Calibration Of Simulation Models Research Articles

Calibration of simulation model to analyze hospital building energy performance

This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry.

Application of Bayesian models in building information modeling of residential buildings

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.

Simulation-Based Evaluation of the Impact of an Electrochromic Glazing on the Energy Use and Indoor Comfort of an Office Room

Electrochromic glazing alters its optical properties in the absence/presence of an electrical charge, varying from clear to dark to control daylighting and solar heat gains. This study aims to evaluate the impact of an electrochromic glazing, with indoor glare or temperature control, on the energy performance and thermal and visual comfort of an office room under three European climates, using a calibrated simulation model. The novelty of the paper lies in its combined performance assessment, using different standards and metrics. The results showed reduced climatization energy requirements with temperature control, but significantly increased artificial lighting energy use. Glare control achieved useful illuminance levels during 74–80% of working hours. Concerning temperature control, working hours within thermal comfort increased (21–43%) under a free-float regime. Moreover, the performance of this glazing was compared to that of a clear glazing with/without a reflective film and a thermochromic glazing for different solar orientations. The electrochromic glazing with glare control showed the highest energy savings (14–36%) for a western orientation, and the lowest negative impact on daylighting for a northern orientation. The best glare reduction was achieved with the reflective film. Considering the free-float regime, the electrochromic glazing, with temperature control, showed the highest increase in working hours within thermal comfort (6–9%) for a western orientation.

Synthetic Population: A Reliable Framework for Analysis for Agent-Based Modeling in Mobility

This paper presents a comprehensive and innovative evaluation framework for identifying a reliable population synthesis for agent-based modeling–transportation-oriented simulations (ABM–TOS). We show, via this framework and different metrics for the analysis of the generated distribution of the individuals’ attributes, that population synthesizers may fail to correctly replicate the real population heterogeneity owing to diverse control variables, data limitations, and post-simulation computation of certain parameter distributions. To show these shortcomings, the authors propose a systematic classification of different types of distributions crucial for mobility simulations. The proposed framework aims to provide a comprehensive overview of the population and serve as a rapid ’debugging’ tool to identify and rectify any flaws in a specific population during the calibration of the activity-based mobility simulation models. To prove the effectiveness of this framework, we applied it to synthetic populations generated through MOBIUS (mobility optimization based on iterative user synthesis), a newly developed synthetic population generator, which in this case was employed to create different variants of the Luxembourg population (1%, 10%, 30%). The application of our framework to these populations not only provided an effective method for assessing their goodness of fit, but also helped highlight the distributions that are most critical to the successful implementation of the methodology.

Modelling the weld cladding process to predict weld clad position and shape error

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Application of differentiable programming to wave simulation

Wave simulations play a crucial role in a wide range of scientific and engineering applications, including seismic imaging, optical design, and acoustic modeling. Here we explore the advantages of differentiable programming in the context of acoustic wave simulation. Differentiable programming enables us to treat wave simulation as a differentiable function, allowing for the automatic computation of gradients with respect to any continuous input parameter. We demonstrate how this approach can be applied to various types of wave simulations, such as Pseudo-spectral time-domain solvers or iterative solvers. Implementing wave simulators via differentiable programming achieves several benefits. First, it enables efficient and accurate sensitivity analysis: This is particularly valuable for optimization and uncertainty quantification tasks. Second, it facilitates the incorporation of wave simulations into machine learning frameworks, enabling the integration of simulation-based models with data-driven approaches. Third, differentiable programming can accelerate the calibration and inversion of wave simulation models, making it easier to match simulated results to observed data. We present practical examples and discuss potential applications in fields such as geophysics and medical imaging. Our findings highlight the potential of this approach to advance the state-of-the-art in wave simulation techniques and their integration into larger computational pipelines.

Evaluating different levels of information on the calibration of building energy simulation models

Evaluating different levels of information on the calibration of building energy simulation models

Data assimilation for online model calibration in discrete event simulation

The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models’ parameters in real time and for improving data assimilation results.

Digital Twin Technologies for Autonomous Environmental Control and Life Support Systems

Environmental control and life support systems will require enhanced self-awareness and self-sufficiency as human spaceflights are designed to reach further destinations. These requirements have led to the development of autonomous technologies and systems to enable more Earth independence, while at the same time relying more heavily on the knowledge contained in their computational models (as opposed to the knowledge of ground control experts). For environmental control and life support systems, these consist of disparate models often tailored to specific subsystems and use cases, such as temperature control and CO2 removal. Therefore, there is a need for technologies supporting the integration of existing models. We propose to extend existing digital twin frameworks to integrate models and serve as a tool for answering onboard queries during operation. Toward this vision, we identify research directions for three types of technologies: i) streamlining the process of merging information models without redundancies by leveraging model-based systems engineering languages; ii) the calibration of simulation models that requires a better understanding of how to encode domain knowledge into a probabilistic representation of subsystem states and model parameters; and iii) automating the query understanding process and constructing a mapping between information and simulation models.

Surrogate model-based calibration of a flying Earth observation satellite

At the European Space Agency (ESA), Modeling and Simulation (M&S) plays a fundamental role during the lifetime of a spacecraft, being used from the design phase to the testing and during operations in space. M&S tools embed general physics-based models and disciplines characterized by configurable parameters which have to be calibrated in order to mimic the behavior of the actual flying spacecraft. However, their calibration requires a large number of simulations which are unfeasible to be obtained through computationally expensive high-fidelity simulation models. Thus, the inability to calibrate the high-fidelity simulation models poses limitations for the use of M&S tools during spacecraft operations.In this light, the present work proposes the use of a surrogate model-based approach for the calibration of simulation models of spacecraft. The approach integrates a computationally inexpensive deep-learning-based surrogate model, which mimics the high-fidelity simulation model without requiring the same computational burden, and a metaheuristic optimization algorithm, to identify the optimal values of the simulation model configurable parameters. This enhances the capabilities of M&S tools and allows their use in operations. The approach’s effectiveness is shown by its application to real flying Earth observation satellite data and simulation models.

Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks

Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios. In general, calibration is performed manually by traffic engineers who use their knowledge and experience to adjust the parameters of the simulator. Unfortunately, there is still no systematic and automatic process for calibrating traffic simulation networks, although some methods have been proposed in the literature. This study proposes a methodology that facilitates the calibration process, where an artificial neural network (ANN) is trained to learn the behavior of the transport network of interest. The ANN used is the Multi-Layer Perceptron (MLP), trained with back-propagation methods. Based on this learning procedure, the neural network can select the optimized values of the simulation parameters that best mimic the traffic conditions of interest. Experiments considered two microscopic models of traffic and two psychophysical models (Wiedemann 74 and Wiedemann 99). The microscopic traffic models are located in the metropolitan region of São Paulo, Brazil. Moreover, we tested the different configurations of the MLP (layers and numbers of neurons) as well as several variations of the backpropagation training method: Stochastic Gradient Descent (SGD), Adam, Adagrad, Adadelta, Adamax, and Nadam. The results of the experiments show that the proposed methodology is accurate and efficient, leading to calibration with a correlation coefficient greater than 0.8, when the calibrated parameters generate more visible effects on the road network, such as travel times, vehicle counts, and average speeds. For the psychophysical parameters, in the most simplified model (W74), the correlation coefficient was greater than 0.7. The advantage of using ANN for the automatic calibration of simulation parameters is that it allows traffic engineers to carry out comprehensive studies on a large number of future scenarios, such as at different times of the day, as well as on different days of the week and months of the year.

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

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

Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

A semi-empirical approach to calibrate simulation models for semiconductor devices

Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. Ideally, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This calibration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non characterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device.

Parameter calibration of the discrete element simulation model for soaking paddy loam soil based on the slump test.

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the pulping components and the paddy soil during the paddy field pulping process. The findings are valuable in optimizing the parameters of the paddy beating device to improve its working quality and efficiency. However, the lack of accurate soil models for paddy soil has limited the application and development of the discrete element method in paddy pulping research. This study selected the Hertz-Mindlin with Johnson-Kendall-Roberts discrete element model for the pre-pulping paddy loam soil and used the slump error as the test index to select nine parameters, including soil Poisson's ratio and surface energy, as test factors to calibrate the model parameters. The Plackett-Burman test identified soil shear modulus, surface energy, and soil-iron plate static friction coefficient as significant factors affecting the test index. The steepest ascent test results determined the test range of the above parameters. The Box-Behnken test obtained the regression model between the significant factors and the test index, and the regression model was optimized using the slump error as the target. The optimal combination of parameters was surface energy of 3.257 J/m2, soil shear modulus of 0.709 MPa, and static friction coefficient between soil and iron plate of 0.701. The slump simulation test using this combination of parameters yielded an average slump error of 2.04%. The collective results indicate the accuracy of the calibrated discrete element simulation parameters for paddy loam soil. These parameters can be used for discrete element simulation analysis of the paddy pulping process after paddy field soaking.

Optimizing overheating, lighting, and heating energy performances in Canadian school for climate change adaptation: Sensitivity analysis and multi-objective optimization methodology

This paper aims to develop long-term adaptation strategies for the existing Canadian school buildings under extreme current and future climates using a developed methodology based on global and local sensitivity analysis and Multi-Objective Optimization Genetic Algorithm. The calibrated simulation model based on indoor and outdoor measured temperature for a school of interest is used to evaluate the optimization strategies. This paper aims to search for the optimum school building design under three simultaneous conflicting objective functions: (1) the minimization of overheating hours to less than 40 h as required by Building Bulletin BB101 building code by using passive mitigation measures, (2) the minimization of heating energy use to less than 15 kW/m2 according to passive house requirements and thus the reduction of greenhouse gas emissions, and (3) the minimization of artificial lighting energy use to less than the current lighting energy use by maximization of daylighting usage without exceeding acceptable glare index in classrooms. Ten building design variables are selected, which could generate approximately 300,000 solutions. The developed methodology reduced the numbers to 14,400 solutions and found seven Pareto solutions that comply with the three objectives and their constraints. High energy-efficient building envelope, appropriate window-wall ratio and window type, natural ventilation during the day, and night cooling can play a key role in achieving the objectives under current weather conditions. An additional cool roof and external overhang will be needed in the medium-term future climate, and an additional movable screen shading will be needed in the long-term future climate.

Experimental and simulative investigation of welding sequences on thermally induced distortions in wire arc additive manufacturing

PurposeThe purpose of this paper is to demonstrate the impact of the welding sequence on the substrate plate distortion during the wire and arc additive manufacturing (WAAM) process. This paper also aims to show the capability of finite element simulations in the prediction of those thermally induced distortions.Design/methodology/approachAn experiment was conducted in which solid aluminum blocks were manufactured using two different welding sequences. The distortion of the substrates was measured at predefined positions and converted into bending and torsion values. Subsequently, a weakly coupled thermo-mechanical finite element model was created using the Abaqus simulation software. The model was calibrated and validated with data gathered from the experiments.FindingsThe results of this paper showed that the welding sequence of a part significantly affects the formation of thermally induced distortions of the final part. The calibrated simulation model was able to capture the different distortion behavior attributed to the welding sequences.Originality/valueWithin this work, a simulation model was developed capable of predicting the distortion of WAAM parts in advance. The findings of this paper can be used to improve the design of WAAM welding sequences while avoiding high experimental efforts.

Objective and automated calibration of progressive damage models for finite element simulation of fiber reinforced composites

Objective and automated calibration of progressive damage models for finite element simulation of fiber reinforced composites