Ursell number, pontoon length, and water depth on the solitary wave profiles are analysed based on the analytical solution. The paper validates each of the three models and performs intercomparison among them to assess their fidelity and computational burden.

Selection of a Potential Synbiotic against Cronobacter sakazakii

Cronobacter sakazakii is an opportunistic foodborne pathogen that can be fatal to infants; it is commonly associated with powdered infant formula due to contamination during manufacturing processes or during preparation in hospitals or homes. This project aimed to select a potential synbiotic, a combination of probiotic strains with a prebiotic product, to inhibit the growth of C. sakazakii in an in vitro dynamic infant gut model (Simulator of the Human Intestinal Microbial Ecosystem). A total of 16 lactic acid bacteria (LAB) were tested for their inhibitory properties against four different C. sakazakii strains by a zone of inhibition test. Lactobacillus and Pediococcus species were able to inhibit the growth (>15-mm inhibition zones) of all C. sakazakii strains tested, and only one strain from the two genera exhibited atypical resistance to tetracycline. All C. sakazakii strains and the selected LAB strains, which inhibited C. sakazakii and did not exhibit atypical antibiotic resistance, were grown in Luria-Bertani or de Man Rogosa Sharpe broth, respectively, containing 1% dextrose or 1% commercial prebiotic (w/v) to compare their ability to metabolize the prebiotic product. Overall, based on the growth inhibition of C. sakazakii, antibiotic susceptibility, and prebiotic metabolism, 6 of the 16 LAB were chosen to be part of a potential synbiotic. This study has provided valuable information that will help with the development of a synbiotic that can be used in powdered infant formula to reduce the potential for C. sakazakii-related illnesses in infants.

System Dynamics Modelling Scenarios for The Import Receiving Infrastructures Capacity Plan & Development in anticipating Future Deficits of Natural Gas Supply in Indonesia.

Indonesia is one amongst the biggest natural gas reserves country in south East Asia, with total reserve of about 142,71 TSCF in 2018. Indonesian demand for energy is also increasing over the time in line with its economic and population growth. The improved of global consciousness toward the sustainable environmental issues, has led in to the choice over an affordable source of energy with a less carbon footprint. On the other hand the natural gas supply and domestic production is depleting. At the current rate of consumption and assuming no new reserves discoveries, sooner or later Indonesia will have to face deficit in gas supply. This study, is first aimed to forecast the Indonesia supply and demand situation of natural gas in years to come, and then performing an analysis and assessment toward the availability and adequacy of the required infrastructures especially receiving terminals facilitating for gas import in anticipation of future deficit of gas supply in Indonesia. From which, it will be further analyzed, the geographical distribution of the demand and then potential short of supply in several regions and areas where the demand are most concentrated. Thus few basis System Dynamics scenarios of public policies were simulated for 30 years running simulation. System Dynamics model simulation has been well known as a powerful tool to represent a complex industrial problems such as national energy systems. The result is unveiled the fact that additional import receiving terminals and storage facility is required at certain areas where the deficits most likely exist, and most of them in fact are in Western Zone of Indonesian.

Using simulation modelling and systems science to help contain COVID-19: A systematic review.

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.

Electric Vehicle Mechanical Transmission System Based on Fractional Differential Equations

Abstract This paper introduces a method for dynamic loading of human-vehicle powertrains during dangerous driving. At the same time, this paper establishes a performance evaluation index of the human-vehicle (electric vehicle) powertrain system based on the second derivative functional. We have analyzed and established the calculation formula of the excitation force of the electric vehicle drive train in unstable driving conditions. We use the numerical solution method to perform the dynamic model simulation calculation on MATLAB. The study found that the high-frequency excitation force increases the amplitude of the acceleration power spectrum of the electric vehicle system. At the same time, the research results prove that the state-space model of the electric vehicle vibration system and the selected parameter values are effective.

A Local Scheme for Numerical Simulation of Multi-dimensional Dynamic Quantum Model: Application to Decision-making

A Local Scheme for Numerical Simulation of Multi-dimensional Dynamic Quantum Model: Application to Decision-making

Dynamic process and model simulation of soil water content during citrus growth period

Dynamic process and model simulation of soil water content during citrus growth period

Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximations

In the past few decades, the development of fluorescent technologies and microscopic techniques has greatly improved scientists' ability to observe real-time single-cell activities. In this paper, we consider the filtering problem associate with these advanced technologies, i.e., how to estimate latent dynamic states of an intracellular multiscale stochastic reaction network from time-course measurements of fluorescent reporters. A good solution to this problem can further improve scientists' ability to extract information about intracellular systems from time-course experiments.A straightforward approach to this filtering problem is to use a particle filter where particles are generated by simulation of the full model and weighted according to observations. However, the exact simulation of the full dynamic model usually takes an impractical amount of computational time and prevents this type of particle filters from being used for real-time applications, such as transcription regulation networks. Inspired by the recent development of hybrid approximations to multiscale chemical reaction networks, we approach the filtering problem in an alternative way. We first prove that accurate solutions to the filtering problem can be constructed by solving the filtering problem for a reduced model that represents the dynamics as a hybrid process. The model reduction is based on exploiting the time-scale separations in the original network and, therefore, can greatly reduce the computational effort required to simulate the dynamics. As a result, we are able to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model. We illustrate the accuracy and the computational efficiency of our approach using several numerical examples.

Implementation of a solar aided refrigration unit for refrigerated trucks employing photovoltaic generators

Solar aided refrigeration system can represent a simple and feasible solution for improving the sustainability of refrigerated transport. The paper presents the design and the performance of an electric powered refrigeration unit integrated with photovoltaic generators installed on top of the refrigerated box of a light truck. The system prototype has been tested in stationary conditions, demonstrating the capacity of the solar system to impact significantly in the net energy balance of the refrigeration unit. The system performance during an urban multi-drop delivery scenario and hot climatic condition are then assessed using a dynamic lumped model simulation. Simulations are also used to discuss the sensitivity to the battery pack capacity size and the shading conditions caused by the urban canyon effects are investigated. Results demonstrate that, on a yearly base at Athens climatic conditions, the solar panels can provide from 65% to 112% of the energy consumed by the cooling unit, depending on the time spent by the truck under direct sunlight. Larger batteries are justified only when long periods of direct light are expected.

What Would Happen to a Fusion Power Plant in the Case of Plasma Disruption? A Dynamic Model Simulation

It is a widespread view in the fusion community that steady-state, water-cooled fusion power plants can utilize the power generation systems of conventional pressurized water reactor (PWR) fission plants as is. However, what would happen to a fusion power plant in the case of plasma disruption? The authors constructed a dynamic simulation model of a water-cooled ceramic breeder blanket fusion power plant model on Modelica language [300.0-MW(electric) electrical output/1138-MW(thermal) fusion output] and evaluated the applicability of a PWR power generation system. Simulation results suggest that while the PWR system would function as intended during steady-state operation, the conventional system may not be able to cope with a sudden loss of energy influx in the event of plasma disruption without modification: The PWR system’s steam generator experienced a water overflow in less than 150 s from the plasma disruption.

Study on the efficiency of the design of the drip irrigation management system using plastics

In modern agriculture, one of the most influential and debatable problems are related to the water management and soil pollution. The water management issue is obviously connected to the usage of the lowest quantity possible, due to the scarce nature of the water, but is also related to cost reasons and profit maximization. The soil is also considered one of the key resources which must be dealt very rationally, due to its structure and complex processes from its function. In this paper we present a model of soil water management system, comprising the irrigation process, that also takes into account one of the main parameters of an irrigation system: the plastic material used for irrigation. The model optimally choses the best suited material and the optimal water content, also taking into account the geographical area of the soil. The main purpose of building the model is to create an optimization environment for the irrigation systems, with both financial and environmental implications. In this paper, we will present some results of the model of a drip irrigation management system. In this matter, we have used two methods based on genetic algorithms and System Dynamic principles that can achieve desired results for the problem proposed. The results of this paper are shown based on simulations of the System Dynamic model.

Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model.

With the acceleration of urbanization, traffic congestion and vehicle exhaust pollution are becoming increasingly serious problems. Focusing on the problem of urban pollution from vehicle exhaust, this study used system dynamics to establish an urban congestion mitigation and emission-reduction management model. Specifically, a nonlinear function that integrates system dynamics and a non-homogeneous discrete grey model (SD-NDGM) was used to construct an algorithm, which improved the accuracy of the model. Thereafter, the mid- and long-term effects of the restriction policy were explored. The main findings from dynamic model simulations were as follows: All types of restrictions alleviated traffic congestion to varying degrees, but “odd and even” restrictions had more obvious effects, with an average annual reduction rate of 43.53% in the number of motor vehicle trips. The driving-restriction policy had a time effect, significantly reducing the number of vehicle trips in the short term. However, it could have negative effects in the long term (e.g., agglomeration effect, emission-reduction paradox), and it does not fundamentally solve traffic and environmental problems. Thus, it could only be used as a phased policy, not a long-term measure. The purchase-restriction policy controlled excessive increases in the number of private cars, but it had little effect in terms of solving environmental problems. Compared with a single policy, the combination of public-transport development and driving-restriction policy not only reduced traffic congestion, air pollution, and air quality health indexes by 29.13%, 52.63%, and 54.63%, respectively, but also improved environmental carrying capacity by 294.26%. A combined approach can therefore be said to have certain benefits for society, health, and the environment.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-022-02319-9.

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Abstract. Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop+SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.