Detailed Simulation Model Research Articles

A Study on Cyclical Learning Rates in Reinforcement Learning and Its Application to Temperature and Power Consumption Control of Refrigeration System

In recent years, with the advancement of computer hardware technology, an increasing number of complex control systems have begun employing reinforcement learning over traditional PID controls to address the challenge of managing multiple outputs simultaneously. In this study, we have for the first time adopted the cyclical learning rate method, which is widely used in deep learning, and applied it to deep reinforcement learning. Utilizing MATLAB Simulink, a detailed simulation model was developed with the RSD-4TFK5J model refrigeration storage as the reference object. We precisely evaluated the effects of various cyclical learning rate strategies on the training process of the model. The simulation results demonstrate the effectiveness of the cyclical learning rate method during the training phase, showcasing its potential to enhance learning efficiency and system performance in complex control environments.

How to use the impossible map – Considerations for a rigorous exploration of Digital Twins of the Earth

The term “Digital Twins of the Earth” has rocketed into scientific use and policymaker discourse by promising a virtual replica of our planet. While the potential of a digital representation of reality is captivating for environmental monitoring, decision-making, and scientific inquiry, the term lacks a clear and shared definition and may be misleading. It conceals that all digital representations are models and, as such, will always be detached from reality. Detailed simulation models are excellent digital laboratories that allow us to interrogate our theories about the world in ways otherwise not possible, given the limited scales at which we can run real-world experiments, yet a perfect representation of reality is impossible as it would be exactly as complex. As we embark on the journey of building such detailed models, one question we must ask is, "How can we ensure that they can be explored with scientific rigor?" Here, we discuss possible ways to utilize a model's internal variability to understand its dominant controls to increase our understanding of both the models we build and the world that they represent.

Environmental Impact of Electrification on Local Public Transport: Preliminary Study

The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares emissions from a diesel bus and an electric bus on a specific route in a small town in central Italy. Key findings reveal that electric buses significantly reduce local exhaust emissions but are not entirely emission-free, considering the full life cycle, including electricity generation. The Well-to-Wheel analysis shows lower CO2 emissions for the electric bus compared with the diesel bus, with a substantial part of the emissions occurring at power generation facilities. Non-exhaust emissions, especially Total Suspended Particles, are similar for both bus types. This study highlights the advantages of adopting electric buses in urban areas to decrease local air pollution and greenhouse gas emissions. However, it also underscores the importance of cleaner electricity generation methods to fully leverage the environmental benefits of electric vehicles. The findings provide valuable insights for decision makers and urban planners in developing sustainable urban transportation systems.

The Application of PID Control in DC Motor Control Systems

Abstract. The application of Proportional-Integral-Derivative (PID) control in direct current (DC) motor control systems is essential for achieving high levels of precision and reliability in both industrial and robotic applications. As DC motors are widely utilized in environments requiring accurate speed and torque control, this study seeks to address common challenges such as overshoot, instability, and sluggish response times that can compromise performance. The research is centered on the development and implementation of a PID-based control strategy using MATLAB/Simulink as the primary tool for simulation and testing. The study involves detailed simulation models and experimental validation, using data collected from controlled lab environments to evaluate the effectiveness of various tuning methods. These methods include adjusting the proportional, integral, and derivative gains to optimize motor responsiveness and system stability. The results indicate that with appropriate tuning, PID controllers can significantly enhance DC motor performance, leading to reduced overshoot, faster response times, and greater overall system reliability. The research concludes that adaptive PID control strategies, which dynamically adjust parameters in response to changing operating conditions, provide a robust solution for improving DC motor control in complex, dynamic, and nonlinear environments. This study lays the groundwork for future innovations in automation and precision-driven industries, offering insights that could be applied across a wide range of applications.

The Transition from Aerosol- to Updraft-Limited Susceptibility Regime in Large-Eddy Simulations with Bulk Microphysics

Large-eddy simulation (LES) is often used as a benchmark simulation in climate science and is suggested as a fundamental tool to examine, e.g., marine cloud brightening. Therefore, it is necessary to critically evaluate if these high-resolution models can skillfully simulate expected physical phenomena. This study focuses on the first indirect aerosol effect in warm stratocumulus clouds. We investigate if an LES code with explicit aerosol-cloud interactions and a widely used two-moment bulk microphysical scheme can reproduce well-known cloud droplet number susceptibility regimes previously identified by observations and supported by detailed parcel model simulations—the updraft-limited regime (typically occurring at high aerosol number concentrations and low updraft speeds) and the aerosol-limited regime (typically occurring at low aerosol number concentrations and high updraft speeds). Our simulations show that the LES in its default configuration cannot reproduce the two regimes if the initial droplet radius of newly activated droplets (rid) is estimated by integrating the wet aerosol size distribution. The main reason is related to the relatively coarse (but commonly used) time step in the model (Δt ≈ 1s), which is too long to resolve relevant microphysical processes adequately at high aerosol concentrations. A regime transition does occur if the timestep is decreased to Δt ≈ 0.1s and if a renormalization procedure is applied, which limits the number of activated droplets so that the water mass of the newly activated droplets cannot exceed the available amount of supersaturated water vapor. Another way to obtain a regime transition is to increase rid to values >1 µm. However, a clear recommendation for the choice of rid cannot be made upon physical arguments. An alternative solution could be to introduce a sub-time-stepping or adaptive time-stepping algorithm to calculate droplet formation and growth, particularly for updraft-limited conditions. Our study highlights the importance of critically evaluating LES results to guarantee that relevant physical processes are properly represented.

The impact of various curing methods on the curing process of polymer-fiber composites analyzed using analytical approach

Ti/Gf/Cf represents a novel form of hybrid fiber metal laminates (HFMLs) that successfully blend the benefits of titanium alloy, glass fiber reinforced plastic (GFRP), and carbon fiber reinforced plastic (CFRP). These materials have garnered attention in the field of engineering due to their exceptional levels of stiffness, strength, and shear resistance. The quality of HFMLs during the curing process was significantly impacted by the chosen curing method. Moreover, the use of multiple types of fibers complicated the optimization of curing parameters in HFMLs. A comprehensive study was conducted using a multi-physics field coupling finite element method to investigate the impact of various curing methods on the development of temperature distribution, curing degree, and curing stress in HFMLs during the curing process. By integrating thermal transmission equations, curing kinetics, and viscoelastic mechanical models, a detailed curing simulation model was established. Increasing the cooling rate from 1 °C to 5 °C was shown to raise the maximum curing stress by 4.7 MPa. Additionally, higher curing temperatures were found to induce internal curing stress due to variations in thermal properties. Insufficient insulation times were observed to exacerbate non-uniform temperature distribution and curing degree fields in HFMLs, potentially leading to increased internal stress. To mitigate the generation of large residual stress, it is recommended to lower the heating and cooling rates, reduce curing temperatures, and extend the insulation time appropriately during the curing process of HFMLs. The primary goal of this research was to accurately predict the evolution of curing parameters in HFMLs under different curing regimes, which were impacted by the chosen curing method. The results of the study demonstrated that rapid changes in temperature during heating and cooling stages can lead to uneven curing, resulting in the accumulation of residual stress within the laminates.

An individual-based dynamic model to assess interventions to mitigate opioid overdose risk

BackgroundIllicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto.MethodsAn individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events.ResultsModel simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses.ConclusionsWithin a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Optimizing the fleet of emergency medical service vehicles

This study addresses the problem of optimizing composition and distribution of an ambulance fleet within an existing emergency medical system. The primary objective is to formulate a strategy aimed at minimizing response times and enhancing the system's responsiveness, particularly to critically ill patients. The optimal spatial distribution of various types of vehicles is proposed using a hierarchical p-median model. The effects of these strategies are evaluated through a detailed computer simulation model. Experimental studies have shown that optimizing station locations contributes more significantly than altering the fleet composition and associated dispatching protocols. In the context of the Slovak Republic, the proportion of high-priority incidents responded to within 8 minutes could be increased by 8.7% in urban areas and 10.5% in rural areas, solely through optimal deployment of currently active ambulances. An additional improvement of nearly 2% could be achieved by introducing 13 patient transport ambulances, optimally distributed throughout the country.

Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

Evaluating the Advantages and Disadvantages of Transition Technologies for IPv6 in the Context of IPv4-as-a-Service

The shift towards IPv6 has led to the emergence of various IPv4-as-a-Service (IPv4aaS) transition technologies that allow the provisioning of IPv4 connectivity over IPv6 networks. The six main IPv4aaS mechanisms – 464XLAT, DS-Lite, Lightweight 4over6, MAP-E, MAP-T, and D4across6 – are compared in this study. We assess these protocols’ performance under a range of network situations using detailed simulation modeling in the ns-3 network simulator. With 100 nodes, we simulate an ISP-scale network architecture. We then configure several routing protocols, traffic patterns, connection delays, and implement each protocol according to its request for proposals. Different random seeds are used for each simulation run, and variables like packet sizes, routing protocols, and traffic loads are varied. The key performance parameters that are gathered are protocol overhead, packet loss, delay, and throughput. Important insights into the workings of each mechanism and how they tackle the problems of coexisting IPv4 and IPv6 under various network circumstances are provided by our thorough examination. The findings demonstrate how much better 464XLAT and D4across6 perform in most situations in terms of low latency, high throughput, minimal packet loss, and low overhead. This site may help network operators locate IPv4aaS solutions that meet their requirements and implementation procedures. Networking must alter transition technologies. This study improves comprehension of IPv4aaS protocols via trustworthy simulations.

A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence

While comprehensive knowledge networks can be instrumental in finding solutions to complex problems or supporting the development of detailed simulation models, their large number of nodes and edges can become a hindrance. When the representation of a network becomes opaque, they stop fulfilling their role as a shared representation of a system between participants and modelers; hence, participants are less engaged in the model-building process. Combating the information overload created by large conceptual models is not merely a matter of changing formats: shifting from an unwieldy diagram to enormous amounts of text does not promote engagement. Rather, we posit that participants need an environment that provides details on demand and where interactions with a model rely primarily on a familiar format (i.e., text). In this study, we developed a visual analytics environment where linked visualizations allow participants to interact with large conceptual models, as shown in a case study with hundreds of nodes and almost a thousand relationships. Our environment leverages several advances in generative AI to automatically transform (i) a conceptual model into detailed paragraphs, (ii) detailed text into an executive summary of a model, (iii) prompts about the model into a safe version that avoids sensitive topics, and (iv) a description of the model into a complementary illustration. By releasing our work open source along with a video of our case study, we encourage other modelers to use this approach with their participants. Their feedback and future usability studies are key to respond to the needs of participants by improving our environment given individual preferences, models, and application domains.

Multi-objective optimisation framework for Blue-Green Infrastructure placement using detailed flood model

Designing city-scale Blue-Green Infrastructure (BGI) for flood risk management requires detailed and robust methods. This is due to the complex interaction of flow pathways and the need to assess cost-benefit trade-offs for various BGI options. This study aims to find a cost-effective BGI placement scheme by developing an improved approach called the Cost OptimisatioN Framework for Implementing blue-Green infrastructURE (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated Sorting Genetic Algorithm II). The use of a high-resolution flood simulation model ensures the explicit representation of BGI and other land use features to simulate flow pathways and surface flood risk accurately, while the optimisation algorithm guarantees achieving the best cost-benefit trade-offs for given BGI options. The current study uses the advanced CityCAT hydrodynamic flood model to evaluate the efficiency of the optimisation framework and the impact of location and size of permeable interventions on the optimisation process and subsequent cost-benefit trade-offs. This is achieved by dividing permeable surface areas into intervention zones of varying size and quantity. Furthermore, rainstorm events with 100-year and 30-year return periods are analysed to identify any common optimal solutions for different rainfall intensities. Depending on the number of intervention locations, the automated framework reliably achieves optimal BGI implementation solutions in a fraction of the time required to find the best solutions by trialling all possible options. Designing and optimising interventions with smaller sizes but many permeable zones save a good fraction of investment. However, such a design scheme requires more computational time to find optimal options. Furthermore, the optimal spatial configuration of BGI varies with different rainstorm severities, suggesting a need for careful selection of the rainstorm return period. Based on the results, CONFIGURE shows promise in devising sustainable urban flood risk management designs.

Flow Simulation-Based Methodology for Reducing The Risk of Fuel Fire In An Aircraft’s Fuel System Enclosure

ABSTRACT This study applies advanced computational fluid dynamics (CFD) simulations to enhance the safety of an aircraft’s fuel system by striving to minimize the risk of on-board fire hazards. Employing a detailed flow simulation model, this research assesses the efficacy of newly designed measures within a fuel system, designed for a modified radial engine on a test-bed AN 2 airplane. The model simulates internal airflow, droplet particle flow, the formation of Eulerian Wall Film, and the vaporization process from the fuel film within the enclosure of the aircraft’s equipment bay. By exploring both the original and modified geometries of this fuel system enclosure, the simulations provide insights into the flowpaths of leaking fuel, the spatial and temporal distribution of fuel vapor concentrations, and the overall effectiveness of design modifications aimed at rapid removal of hazardous substances. Structural improvements, including the addition of strategic ventilation inlets and outlets, are proposed based on the simulation results to ensure rapid dispersion of vapors and minimal residual fuel, effectively reducing the potential for vapor ignition. This study thus underscores the potential of precise CFD modelling in identifying risks and developing robust fire hazard mitigation strategies in aviation fuel systems.

Adaptive non-invasive ventilation treatment for sleep apnea.

The purpose of this study was to investigate the effectiveness of two non-invasive mechanical ventilation (NIV) modalities to treat sleep apnea: (1) Average Volume Assured Pressure Support (AVAPS) NIV, and (2) Pressure Support (PS) NIV with Continuously Calculated Average Required Ventilation (CCARV). Two detailed (previously developed and tested) simulation models were used to assess the effectiveness of the NIV modalities. One simulated subjects without chronic obstructive pulmonary disease (COPD), and the other simulated patients with COPD. Sleep apnea was simulated in each model (COPD and Non-COPD), and the ability of each NIV modality to normalize breathing was measured. In both NIV modalities, a low level continuous positive airway pressure was used and a backup respiratory rate was added to the algorithm in order to minimize the respiratory work rate. Both modalities could help normalize breathing in response to an episode of sleep apnea within about 5 min (during which time blood gases were within safe limits). AVAPS NIV and PS NIV with CCARV have potential value to be used for treatment of sleep apnea. Clinical evaluations are needed to fully assess the effectiveness of these NIV modalities.

Adaptive impact analysis in flexible multibody systems based on hierarchically refined IGA models

Usually, detailed impact simulation models within flexible multibody systems have to be set up manually rather than being generated automatically. This is because the process requires prior knowledge of the time and location of the impact, as well as the element resolution within the contact area. If the penalty method is used to determine the occurring contact forces, the corresponding penalty factor also needs to be determined manually. This work, however, presents an adaptive algorithm to simulate impacts within flexible multibody systems fully automatically using reduced isogeometric analysis models, the floating frame of reference formulation, and quasistatic contact models for an efficient but still accurate simulation. The adaptive algorithm detects impacts in the system, determines the contact locations on the bodies, refines the contact area, and determines the penalty factor, and therefore automatically simulates impacts. The work shows how to automatically simulate impacts in flexible multibody systems without user action or prior knowledge of impact location and size. The first application example simulates significant elastodynamic effects within a long flexible rod. The goal is to validate the algorithm by preserving the wave propagation and energy of the system. The second application example simulates the impacts of two flexible double pendulums. This setup is a suitable benchmark for the complete adaptive impact analysis procedure as the flexible double pendulums undergo large rigid body motions.

Experimental and simulation investigation for prismatic lithium-ion battery cells under impact loading

In the current work, prismatic lithium-ion battery (LIB) cells were impacted in various rigid cylinder loading speeds (v = 1, 5, 10, 2000 and 5000 mm/s), which provided the data basis for establishing a practical and reasonable LIB cell damage assessment method. Based on thermal-runaway cell safety borders (TCSB) and undamaged cell safe borders (UCSB), the deformed cells can be classified into safe, risk and failure regions. With the increment of the v values, the UCSB and TCSB of cells decreased linearly along a logarithm axis. Moreover, the relationships between key cell parameters (cell thickness, separator thickness and separator fracture strain) and the cell deformation limits (DL) under rigid cylinder impact loads were also revealed. A DL prediction method was newly introduced for the prismatic LIB cells with different sizes. Finally, the tensile mechanical data of cell components (anodes, cathodes and separators) were also measured for cell local detailed simulation models. By the simulations, the effect of the frictional behaviors between cell components on the deformation processes and failure mechanisms of LIB cells were deeply discussed.

Dynamic Modeling and Simulation of a Facade-Integrated Adsorption System for Solar Cooling of Lightweight Buildings

Reductions of carbon dioxide emissions from the building sector are mandatory for climate protection. This calls for both a reduction of the construction material and energy as well as a reduction of the operational energy. Against this background, a novel facade-integrated adsorption system for solar cooling of lightweight buildings is proposed and theoretically investigated in this work. For this purpose, a detailed simulation model is developed to analyze both the processes in the single components as well as the overall system behavior. The proposed system consists of the three components adsorber, condenser and evaporator, which are connected vacuum-tight and are coupled by vapor transfer. The simulation results of a defined reference case yield cooling rates of 54 W per installed square meter of adsorber facade. The cooling power can be maintained for 12 h, confirming the applicability of the proposed system. Furthermore, a comprehensive parametric study is carried out in order to identify an optimum set of parameter values for maximum cooling rate under the assumed conditions. The results reveal that controlled constant cooling rates of 105 W per square meter of adsorber facade can be reached and a maximum peak power of 145 W per square meter of adsorber facade is possible.

Construction and application of online parametric modeling platform for face milling cutters based on WebGL and Open CASCADE

The secondary development of traditional commercial CAD/CAE software has been widely used in the process of cutting tools design and optimization. However, limited by copyright and hardware configuration, the effect of this development is limited, and the secondary development can no longer meet the needs of rapid and accurate cutting tools design. With the rapid development of Internet, online cutting tools CAD software based on Web has become a new development direction with its characteristics of specialization and lightweight. In order to build an online cutting tools CAD software, an online parametric design platform for indexable face milling cutters is established based on Web development technology, Open CASCADE and Three.js. The platform can quickly generate a 3D basic model for NC machining programing and a 3D detailed model for finite element simulation of cutting process according to ISO 13399. Based on the built platform, the finite element simulation model of cutting process of indexable surface milling cutter is established. The establishment of the model can play an important role in improving the design efficiency and reducing the design cost for the optimal design of this kind of milling cutter.

Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

The intermittency and uncertain forecasts of solar irradiation complicate the operation of a solar thermal plant with thermal storage for heat production. In this work, a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology is proposed to determine the economic optimal operation of a non-concentrating solar thermal plant for low temperature heat production, using a planning phase to improve storage management. The methodology is tested online on a detailed simulation model representing a large-scale solar thermal plant in case studies, using variable heat demand and real data for the weather forecasts and measurements. It was shown that DRTO performs better than offline Dynamic Optimization thanks to the use of updated weather forecasts and the regular re-initialization of the system state using measurements. An increase up to 57% in supplied energy and a decrease up to 35% in operating costs were achieved with DRTO compared to DO. Guidelines on the best storage management policy at the DRTO level, using the planning phase, are formulated. While storing the maximum energy possible for later use is the best option when overheating is not a risk, following the planned storage state helps to prevent overheating when the solar irradiation is high and the heat demand low.

Adaptive inter-area power oscillation damping from offshore wind farm and MMC-HVDC using deep reinforcement learning

The coordination of the offshore wind farm (OWF) and high-voltage direct current (HVDC) links to provide ancillary power oscillation damping (POD) services may become a mandatory requirement from the transmission system operators in the near future. However, the performances of the POD controllers (PODCs) are vulnerable to the power system uncertainties and random communication delays of the wide-area signals. To address these issues, this paper proposes the design of the coordinated PODCs by using the deep reinforcement learning (DRL) method. The DRL-based PODCs employ one of the state-of-the-art DRL algorithms, proximal policy optimization, which can learn to adapt to the system uncertainties and time-varying communication delays by interacting with the power system continuously. A detailed simulation model of an OWF connected to the IEEE-39 bus AC grid via the modular multilevel converter based HVDC link has been built as a simulation platform, and the efficacy of the proposed DRL-based PODCs has been validated across a broad spectrum of operating conditions, disturbances, and communication latency.