Validation Of Simulation Model Research Articles

A framework for modeling, generating, simulating, and predicting carbon dioxide dispersion indoors using cell-DEVS and deep learning

Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

A general methodology for simulation model validation and model updating of the coordination mechanism with uncertainty

A general methodology for simulation model validation and model updating of the coordination mechanism with uncertainty

Optimizing thermal comfort in physical exercise spaces: A study of spatial and thermal factors

Fitness centers have become famous for maintaining a healthy lifestyle. They require different thermal comfort conditions and higher fresh air supply rates than other indoor spaces. However, even well-designed centers may cause discomfort due to factors such as design decisions, ventilation, overheating, and overcrowding. The standards for fitness centers do not consider these specific requirements sufficiently, so this study focuses on understanding the thermal comfort requirements during physical exercise and evaluating spatial and thermal factors affecting the thermal environment around the body. The study investigated the ceiling height, lateral and frontal distances between machines, and vent locations as spatial factors and inlet temperature and air velocity as thermal factors. A thirty-minute moderate-intensity constant work rate exercise test was conducted in a controlled climatic chamber using a cycle ergometer with six healthy male participants. The experiment conditions were simulated in CFD software using the collected data. Once a validated simulation model was provided, computational models for different environmental and spatial scenarios for the five-person cycling class were generated. Using Taguchi L9 (34) orthogonal arrays method, nine spatial scenarios were simulated with three different thermal operations each. Optimal factor levels were determined by using thermal comfort conditions (based on predicted mean vote) around the body’s thermal plume. The results showed that a ceiling height of 5 m, lateral and frontal distances of 1 m and 0.5 m between machines, and Type 2 (two inlets mounted on the ceiling) ventilation strategy were optimal for achieving better thermal comfort values in a thermal condition of 18 °C and 0.2 m·s−1. The study found that increasing the ceiling height and using cross-positioned vents that project air vertically from the ceiling improved the comfort conditions significantly. It is expected that these criteria, which were determined, compared with the standards and detailed, will contribute to the production processes of comfortable exercise spaces.

A method for developing and validating simulation models for automated storage and retrieval system digital twins

Automated storage and retrieval systems have become increasingly popular in modern supply chains due to their significant advantages over traditional warehousing systems. Due to the high complexity of these systems, simulation approaches can be used to generate accurate performance measures for a specific system configuration. Simulation models are also the cornerstone of digital twins, one of the latest technological innovations that can further improve warehouse operations. Therefore, the aim of this research is to describe an approach for the development of a discrete event simulation model of an automated storage and retrieval system with a perspective towards the implementation of a digital twin. To be consistent with the objectives of the digital twin, the proposed model represents both the physical system and the overarching information technology architecture, such as the warehouse management system and the warehouse control system. In addition, this paper describes a methodology to validate such a simulation model by setting up an experimental campaign based on the principles of design of experiment. The experiments conducted in a logistics laboratory were used to iteratively calibrate the model until its performance accurately reflected the functioning of the real system. The results obtained demonstrate the effectiveness of the proposed method. Finally, this work contributes to the literature on warehouse digital twins by highlighting new variables to be considered when defining travel time models and their stochastic nature.

“Primer shot” fractionation with an early treatment break is theoretically superior to consecutive weekday fractionation schemes for early-stage non-small cell lung cancer

PurposeRadiotherapy is traditionally given in equally spaced weekday fractions. We hypothesize that heterogeneous interfraction intervals can increase radiosensitivity via reoxygenation. Through modeling, we investigate whether this minimizes local failures and toxicity for early-stage non-small cell lung cancer (NSCLC). MethodsPreviously, a tumor dose-response model based on resource competition and cell-cycle-dependent radiosensitivity accurately predicted local failure rates for early-stage NSCLC cohorts. Here, the model mathematically determined non-uniform inter-fraction intervals minimizing local failures at similar normal tissue toxicity risk, i.e., iso-BED3 (iso-NTCP) for fractionation schemes 18Gyx3, 12Gyx4, 10Gyx5, 7.5Gyx8, 5Gyx12, 4Gyx15. Next, we used these optimized schedules to reduce toxicity risk (BED3) while maintaining stable local failures (TCP). ResultsOptimal schedules consistently favored a “primer shot” fraction followed by a 2-week break, allowing tumor reoxygenation. Increasing or decreasing the assumed baseline hypoxia extended or shortened this optimal break by up to one week. Fraction sizes of 7.5 Gy and up required a single primer shot, while smaller fractions needed one or two extra fractions for full reoxygenation. The optimized schedules, versus consecutive weekday fractionation, predicted absolute LF reductions of 4.6%-7.4%, except for the already optimal LF rate seen for 18Gyx3. Primer shot schedules could also reduce BED3 at iso-TCP with the biggest improvements for the shortest schedules (94.6Gy reduction for 18Gyx3). ConclusionA validated simulation model clearly supports non-standard “primer shot” fractionation, reducing the impact of hypoxia-induced radioresistance. A limitation of this study is that primer-shot fractionation is outside prior clinical experience and therefore will require clinical studies for definitive testing.

Towards a resilience assessment framework for the airport passenger terminal operations

Airport terminals can generate and intensify the undesired phenomena that take place on transportation networks in the wake of disruptive events. The more resilient a terminal is, the more easily it will bounce back to the desired state, causing less nuisance to the users. In this study, we expand the state-of-the-art airport terminal resilience assessment through fast-time simulation and tailored resilience metrics. From the point of view of simulation, we establish delay management strategies based on flight schedule alterations as a response to a disruptive event. In the baseline scenario, no disruption is set, whereas passenger arrival to the airport is blocked for 1 or 2 h in the disrupted scenarios. A previously verified and validated simulation model of a Brazilian domestic airport was used to represent the passenger departing flow, encompassing check-in and security screening. From the resilience perspective, a systematic assessment procedure for new resilience metrics is unveiled, providing means for exploring the metrics’ formulations and their relationship to the operational strategies. Following the simulation runs, resilience metrics for individual passenger and aircraft delays were investigated, something yet scarcely explored in the literature. The results show that the proposed metrics deliver significantly different outcomes from those associated with the current literature. Consequently, we can conclude that a resilience assessment concerning the airport terminal that disregards individual delays may be missing important information. The systematic approach we propose is a contribution in that sense, even though this topic requires more research, considering the vast nature of the possible disruptive events and their subsequent operational impacts.

Computational fluid dynamics simulation analysis of the effect of curved rice leaves on the deposition behaviour of droplets

BackgroundAlthough previous studies on the droplet deposition behaviour of rice leaves have modelled the leaves as flat surface structures, their curved surface structures actually have a significant effect on droplet deposition.ResultsIn this paper, the statistical distribution of the coordinate parameters of rice leaves at the elongation stage was determined, computational fluid dynamics (CFD) simulation models of droplet impact on rice leaves with different curvature radii were built, and the effect of leaf curvature radius on the deposition behaviour and spreading diameter of droplets on rice leaves was studied using validated simulation models. The results showed that the average relative errors of the CFD simulation models were in the range of 2.23–9.63%. When the droplets struck the rice leaves at a speed of 4 m/s, the 50 μm droplets did not bounce within the curvature radii of 25–120 cm, the maximum spreading diameters of 200 and 500 μm droplets that just adhered to the leaves were 287 and 772 μm, respectively. The maximum spreading diameters of 50, 200, and 500 μm droplets that just split were 168, 636, and 1411 μm, respectively. As the curvature radii of the leaves increased, the maximum spreading diameter of the droplets gradually decreased, and droplet bouncing was more likely to occur. However, a special case in which no significant change in the maximum spreading diameter arose when 50 μm droplets hit a leaf with a curvature radius exceeding 50 cm.ConclusionSplitting generally occurred for large droplets with a small curvature radius and small tilt angle; bouncing generally occurred for large droplets with a large curvature radius and large tilt angle. When the droplet was small, the deposition behaviour was mostly adhesion. The change in spreading diameter after stabilisation was similar to the change in maximum spreading diameter, where the spreading diameter after stabilisation greatly increased after droplet splitting. This paper serves as a reference for the study of pesticide droplet deposition and its application in rice-plant protection.

Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations.

AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).

Verification and validation of software process simulation models: A systematic mapping study

AbstractSoftware process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta‐model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ( ) failed to mention or elaborate on their V&V process. We systematically summarize and map the state‐of‐the‐art V&V research in software process modeling field to support modelers' practice and improve their V&V process.

Process model design for positive displacement compressors and their experimental validation: Comparison of Optimal Experimental Design and Machine Learning

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Model Calibration, and Validation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector’s heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous.This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression.The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results.

Spatial migration of lattice oxygen for copper-iron based oxygen carriers in chemical looping combustion

Oxygen carrier (OC) is of great importance in chemical looping combustion (CLC) technology. The migration rate of bulk lattice oxygen during reduction process is detrimental in reaction rate. However, the migration path of lattice oxygen is still not clear. In this study, a thermogravimetric analyzer (TGA) was used to investigate the reaction characteristics and kinetic parameters of the OC. The oxygen migration paths of CuO and CuFe2O4 bulk lattices were studied by density functional theory (DFT) in a five-layer (2 × 1) CuO (1 1 1) and nine-layer (1 × 1) CuFe2O4 (1 0 0) plane model. The kinetic parameters, activation energy changes and relative kinetic model of the OC reduction were explored. According to the DFT results, the deeper bulk phase oxygen requires greater more energy toovercome energy barrier for migration. CuO has a lower oxygen release capacity than CuFe2O4 in the early stages, but higher in the later stage. The reaction kinetics study revealed that the reaction mechanism of the reduction of the copper-iron composite OC is a tertiary chemical reaction control mechanism. During the reaction, the activation energy of the OC changes continuously. The macroscopic reaction kinetics agree with the microscopic simulation, proving the simulation model's validity.

Optimal Worker Allocation of Food Manufacturing System using Simulation and Data Envelopment Analysis

This study suggests employing simulation and data envelopment analysis to support decision making for determining the optimal worker allocation for a food manufacturing production line. The simulation model for food production is based on a real-world example from the food industry, focusing on a manufacturing company. The challenge faced by the company involves a constrained number of workers allocated to four processes. The imbalance number of workers in each process will affect the productivity of the company. Through simulation modeling of the actual systems, it was discovered that the filling process presents a bottleneck due to its significantly higher average waiting time compared to the other processes. A valid simulation model was subsequently employed to generate potential improvements. Nineteen improvement models were proposed and assessed based on various selection criteria, including average total production time, average number of entities still in the system, total production, and average resource utilization. Among the nineteen improvement models, Improvement Model 11 (IM11) emerged as the best improvement model. The optimal worker allocation alternative involves assigning two workers to each process, leading to anticipated increases in total production and average resource utilization. Utilizing simulation and data envelopment analysis can help the management of the company to make better decisions in determining the optimal worker allocation.

Simulation and experimental investigation of high-pressure pneumatic pilot-driven on/off valve with high transient performances for compressed air ejection

A high-pressure pneumatic pilot-driven on/off valve (HPPV) that quickly switches an air inlet line on and off was designed to meet the requirements of a compressed air ejection device with high pressure, high flow rate, and continuous ejection. An HPPV multi-physics field simulation model was established using AMESim simulation software based on the bond graph modeling approach that considered mechanical, gas flow, pipe heat exchange, and pilot valve operation, and experiments were conducted to check the simulation model accuracy. The effects of real gas thermodynamics, pipeline heat exchange, and pilot valve operation on the simulation model accuracy and those of different initial parameter conditions on the dynamic characteristics of the HPPV were investigated in the validated simulation model. The results indicated that the tolerance between simulation and experiment was less than 7.3%, so the simulation model was realistic and reliable. In addition, among the different influencing factors, the pipeline heat transfer significantly affected the simulation model accuracy, and the tolerance between the simulation results without considering the pipeline heat exchange and the experimental results exceeded 10%. The real gas thermal effect slightly affected the simulation model accuracy, and the tolerance between the simulation results under the action of each gas state equation was less than 3%. The pilot valve operation affected only the opening and closing moments of the HPPV and had no effect on the simulation model accuracy. The dynamic characteristics of the HPPV were stable and reliable under different initial parameter conditions.

Model validation method by considering uncertainty for numerical simulation

There is a great amount of uncertainties in the whole life cycle of a flight vehicle, which can induce anon-negligible uncertainty in the numerical simulation output, then the model validation of the numerical simulation is the premise of design optimization. Lots of researches on the uncertainty quantification (UQ) of numerical simulation has been carried out. However, literature has rarely seen works about performing scientific model parameter correction by using the results of UQ and establishing a closed-loop procedure of numerical simulation model validation in a systematic way. Therefore, a numerical simulation model validation method by considering uncertainties is proposed, and a model parameter correction approach based on high-quality small samples is developed, and then a closed-loop model validation process composed by uncertainty quantification, global sensitivity analysis, and parameter correction strategy to provide a scientific and systematic procedure for constructing high-fidelity numerical simulations is established. The effectiveness and advantages of the model validation method is verified through airfoil simulation.

Simulation of Mouldboard Plough Soil Cutting Based on Smooth Particle Hydrodynamics Method and FEM–SPH Coupling Method

In the field of agricultural machinery, various empirical field tests are performed to measure the tillage force for precision tillage. However, the field test performance is costly and time-consuming, and there are many constraints on weather and field soil conditions; the utilization of simulation studies is required to overcome these shortcomings. As a result, the SPH method and the coupled FEM-SPH method are used in this paper to investigate the mouldboard plough–soil interaction. In this paper, the finite element software LS-DYNA was used to build the SPH model and the FEM-SPH coupling model of soil cutting, as well as to investigate the change in cutting resistance during the soil cutting process. The simulation results are compared with those of the experiments, and the curves of the simulation and experiment are in good agreement, which verifies the reliability of the model. The validated simulation model was used to investigate the effects of the cutting speed, depth of cut, inclination angle, and lifting angle of the mouldboard plough on cutting resistance. The simulation studies show that the SPH model takes 5 h and 2 min to compute, while the FEM-SPH coupled model takes 38 min; obviously, the computational efficiency of the FEM-SPH coupled model is higher. The relative errors between the SPH model and the experiment are 2.17% and 3.65%, respectively. The relative errors between the FEM-SPH coupled model and the experiment are 5.96% and 10.67%, respectively. Obviously, the SPH model has a higher computational accuracy. The average cutting resistances predicted by the SPH model and the FEM-SPH coupled model, respectively, were 349.48 N and 306.25 N; these resistances are useful for precision tillage. The cutting resistance increases with the increase in cutting speed and is quadratic; the cutting resistance increases with the increase in cutting depth and is quadratic; the horizontal cutting resistance and the combined cutting resistance increase with the increase in inclination angle, while the vertical cutting resistance remains essentially constant with the increase in inclination angle; the horizontal cutting resistance and combined cutting resistance increase as the lifting angle increases, while the vertical cutting resistance decreases as the lifting angle increases.

Nonlinear model predictive control for yaw rate and body motion control through semi-active and active suspensions

Active and semi-active suspensions for passenger cars traditionally enhance comfort through body control, and vehicle handling by reducing the tyre load variations induced by road irregularities. Active suspensions can also be designed to track a desired yaw rate profile through the control of the lateral load transfer distribution between the front and rear axles. This paper considers an integrated system including semi-active and active suspension actuation to control the yaw, roll, pitch and heave dynamics excited by the driving actions. To this purpose, two novel real-time-capable implicit nonlinear model predictive control (NMPC) formulations, excluding and including cost function weight adaptation, are proposed and compared with the passive vehicle, and the controlled vehicle with two combinations of skyhook and active roll control, the first based on a pseudoinverse decoupling transformation for obtaining the damping force contributions, and the second using an inverse formulation. The algorithms are assessed through an experimentally validated simulation model, along manoeuvres corresponding to sub-limit and limit handling operation, to analyse the trade-off between body motion reduction and cornering response enhancement. The results show that the adaptable NMPC configuration provides the best performance in all scenarios, also for significant variations of the main vehicle and tyre parameters.

Preventing thermal runaway propagation in lithium-ion batteries: Model-based optimization of interstitial heat-absorbing thermal barriers

Advances in cathode and anode materials enable the energy density of lithium-ion batteries to increase further. However, safety concerns, particularly regarding thermal runaway propagation (TP), are intensifying. TP is a cascading reaction that occurs when a cell undergoing thermal runaway in a module triggers adjacent cells, leading to module destruction. Preventing TP is crucial, especially in applications like electric vehicles. This research introduces a method for designing safe battery systems using an interstitial barrier with a heat-absorbing effect to avert TP. For this purpose, a lumped element model parameterized by different methods is implemented to simulate the cell-to-cell TP inside a battery module. Comparison with TP tests on 3-cell modules of varying barrier thicknesses validates the model. The results exhibit effective TP prevention by the barrier, with TP occurring in just 41s without it. Moreover, our model is able to predict the TP times from the experiments. Further, this work demonstrates a design strategy involving a barrier with optimal thickness for maintaining volumetric energy density while ensuring safety. Therefore, this work highlights the significance of a heat-absorbing barrier and demonstrates its optimal module integration using a validated simulation model to promote the development of safe batteries.

Sparse SiPM Pixel Arrangement for a TOF-PET Detector Design that Achieves 100 ps Coincidence Time Resolution

Coupling silicon photomultipliers (SiPMs) to the side of a long and narrow scintillation crystal elements offers near-complete light extraction efficiency, independent of 511 keV photon interaction depth, and reduced scintillation light transit time jitter in its path toward the photodetector. With these advantages, significant improvements in PET detector coincidence time resolution (CTR) can be achieved compared to the traditional scintillation light readout configuration where an SiPM is coupled on the narrow end of crystals. Moreover, the arrangement of photosensors along the crystal’s side intrinsically provides a three-dimensional position sensing detector. One consideration for the side readout approach is the increase in the number of SiPMs required to read each crystal element. In this work, we investigate the achievable CTR when the number of SiPM elements coupled to the side of crystals is parametrically varied, including different crystal lengths and reflector configurations. A simulation model for CTR was validated against measurements with 3-20 mm length crystals employing side readout of scintillation light. The validated simulation model was used to parametrically investigate twenty-seven configurations of SiPMs arranged along the side of crystal elements. Reflector and sensor placement was parametrically varied to find configurations that optimize light collection efficiency, energy resolution, and CTR. One configuration achieved 101±1 ps FWHM CTR, while maintaining high light collection efficiency and good energy performance with a 33% reduction in photosensor area.

Dynamic Simulation Model and Experimental Validation of One Passive Fuel Cell–Battery Hybrid Powertrain for an Electric Light Scooter

Given the escalating issue of climate change, environmental protection is of growing importance. A rising proportion of battery-powered scooters are becoming available. However, their range is limited, and they require a long charging time. The fuel cell–battery-powered electric scooter appears to be a promising alternative. Further development of the active hybrid is the passive hybrid, in which the fuel cell is directly coupled to the battery, eliminating the need for a DC/DC converter. The passive hybrid promises the possibility of a reduction in the installation volume and cost. A simulation model is created MATLAB/Simulink for the passive fuel cell–battery hybrid electric scooter. It specifically focuses on how the power split between the fuel cell and battery occurs under dynamic load requirements. The scooter is powered by two air–hydrogen Proton Exchange Membrane Fuel Cell (PEMFC) systems with a nominal power of 250 W each and a Li-ion battery (48 V, 12 Ah). The validation is performed following an ECE-R47 driving cycle. The maximum relative deviation of the fuel cell is 2.82% for the current value. The results of the simulation show a high level of agreement with the test data. This study provides a method allowing for an efficient assessment of the passive fuel cell–battery hybrid electric scooter.

Calibration and validation of APSIM maize simulation model for different date of sowing

Calibration and validation of APSIM maize simulation model for different date of sowing