The aim of this work is to evaluate the accuracy of two computational models applied to the turbulent forced convection of alkali liquid metals of Na and NaK. The results of local Nusselt numbers for two turbulent models of realizable k-ε and SST k-ω are evaluated in this analysis. A solution verification process is carried out to determine epistemic uncertainty in computational results of convective heat transfer rates of Na in stainless steel (SS-316) miniature heat sinks. Besides solutions verification, the Numerical results were validated against the experimental data for local Nusselt numbers of NaK turbulent flow within a uniformly heated tube at a Reynolds number of 30,260. The results from the SST k-ω model follow the trend of the experimental data better than the realizable k-ε turbulent model. The realizable k-ε turbulent model overestimates the NaK local Nusselt numbers by almost 5%. In both turbulent models, the maximum epistemic uncertainty of the local convective heat transfer rate is 4% within the investigated miniature heat sink at a Reynolds number of 9,000.
{"title":"Validation and Verification Analyses of Turbulent Forced Convection of Na and NaK in Miniature Heat Sinks","authors":"Baixuan Pourghasemi, N. Fathi","doi":"10.1115/vvuq2023-108819","DOIUrl":"https://doi.org/10.1115/vvuq2023-108819","url":null,"abstract":"\u0000 The aim of this work is to evaluate the accuracy of two computational models applied to the turbulent forced convection of alkali liquid metals of Na and NaK. The results of local Nusselt numbers for two turbulent models of realizable k-ε and SST k-ω are evaluated in this analysis. A solution verification process is carried out to determine epistemic uncertainty in computational results of convective heat transfer rates of Na in stainless steel (SS-316) miniature heat sinks. Besides solutions verification, the Numerical results were validated against the experimental data for local Nusselt numbers of NaK turbulent flow within a uniformly heated tube at a Reynolds number of 30,260. The results from the SST k-ω model follow the trend of the experimental data better than the realizable k-ε turbulent model. The realizable k-ε turbulent model overestimates the NaK local Nusselt numbers by almost 5%. In both turbulent models, the maximum epistemic uncertainty of the local convective heat transfer rate is 4% within the investigated miniature heat sink at a Reynolds number of 9,000.","PeriodicalId":387733,"journal":{"name":"ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131519893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deterministic integrated metrics for quantitative comparison of simulated images and experimental images, e.g., RMS error, are agnostic to structures that can emerge in highly nonlinear complex systems. Similarly, simple probabilistic metrics, such as direct comparisons of image data distributions, also do not explicitly account for salient structures. Normalizing flow architectures are probabilistic generative deep learning algorithms that leverage the nonlinear pattern recognition capacity of neural networks with variational Bayesian methods to assign likelihood values to images with respect to a “target” probability density learned from training images. If a normalizing flow is trained on simulation image data, then it can be used to quantify the probability that an experiment image could have been sampled from the unknown high dimensional distribution that describes the simulated images and vice versa. We demonstrate this validation method using the real non-volume-preserving (RealNVP) normalizing flow architecture and MNIST, corrupted MNIST, Wingdings, and blurred Wingdings data sets. Normalizing flows, and consequently our validation method, are not limited to two-dimensional data and may be applied to higher dimensions with appropriate modifications. Applications include, but are not limited to, turbulent flow simulations, proton radiography simulations, multi-phase flow simulations, and medical radiology.
{"title":"Probabilistic Deep Learning for Validation of Emergent Structures in Simulated Images","authors":"B. Kaiser, K. Hickmann","doi":"10.1115/vvuq2023-108722","DOIUrl":"https://doi.org/10.1115/vvuq2023-108722","url":null,"abstract":"\u0000 Deterministic integrated metrics for quantitative comparison of simulated images and experimental images, e.g., RMS error, are agnostic to structures that can emerge in highly nonlinear complex systems. Similarly, simple probabilistic metrics, such as direct comparisons of image data distributions, also do not explicitly account for salient structures. Normalizing flow architectures are probabilistic generative deep learning algorithms that leverage the nonlinear pattern recognition capacity of neural networks with variational Bayesian methods to assign likelihood values to images with respect to a “target” probability density learned from training images. If a normalizing flow is trained on simulation image data, then it can be used to quantify the probability that an experiment image could have been sampled from the unknown high dimensional distribution that describes the simulated images and vice versa. We demonstrate this validation method using the real non-volume-preserving (RealNVP) normalizing flow architecture and MNIST, corrupted MNIST, Wingdings, and blurred Wingdings data sets. Normalizing flows, and consequently our validation method, are not limited to two-dimensional data and may be applied to higher dimensions with appropriate modifications. Applications include, but are not limited to, turbulent flow simulations, proton radiography simulations, multi-phase flow simulations, and medical radiology.","PeriodicalId":387733,"journal":{"name":"ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125303868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present work is devoted to evaluating the hydraulic properties of Triply Periodic Minimal Surfaces (TPMS) structures, a generation of porous structures developed using the periodicity of trigonometric equations to generate triply periodic minimal surfaces. The thorough computational and experimental analysis coupled with verification assessment is key to using these product structures in thermal hydraulics especially to address industrial requirements. Here the hydraulic properties are computed by performing three-dimensional CFD analyses using Star-CCM+. Gyroid TPMS was hydraulically analyzed with a water flow in three-channel configurations (circular, square, and rectangular section), with the same hydraulic diameter and length, respectively 5.08cm and 10cm. Their porosity values range from 80% to 93% depending on the unit cell dimensions (chosen values were 10mm, 15mm, 20mm, 25mm, and 30mm). The CFD models for the rectangular TPMS contain the maximum epistemic uncertainty of 19% following the ASME VV 20 codes. In preparation for the forthcoming test campaign, the hydraulic characteristic of the different channels is assessed comparatively, and the friction factors are computed and compared to reach a basic understanding of the parametric effect of channel shape and cell size.
{"title":"Numerical Assessment of Hydraulic Properties of Triply Periodic Minimal Surfaces Structures","authors":"Cecilia Piatti, L. Savoldi, N. Fathi","doi":"10.1115/vvuq2023-108794","DOIUrl":"https://doi.org/10.1115/vvuq2023-108794","url":null,"abstract":"\u0000 The present work is devoted to evaluating the hydraulic properties of Triply Periodic Minimal Surfaces (TPMS) structures, a generation of porous structures developed using the periodicity of trigonometric equations to generate triply periodic minimal surfaces. The thorough computational and experimental analysis coupled with verification assessment is key to using these product structures in thermal hydraulics especially to address industrial requirements. Here the hydraulic properties are computed by performing three-dimensional CFD analyses using Star-CCM+. Gyroid TPMS was hydraulically analyzed with a water flow in three-channel configurations (circular, square, and rectangular section), with the same hydraulic diameter and length, respectively 5.08cm and 10cm. Their porosity values range from 80% to 93% depending on the unit cell dimensions (chosen values were 10mm, 15mm, 20mm, 25mm, and 30mm). The CFD models for the rectangular TPMS contain the maximum epistemic uncertainty of 19% following the ASME VV 20 codes. In preparation for the forthcoming test campaign, the hydraulic characteristic of the different channels is assessed comparatively, and the friction factors are computed and compared to reach a basic understanding of the parametric effect of channel shape and cell size.","PeriodicalId":387733,"journal":{"name":"ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：