Validation Of Simulation Model Research Articles

Probabilistic Validation: Theoretical Foundation and Methodological Platform

Abstract Addressing safety concerns in commercial nuclear power plants (NPPs) often requires the use of advanced modeling and simulation (M&S) in association with the probabilistic risk assessment (PRA). Advanced M&S are also needed to accelerate the analysis, design, licensing, and operationalization of advanced nuclear reactors. However, before a simulation model can be used for PRA, its validity must be adequately established. The objective of this research is to develop a systematic and scientifically justifiable validation methodology, namely, probabilistic validation (PV), to facilitate the validity evaluation (especially when validation data are not sufficiently available) of advanced simulation models that are used for PRA in support of risk-informed decision-making and regulation. This paper is the first in a series of two papers related to PV that provides the theoretical foundation and methodological platform. The second paper applies the PV methodological platform for a case study of fire PRA of NPPs. Although the PV methodology is explained in the context of PRA of the nuclear industry, it is grounded on a cross-disciplinary review of literature and so applicable to validation of simulation models, in general, not necessarily associated with PRA or nuclear applications.

Enumerating contributions of fentanyls and other factors to the unprecedented 2020 rise in opioid overdose deaths: model-based analysis.

In 2020, the ongoing US opioid overdose crisis collided with the emerging COVID-19 pandemic. Opioid overdose deaths (OODs) rose an unprecedented 38%, due to a combination of COVID-19 disrupting services essential to people who use drugs, continued increases in fentanyls in the illicit drug supply, and other factors. How much did these factors contribute to increased OODs? We used a validated simulation model of the opioid overdose crisis, SOURCE, to estimate excess OODs in 2020 and the distribution of that excess attributable to various factors. Factors affecting OODs that could have been disrupted by COVID-19, and for which data were available, included opioid prescribing, naloxone distribution, and receipt of medications for opioid use disorder. We also accounted for fentanyls' presence in the heroin supply. We estimated a total of 18,276 potential excess OODs, including 1,792 lives saved due to increases in buprenorphine receipt and naloxone distribution and decreases in opioid prescribing. Critically, growth in fentanyls drove 43% (7,879) of the excess OODs. A further 8% is attributable to first-ever declines in methadone maintenance treatment and extended-released injectable naltrexone treatment, most likely due to COVID-19-related disruptions. In all, 49% of potential excess OODs remain unexplained, at least some of which are likely due to additional COVID-19-related disruptions. While the confluence of various COVID-19-related factors could have been responsible for more than half of excess OODs, fentanyls continued to play a singular role in excess OODs, highlighting the urgency of mitigating their effects on overdoses.

Validation of a wind turbine gearbox strain simulation model in service to virtual sensing

Over the last decades, the use of wind power as a source of renewable power has increased, while the Levelized Cost of Electricity (LCoE) of wind power has fallen. In order to further drive down the LCoE, there is an increasing interest to monitor key turbine quantities in order to improve maintenance procedures and decrease downtime. In this paper, we focus on the indirect detection of one such key quantity, the torque load on the gearbox. In order to avoid expensive direct torque sensors, we study the potential of strain gauges installed on the gearbox housing as virtual torque sensors. Our results verify that the strain response is repeatable, torque-driven and predictable; which are the most important conditions for using the sensors in virtual load sensing. Next, we compare the measured strain response to simulated strain results obtained from a physics-based model. The model consists of a static FE model of the gearbox housing and a torsional model of the shafts and gears in the drivetrain. An optimisation approach selects the FE model node where the simulated strain best matches the measured strain. We conclude that the studied strain gauges are promising for model-based virtual sensing of the torque on the gearbox.

Characterization of Soot Loading and Filtration Efficiency of a Gasoline Particulate Filter with Photoacoustic Sensor and Particle Number Counting Systems

An optimum operation of a gasoline particulate filter (GPF) for a gasoline direct injection vehicle in terms of its performance of pressure drop, soot loading, and filtration efficiency becomes inevitable to fulfill upcoming emission regulations. This paper proposes a methodology to characterize the GPF performance for validation of simulation models for more precise operation strategies along with future legislative requirements. The feasibility was examined through experiments of miniature GPF samples using a synthetic particle generator. Firstly, permeability of the GPF walls was estimated to be 6.9 ± 1.5 × 10−13 m2 by a flow resistance descriptor model, which was in good agreement with its pore structure. Secondly, photoacoustic sensor systems indicated soot accumulation inside the GPFs in real time thanks to linear correlations between sensor signals and soot mass concentrations in exhaust. Thirdly, particle number counting systems compliant with respective regulatory technical requirements exhibited time-resolved filtration efficiencies of the GPFs in conjunction with solid particle number emissions whose diameter was larger than 10 nm and 23 nm. The filtration efficiencies at a clean state of the GPF were 0.78 and 0.77, respectively. The slight difference could be explained by Brownian diffusion and interception for particle filtration.

Long-Term Effects of Increasing Buprenorphine Treatment Seeking, Duration, and Capacity on Opioid Overdose Fatalities: A Model-based Analysis.

Because buprenorphine treatment of opioid use disorder reduces opioid overdose deaths (OODs), expanding access to care is an important policy and clinical care goal. Policymakers must choose within capacity limitations whether to expand the number of people with opioid use disorder who are treated or extend duration for existing patients. This inherent tradeoff could be made less acute with expanded buprenorphine treatment capacity. To inform such decisions, we used a validated simulation model to project the effects of increasing buprenorphine treatment-seeking, average episode duration, and capacity (patients per provider) on OODs in the United States from 2023 to 2033, varying the start time to assess the effects of implementation delays. Results show that increasing treatment duration alone could cost lives in the short term by reducing capacity for new admissions yet save more lives in the long term than accomplished by only increasing treatment seeking. Increasing provider capacity had negligible effects. The most effective 2-policy combination was increasing capacity and duration simultaneously, which would reduce OODs up to 18.6% over a decade. By 2033, the greatest reduction in OODs (≥20%) was achieved when capacity was doubled and average duration reached 2 years, but only if the policy changes started in 2023. Delaying even a year diminishes the benefits. Treatment-seeking increases were equally beneficial whether they began in 2023 or 2025 but of only marginal benefit beyond what capacity and duration achieved. If policymakers only target 2 policies to reduce OODs, they should be to increase capacity and duration, enacted quickly and aggressively.

Development and validation of a transient simulation model of a full-scale PCM embedded radiant chilled ceiling

The recent significant rise in space cooling energy demand due to the massive use of air-conditioning systems has adversely changed buildings’ energy use patterns globally. The updated energy technology perspectives highlight the need for innovative cooling systems to address this growing cooling demand. Phase change material embedded radiant chilled ceiling (PCM-RCC) has lately acquired popularity as they offer more efficient space cooling together with further demand-side flexibility. Recent advancements in PCM-RCC applications have increased the necessity for reliable simulation models to assist professionals in identifying improved designs and operating settings. In this study, a transient simulation model of PCM-RCC has been developed and validated using measured data in a full-scale test cabin equipped with newly developed PCM ceiling panels. This model, developed in the TRNSYS simulation studio, includes Type 399 that uses the Crank-Nicolson algorithm coupled with the enthalpy function to solve transient heat transfer in PCM ceiling panels. The developed model is validated in both free-running and active operation modes, and its quality is then evaluated using several validation metrics. The results obtained in multiple operating scenarios confirm that the model simulates the transient behaviour of the PCM-RCC system with an accuracy within ±10%. Aided by this validated model, which offers the user detailed flexibilities in the system design and its associated operating schemas, PCM-RCC’s potentials regarding peak load shifting, energy savings, and enhanced thermal comfort can be investigated more reliably.

Modeling, validation and exergy evaluation of a thermally-integrated industrial cryogenic air separation plant in Colombia. Part II. Optimization under variable products demand

This work deals with the optimization of an existing industrial cryogenic air separation plant with respect to exergy efficiency and argon recovery. A previously validated simulation model of the plant was used, and the operating conditions and specifications of the process correspond to those reported in the historical records of an air separation facility in Colombia. The optimization is carried out considering the variation in the historical demand and the installed capacity of the plant, using an elitist nondominated sorting genetic algorithm (NSGA-II). As exergy efficiency and argon production goals are in conflict, a Pareto front of optimal solutions is obtained for the assessed variables. It is found that under optimal conditions of the pareto fronts, exergy efficiency and argon recovery can be enhanced up to 30 % and 23 %, respectively, with respect to recommended operating conditions from the plant manual. Also, obtained results indicate that the plant operated with larger air feed flowrates than required for the corresponding specific monthly demands. Under the optimal conditions, the plant could operate with a 58 % average reduction in the specific energy consumption (0.53 kW/Nm3O2) and larger argon recoveries. This represents a major improvement in the profitability of the operation and its sustainability indicators.

A Continuous Process for Validation, Verification, and Accreditation of Simulation Models

A simulation model, and more generically, a model, is founded on its assumptions. Assurance of the model’s correctness and correct use is needed to achieve accreditation. Often the exercise of working with a specific code misunderstands the overall process, focusing the resources on the model coding and forgetting the needed resources to ensure the validation of every step of the model definition and coding. The goal of this work is to present a methodology to help in the definition and use of the assumptions in the modeling process. To do so, we present a process to conduct a simulation project, an assumptions taxonomy, and a method that simplifies working with those assumptions. We propose to extend the traditional Validation, Verification, and Accreditation processes to a process composed of eight Validation, Verification, and Accreditation phases that cover the overall life cycle of a model. Although this paper is focused on a simulation model, we can extend the proposed method to a more general modeling approach.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology.

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

Influence of Moisture on the Energy Performance of Retrofitted Walls - Experimental Assessment and Validation of an Hygrothermal Model

ABSTRACT The rehabilitation of existing buildings façades should be based on the improvement in comfort and the decrease of energy consumption. For this purpose, it is necessary to select a retrofitted solution capable of minimizing the heat losses across the building envelope. However, it is not only the retrofitted solution of the wall that influences the amount of heat transported across it, but also the moisture content in the wall under in-service conditions. An experimental prototype was developed to experimentally assess the impact of a wetting period on the energy performance of wall test specimens. The experiment simulated the exposure of the specimens to a rainy period with a continuous record of the heat fluxes across both surfaces of the specimens. Additionally, the validation of the WUFI hygrothermal simulation model, based on the experimental measurements performed, was carried out. Driving rain resulted in an increase of heat losses of around 10%.

Determining the Validity of Simulation Models for the Verification of Automated Driving Systems

Determining the Validity of Simulation Models for the Verification of Automated Driving Systems

Stochastic Models for the Fading Channel Success Rate of Sub-6G UWB Systems

This paper presents different stochastic models for entangled UWB fading channels. Closed-form canonical expressions are derived for the statistical distribution of the fading power difference of interfering independent and dependent mixture of flat-fading Rayleigh and Rician channels. The link success probability is derived in terms of the cumulative distribution function of the channel fading power differences. An alternative metric for link success rate (LSR) is developed in terms of the statistical distribution of the ratio of the diffuse powers. Under this alternative formulation, and for the case of 2 independent Rayleigh paths, the LSR statistical distribution was consistent with the heavy-tail Lomax distribution. The scope of future research includes the validation of theoretical and simulated models with real measurements on actual multi-user massive multiple-input-multiple-output (MU-mMIMO) beam-forming sub-6G physical systems.

Simulation and Experimental Analysis of Surface Defects in Turning of TiCp/TC4 Composites.

Processing TiCp/TC4 composites has always been difficult due to the mismatch between the mechanical and thermal properties of the matrix and the reinforced particles, which results in a variety of machined surface defects. To expose the mechanism of defect generated on the cutting surface of TiCp/TC4 composites and improve their cutting surface quality, a 3D finite element orthogonal turning simulation model of TiCp/TC4 composites is developed. The failure at the matrix-particle interface and the fracture and removal mechanism of the reinforcing phase particles are analyzed from a microscopic perspective using a single particle cutting simulation model. In addition, a three-dimensional cutting simulation model with randomly dispersed TiC particles and a volume fraction of 5% is developed, and various forms of cutting surface defects of TiCp/TC4 composites are examined. To verify the validity of the finite element simulation model, TiCp/TC4 composites with a volume fraction of 5% are selected for turning tests. For various cutting tools and particle relative positions, the simulation and test results show that the removal of particles takes the following forms: debonding, crushing, brittle fracture, and slight fracture at the top, leading to a shallow cavity, microcracks, residual TiC particles embedded in the cavity, and surface defects with severe plastic deformation of the matrix surrounding the cavity on the machined surface.

Development, Validation, and Evaluation of Partial PST Tractor Simulation Model for Different Engine Modes during Field Operations

The objectives of this study are the development and verification of a simulation model of the partial PST (power-shift transmission) tractor based on actual field operations. The PST simulation model was verified for the asphalt driving condition, and performance was evaluated for asphalt driving, plow, and rotary tillage. In this study, the traditional, APS (Auto Power Shift) ECO, and APS power engine modes were used to analyze fuel consumption. The statistical analysis proved that the experimental and simulation results were in a linear relationship, with an accuracy of over 98%. Finally, the results suggested that users could utilize the 95-kW partial PST tractor in the APS ECO engine mode with higher fuel economy compared to the traditional and APS power modes.

Rational Allocation of Water Resources in the Arid Area of Northwestern China Based on Numerical Simulations

Adding a series of surface-water transfer projects still cannot solve the current water shortages in the arid area of northwestern China. Selecting a rational allocation plan for the water resources is the key to coordinating water use for the national economy and ecological environment. In this study, taking the Wuwei Basin as the study area, long-term data of source-sink terms from 2007 to 2018 were analyzed. Following the calibration and validation of the numerical simulation model of the groundwater system, the data was highly fitted. Based on this model, the groundwater system balance, water level variations, and suitable ecological water level area in 2050 under four water resource allocation plans were compared. Under plan 4, the groundwater resources change from an average decrease of 7656.4 × 104 m3·yr−1 from 2007 to 2018, to an increase of 4624.6 × 104 m3·yr−1 in 2050, which means the groundwater systems are almost in a positive balance state. Compared with 2018, the water level with small groundwater depth drops by 2.2–5.7 m, while that with large groundwater depths steadily rises by 2.7–8.6 m. In addition, it can maintain the 9 km2 natural oasis wetland area and the 116 km2 well-growing natural vegetation area, which can effectively promote the benign evolution and efficient, balanced sustainable development of the regional water resources, economy, and ecological environment.

Model-based energy management systems: Weighting of multiobjective functions using the Volatile Energy Prices Scalarization (VEPS)

Predictive energy management systems (EMSs) enable industrial plants to operate the energy supply systems at optimal efficiency, taking account of multiple objectives, including energy cost reduction. The performance of model-based EMSs depends on the appropriate design and correct scalarization of the resulting multiobjective function. This paper introduces the Volatile Energy Prices Scalarization (VEPS) method, which effectively designs, standardizes, and weighs the multiobjective function of model-based EMSs without the need for prior simulations or test runs. We present a case study, in which we compare the VEPS method to other state-of-the-art methods, utilizing a validated simulation model from an industrial food plant. The results show that the VEPS method outperforms other weighting methods with comparable tuning effort in this case-study. Moreover, the performance of the VEPS method is close to the Pareto-optimal performance. Economic weighting methods such as VEPS enable a fast and cost-effective implementation of EMS in the manufacturing industry.

Microscale Modeling of Frozen Particle Fluid Systems with a Bonded-Particle Model Method

An inventive microscale simulation approach is applied to investigate the mechanics of frozen particle fluid systems (PFS). The simulation is based on the discrete element method (DEM) and bonded-particle model (BPM) approach. Discrete particles connected by solid bonds represent frozen agglomerates. Uniaxial compression experiments were performed to gather data for material modeling and further simulation model validation. Different typical mechanical behavior (brittle, ductile, dilatant) were reviewed regarding strain rates, saturation levels, and particle mechanical or surface properties. Among all these factors, strain rate significantly affects the mechanical behavior and properties of the agglomerates. A new solid bond model considering strain-dependent and time-dependent behavior is developed for describing the rheology of the frozen particle fluid systems. Without alternating Young's modulus and Poisson's ratio of the bond material, the developed solid model provides a suitable agreement with the experimental results regarding different strain rates.

Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles

Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The ”eigenangle test” quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov–Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.

A hybrid solidification enhancement in a latent-heat storage system with nanoparticles, porous foam, and fin-aided foam strips

The relatively high storage density and negligible temperature stratification of phase-changing materials (PCMs) facilitate their application as storage modules for managing the mismatch between energy generation and consumption. The poor heat diffusivity of these materials limits their storage effectiveness, and therefore employing efficient thermal enhancers is required. This study examines the fin-aided foam strip as a hybrid enhancer for overcoming the relatively low thermal response of erythritol as PCM in a triple-pipe containment system. Four different enhancement additives, including porous foam, graphene nanoparticles, longitudinal fins, and foam strips within the same volume limitations, were compared and tested via applying an experimentally validated simulation model to verify the functionality of the proposed thermal enhancers. The results show that the inclusion of full foam, fin arrays, and fin-assisted foam strips substantially enhances the solidifying behaviour of PCM as compared to the inclusion of nanoparticles alone. The solidifying time can be decreased by 60.8 %, 63.2 %, and 77.3 %, while the recovery rate can be increased by 152.9, 171.6 %, and 340.1 %, respectively. The results also suggest that increasing the length of strips while decreasing their thickness can further enhance the thermal response of PCM for solidification within the same volume limitations.

Dynamic modeling and experimental validation of a standalone hybrid microgrid system in Fukuoka, Japan

Microgrids are essential for creating next-generation energy systems because they allow loads, energy storage systems (ESS), and distributed energy resources (DER) to be efficiently and seamlessly integrated. This study presents the dynamic modeling and simulation of an off-grid direct current (DC) microgrid consisting of the photovoltaic (PV) panel, wind turbine, battery, and a DC load incorporating simple, comprehensible, and well-established component-based power control strategies for quality power output. In addition, the main emphasis is on the real-time experimental validation of the dynamic simulation model achieved by designing and developing an indoor test system architecture. A 48-hourly meteorological dataset from Fukuoka, Japan, was used to validate the developed model. The results show a reasonable range of Root-mean-square deviation (RMSE), suggesting that the simulation model can precisely depict the model the dynamic operation of a hybrid DC microgrid system. Furthermore, detailed scenario analysis for sunny, windy, rainy, and cloudy considering real-time meteorological conditions for 72 h of simulation reveals that the proposed microgrid system can effectively meet the load in any situation with a sufficiency factor above 1, making it a self-sustaining hybrid renewable microgrid for residential areas in Japan. The study highlights the significance of understanding microgrid operation energy management with actual implementation.