David A. Najera-Flores, Justin Jacobs, D. Quinn, Anthony Garland, Michael D. Todd
Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural state. However, these models-which may be thought of as a form of a digital twin-need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. It is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.
用于航空航天、民用或机械应用的复杂结构系统必须在不同的运行条件下可靠运行。结构健康监测（SHM）系统通过对结构状态进行持续监测，有助于确保这些系统的可靠性。SHM 依靠将测量数据与预测模型相结合，对结构状态做出明智的决策。然而，这些模型可被视为数字孪生的一种形式，需要在结构发生变化（如损坏）时不断更新。我们提出了一种不确定性感知机器学习模型，该模型强制保持原始输入状态空间的距离，然后通过高斯过程（GP）内核对距离感知机制进行编码。所提出的方法利用频谱归一化神经 GP 算法，将神经网络的灵活性与 GP 的优势结合起来，再加上结构保持约束，从而产生一个不确定性感知模型。该模型用于检测因结构变化而导致的域偏移，由于结构变化可能在空间上是孤立的（如关节内部或局部损坏），因此无法直接观察到。这项工作利用检测理论，根据模型产生的预测方差的统计特征，系统地检测域偏移。所提出的方法在一个受到损伤的非线性结构上进行了演示。结果表明，所提出的方法能够依靠转换后的输入状态空间的距离来预测转移域中增加的方差，同时对规范变化具有鲁棒性。
{"title":"Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems","authors":"David A. Najera-Flores, Justin Jacobs, D. Quinn, Anthony Garland, Michael D. Todd","doi":"10.1115/1.4066054","DOIUrl":"https://doi.org/10.1115/1.4066054","url":null,"abstract":"\u0000 Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural state. However, these models-which may be thought of as a form of a digital twin-need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. It is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800843","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}
An analytical approach is presented in this article for the random dynamic study of two parallel interfacial cracks in a functionally graded material (FGM) strip that is bonded between two distinct elastic strips. One of the parallel cracks is placed at the interface of the elastic strip I and the FGM strip, and another is at the interface of the FGM strip and elastic strip II. A stationary stochastic process of time is used to model the dynamic loadings that are applied to the crack faces. To find the solution, the FGM strip is splited into a number of sub-strips, and using an average method, the material properties of each sub-strip are reduced to random variables. A fundamental problem is formulated to find the solution. The boundary conditions are reduced to a set of singular integral equations employing the Fourier sine, Fourier cosine, and Laplace transforms, which are solved by using the Collocation method. Further, the analytical expressions of dynamic stress intensity factors (DSIFs) about the crack tips in the time domain are obtained with the help of the Improved Dubner and Abate's method. Finally, the Monte Carlo method is used to obtain the mathematical expectation and standard deviation of DSIFs. The outcomes of the present study are also verified. The unique aspect of this study is the pictorial illustration of mathematical expectation and standard deviation as functions of the number of sub-strips, functionally graded parameter, thickness of the strips, and length of parallel interfacial cracks.
本文提出了一种分析方法，用于对粘结在两个不同弹性条带之间的功能分级材料（FGM）条带中的两条平行界面裂缝进行随机动态研究。其中一条平行裂缝位于弹性条带 I 和 FGM 条带的界面上，另一条位于 FGM 条带和弹性条带 II 的界面上。使用时间静态随机过程来模拟施加在裂缝面上的动态载荷。为了求解，将 FGM 带分割成若干子带，并使用平均法将每个子带的材料属性简化为随机变量。为了求解，提出了一个基本问题。利用傅立叶正弦、傅立叶余弦和拉普拉斯变换，将边界条件简化为一组奇异积分方程，并通过拼合法求解。此外，在改进的 Dubner 和 Abate 方法的帮助下，还获得了时域中裂纹尖端动态应力强度因子 (DSIF) 的分析表达式。最后，使用蒙特卡罗方法获得了 DSIF 的数学期望值和标准偏差。本研究的结果也得到了验证。本研究的独特之处在于以图解的方式说明了数学期望和标准偏差与子条带数量、功能分级参数、条带厚度和平行界面裂缝长度的函数关系。
{"title":"Random Dynamic Responses of Two Parallel Interfacial Cracks Between A Functionally Graded Material Strip And Two Dissimilar Elastic Strips","authors":"Ritika Singh","doi":"10.1115/1.4065930","DOIUrl":"https://doi.org/10.1115/1.4065930","url":null,"abstract":"\u0000 An analytical approach is presented in this article for the random dynamic study of two parallel interfacial cracks in a functionally graded material (FGM) strip that is bonded between two distinct elastic strips. One of the parallel cracks is placed at the interface of the elastic strip I and the FGM strip, and another is at the interface of the FGM strip and elastic strip II. A stationary stochastic process of time is used to model the dynamic loadings that are applied to the crack faces. To find the solution, the FGM strip is splited into a number of sub-strips, and using an average method, the material properties of each sub-strip are reduced to random variables. A fundamental problem is formulated to find the solution. The boundary conditions are reduced to a set of singular integral equations employing the Fourier sine, Fourier cosine, and Laplace transforms, which are solved by using the Collocation method. Further, the analytical expressions of dynamic stress intensity factors (DSIFs) about the crack tips in the time domain are obtained with the help of the Improved Dubner and Abate's method. Finally, the Monte Carlo method is used to obtain the mathematical expectation and standard deviation of DSIFs. The outcomes of the present study are also verified. The unique aspect of this study is the pictorial illustration of mathematical expectation and standard deviation as functions of the number of sub-strips, functionally graded parameter, thickness of the strips, and length of parallel interfacial cracks.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649142","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}
Multi-fidelity surrogate modeling offers a cost-effective approach to reduce extensive evaluations of expensive physics-based simulations for reliability predictions. However, considering spatial uncertainties in multi-fidelity surrogate modeling remains extremely challenging due to the curse of dimensionality. To address this challenge, this paper introduces a deep learning-based multi-fidelity surrogate modeling approach that fuses multi-fidelity datasets for high-dimensional reliability analysis of complex structures. It first involves a heterogeneous dimension transformation approach to bridge the gap in terms of input format between the low-fidelity and high-fidelity domains. Then, an explainable deep convolutional dimension-reduction network is proposed to effectively reduce the dimensionality of the structural reliability problems. To obtain a meaningful low dimensional space, a new knowledge reasoning-based loss regularization mechanism is integrated with the covariance matrix adaptation evolution strategy to encourage an unbiased linear pattern in the latent space for reliability predictions. Then, the high-fidelity data can be utilized for bias modeling using Gaussian process regression. Finally, Monte Carlo simulation is employed for the propagation of high-dimensional spatial uncertainties. Two structural examples are utilized to validate the effectiveness of the proposed method.
{"title":"Deep Learning-Based Multi-Fidelity Surrogate Modeling for High Dimensional Reliability Prediction","authors":"Luojie Shi, Baisong Pan, Weile Chen, Zequn Wang","doi":"10.1115/1.4065846","DOIUrl":"https://doi.org/10.1115/1.4065846","url":null,"abstract":"\u0000 Multi-fidelity surrogate modeling offers a cost-effective approach to reduce extensive evaluations of expensive physics-based simulations for reliability predictions. However, considering spatial uncertainties in multi-fidelity surrogate modeling remains extremely challenging due to the curse of dimensionality. To address this challenge, this paper introduces a deep learning-based multi-fidelity surrogate modeling approach that fuses multi-fidelity datasets for high-dimensional reliability analysis of complex structures. It first involves a heterogeneous dimension transformation approach to bridge the gap in terms of input format between the low-fidelity and high-fidelity domains. Then, an explainable deep convolutional dimension-reduction network is proposed to effectively reduce the dimensionality of the structural reliability problems. To obtain a meaningful low dimensional space, a new knowledge reasoning-based loss regularization mechanism is integrated with the covariance matrix adaptation evolution strategy to encourage an unbiased linear pattern in the latent space for reliability predictions. Then, the high-fidelity data can be utilized for bias modeling using Gaussian process regression. Finally, Monte Carlo simulation is employed for the propagation of high-dimensional spatial uncertainties. Two structural examples are utilized to validate the effectiveness of the proposed method.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687565","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}
Physics-based multi-scale corrosion simulation plays a vital role in predicting the evolution of pitting corrosion on large civil infrastructure such as miter gates, contributing to a model-informed structural health monitoring (SHM) strategy for risk-based asset health management. The physics-based analysis, however, may not accurately reflect the underlying true physics due to various uncertainty sources and needs to be updated using Bayesian inference methods based on observations to make the prediction closer to field observations. However, traditional Bayesian inference requires the evaluation of a likelihood function, which is often unavailable due to the complex model architecture and various surrogate models used in the analysis. Therefore, likelihood-free inference approaches are required for the updating of the multi-scale corrosion simulation models. This paper meets this need by proposing a conditional invertible neural network (cINN)-based Bayesian model updating method for an existing corrosion simulation model. We first train an cINN model based on simulated observations generated from a high-fidelity forward corrosion analysis model. A convolutional neural network (CNN)-based feature extraction algorithm is then employed to extract key features from corrosion images. After that, the extracted corrosion features from CNN is used as inputs of the cINN model to directly obtain posterior distributions of uncertain corrosion model parameters without evaluating the likelihood function. The results show that the proposed cINN-based model updating approach can provide more accurate inference results with a reduced computational cost in comparison to the classical approximate Bayesian computation (ABC) approach.
{"title":"Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks","authors":"Gu Qian, Jice Zeng, Zhen Hu, Michael Todd","doi":"10.1115/1.4065845","DOIUrl":"https://doi.org/10.1115/1.4065845","url":null,"abstract":"\u0000 Physics-based multi-scale corrosion simulation plays a vital role in predicting the evolution of pitting corrosion on large civil infrastructure such as miter gates, contributing to a model-informed structural health monitoring (SHM) strategy for risk-based asset health management. The physics-based analysis, however, may not accurately reflect the underlying true physics due to various uncertainty sources and needs to be updated using Bayesian inference methods based on observations to make the prediction closer to field observations. However, traditional Bayesian inference requires the evaluation of a likelihood function, which is often unavailable due to the complex model architecture and various surrogate models used in the analysis. Therefore, likelihood-free inference approaches are required for the updating of the multi-scale corrosion simulation models. This paper meets this need by proposing a conditional invertible neural network (cINN)-based Bayesian model updating method for an existing corrosion simulation model. We first train an cINN model based on simulated observations generated from a high-fidelity forward corrosion analysis model. A convolutional neural network (CNN)-based feature extraction algorithm is then employed to extract key features from corrosion images. After that, the extracted corrosion features from CNN is used as inputs of the cINN model to directly obtain posterior distributions of uncertain corrosion model parameters without evaluating the likelihood function. The results show that the proposed cINN-based model updating approach can provide more accurate inference results with a reduced computational cost in comparison to the classical approximate Bayesian computation (ABC) approach.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684241","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}
{"title":"Special Issue on Probabilistic Digital Twins in Additive Manufacturing","authors":"Zequn Wang, Zhen Hu, Moon Seung Ki, Qi Zhou, Hong-Zhong Huang","doi":"10.1115/1.4065929","DOIUrl":"https://doi.org/10.1115/1.4065929","url":null,"abstract":"","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708890","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}
Yiping Wang, Xixi Geng, Pengfei Ma, Deren Zhang, Hongzheng Shi, Junyu Li, Weibing Peng, Yi Zhang
Road health monitoring systems aim to obtain the technical indexes of roads, and then analyze the usage and the degree of damage of the roads, which can provide an important basis for road construction, maintenance, and management. Road roughness is one of the main technical indexes for road quality evaluation and road health monitoring. This study built a system and implemented it as an application to measure and analyze road longitudinal profiles simply and conveniently using the sensors in a mobile phone. The application uses the accelerometer sensor and the gravity sensor to obtain vertical acceleration by a projection method, then denoises through empirical mode decompositions and a Butterworth filter, which has a repeated measurement error of 11%. Different filters were compared and the repeatability, accuracy, robustness, and effectiveness of the system were analyzed. An index to evaluated road longitudinal profiles is given, so that the results can be analyzed and viewed interactively in the application, and a series of cases are given in this paper.
{"title":"An Android Sensors-Based Portable Road Health Monitoring System Utilizing Measurement Uncertainty Analysis","authors":"Yiping Wang, Xixi Geng, Pengfei Ma, Deren Zhang, Hongzheng Shi, Junyu Li, Weibing Peng, Yi Zhang","doi":"10.1115/1.4065664","DOIUrl":"https://doi.org/10.1115/1.4065664","url":null,"abstract":"\u0000 Road health monitoring systems aim to obtain the technical indexes of roads, and then analyze the usage and the degree of damage of the roads, which can provide an important basis for road construction, maintenance, and management. Road roughness is one of the main technical indexes for road quality evaluation and road health monitoring. This study built a system and implemented it as an application to measure and analyze road longitudinal profiles simply and conveniently using the sensors in a mobile phone. The application uses the accelerometer sensor and the gravity sensor to obtain vertical acceleration by a projection method, then denoises through empirical mode decompositions and a Butterworth filter, which has a repeated measurement error of 11%. Different filters were compared and the repeatability, accuracy, robustness, and effectiveness of the system were analyzed. An index to evaluated road longitudinal profiles is given, so that the results can be analyzed and viewed interactively in the application, and a series of cases are given in this paper.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267018","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 transition to Industry 5.0 begins with the integration of the human aspect into Industry 4.0 technologies. Industry 5.0 is a human-centric design approach that aims to overcome the issues raised by Industry 4.0 and involves collaborating both with humans and robots in a shared working environment. The new idea demonstrates a great connection between technology and people, or “soft” sectors. At this point, the idea of a digital twin, a novel technological innovation, appears. The digital twin is a newly developed technology that is essential for digital transformation and intelligent updates. The fundamental basis of this concept involves the amalgamation of artificial intelligence (AI) with the notion of digital twins, which refer to virtual renditions of tangible entities, systems, or procedures. Therefore, this article focuses on digital twins and the innovative concept of Human Digital Twins, with particular emphasis on the technological tools of AI in the usage of mentioned technology. Also, this article conducts a comprehensive PESTLE analysis of Industry 5.0, while specifically delving into the concepts of Digital Twin and Human Digital Twin.
{"title":"Smart & Digital World: The Technologies Needed for Digital Twins and Human Digital Twins","authors":"Atıl Emre Coşgun","doi":"10.1115/1.4065643","DOIUrl":"https://doi.org/10.1115/1.4065643","url":null,"abstract":"\u0000 The transition to Industry 5.0 begins with the integration of the human aspect into Industry 4.0 technologies. Industry 5.0 is a human-centric design approach that aims to overcome the issues raised by Industry 4.0 and involves collaborating both with humans and robots in a shared working environment. The new idea demonstrates a great connection between technology and people, or “soft” sectors. At this point, the idea of a digital twin, a novel technological innovation, appears. The digital twin is a newly developed technology that is essential for digital transformation and intelligent updates. The fundamental basis of this concept involves the amalgamation of artificial intelligence (AI) with the notion of digital twins, which refer to virtual renditions of tangible entities, systems, or procedures. Therefore, this article focuses on digital twins and the innovative concept of Human Digital Twins, with particular emphasis on the technological tools of AI in the usage of mentioned technology. Also, this article conducts a comprehensive PESTLE analysis of Industry 5.0, while specifically delving into the concepts of Digital Twin and Human Digital Twin.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272608","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}
With the blooming of the electric vehicle market and the advancement in the lithium-ion battery industry, silicon anode has shown great potential for the next-generation battery. Using the state-of-the-art additive manufacturing technique (three-dimensional holographic lithography), researchers have demonstrated that silicon anode can be fabricated as a three-dimensional bicontinuous porous microstructure. However, the volume fluctuation of the silicon anode caused by lithiation during the discharging process causes continuous capacity decay and poor cycling life. Besides, uncertainties are inherent in the manufacturing and usage processes, making it crucial to systematically consider them in the silicon anode design to improve its performance and reliability. To fill the gap between current silicon anode research and future industrial need, this study established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. This study started with developing multiphysics finite element models of the silicon anode lithiation process to investigate the volume fluctuation of silicon. Then, surrogate models were built based on the results from the finite element models to reduce computational cost. The reliability-based design optimization was employed to find the best design point for the silicon anode, in which an outer optimization loop maximized the objective function and an inner loop dedicated to reliability analysis. Finally, the Pareto optimal front of the silicon anode designs was obtained and validated, which shows over 10% improvements in the silicon anode's total capacity and rate capability.
{"title":"Reliability-Based Design Optimization of Additive Manufacturing for Lithium Battery Silicon Anode","authors":"Zheng Liu, Hao Wu, Pingfeng Wang, Yumeng Li","doi":"10.1115/1.4065530","DOIUrl":"https://doi.org/10.1115/1.4065530","url":null,"abstract":"\u0000 With the blooming of the electric vehicle market and the advancement in the lithium-ion battery industry, silicon anode has shown great potential for the next-generation battery. Using the state-of-the-art additive manufacturing technique (three-dimensional holographic lithography), researchers have demonstrated that silicon anode can be fabricated as a three-dimensional bicontinuous porous microstructure. However, the volume fluctuation of the silicon anode caused by lithiation during the discharging process causes continuous capacity decay and poor cycling life. Besides, uncertainties are inherent in the manufacturing and usage processes, making it crucial to systematically consider them in the silicon anode design to improve its performance and reliability. To fill the gap between current silicon anode research and future industrial need, this study established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. This study started with developing multiphysics finite element models of the silicon anode lithiation process to investigate the volume fluctuation of silicon. Then, surrogate models were built based on the results from the finite element models to reduce computational cost. The reliability-based design optimization was employed to find the best design point for the silicon anode, in which an outer optimization loop maximized the objective function and an inner loop dedicated to reliability analysis. Finally, the Pareto optimal front of the silicon anode designs was obtained and validated, which shows over 10% improvements in the silicon anode's total capacity and rate capability.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966050","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 Human Error Probabilities (HEP) can be estimated using multipliers that correspond to the level of Performance Shaping Factors (PSFs) in the Human Reliability Analysis (HRA). This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. Markov Chain Monte Carlo (MCMC) and Bayesian Monte Carlo (BMC) are used as Bayesian inference methods based on Monte Carlo techniques. MCMC is utilized to obtain the posterior distribution of the multipliers. BMC is used for the estimation of the moments of the posterior distribution such as the mean and variance. The results obtained by MCMC and that by BMC well agree with the reference results. As a case study, the data assimilation was performed using the results of the simulator experiment of Halden reactor. The results show that the multiplier changes by the result of a particular scenario and HEP of another scenario that uses the same multiplier also changes by data assimilation. Also, in the case study, the correlation between multipliers is obtained by the data assimilation and the correlation contributes to the reduction of uncertainty of HEP.
人的失误概率（HEP）可以使用乘数来估算，乘数与人的可靠性分析（HRA）中的性能影响因素（PSF）水平相对应。本文的重点是利用模拟器的实验结果，通过基于蒙特卡洛技术的贝叶斯推理对乘数进行调整。马尔可夫链蒙特卡洛（MCMC）和贝叶斯蒙特卡洛（BMC）是基于蒙特卡洛技术的贝叶斯推理方法。MCMC 用于获得乘数的后验分布。BMC 用于估计后验分布的矩，如均值和方差。MCMC 和 BMC 得出的结果与参考结果完全一致。作为案例研究，我们使用哈尔登反应堆模拟器实验的结果进行了数据同化。结果表明，乘数会因特定方案的结果而改变，而使用相同乘数的另一方案的 HEP 也会因数据同化而改变。此外，在案例研究中，乘数之间的相关性是通过数据同化获得的，这种相关性有助于降低 HEP 的不确定性。
{"title":"Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor","authors":"Satoshi Takeda, Takanori Kitada","doi":"10.1115/1.4065531","DOIUrl":"https://doi.org/10.1115/1.4065531","url":null,"abstract":"\u0000 The Human Error Probabilities (HEP) can be estimated using multipliers that correspond to the level of Performance Shaping Factors (PSFs) in the Human Reliability Analysis (HRA). This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. Markov Chain Monte Carlo (MCMC) and Bayesian Monte Carlo (BMC) are used as Bayesian inference methods based on Monte Carlo techniques. MCMC is utilized to obtain the posterior distribution of the multipliers. BMC is used for the estimation of the moments of the posterior distribution such as the mean and variance. The results obtained by MCMC and that by BMC well agree with the reference results. As a case study, the data assimilation was performed using the results of the simulator experiment of Halden reactor. The results show that the multiplier changes by the result of a particular scenario and HEP of another scenario that uses the same multiplier also changes by data assimilation. Also, in the case study, the correlation between multipliers is obtained by the data assimilation and the correlation contributes to the reduction of uncertainty of HEP.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965357","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}
Wind energy harvesters are usually designed to operate in the low wind speed range. They rely on smaller swept areas, as a complement to larger horizontal-axis wind turbines. A torsional-flutter-based apparatus is investigated herein to extract wind energy. A nonlinear hybrid restoring toque mechanism, installed at equally spaced supports, is used to produce energy through limit-cycle vibration. Energy conversion and storage from the wind flow are enabled by eddy currents. The apparatus is used during thunderstorm outflows to explore the efficiency in non-ideal wind conditions. The thunderstorm flow model accounts for both non-stationary turbulence and slowly varying mean speed, replicating thunderstorm's intensification and decay stages. This paper evolves from a recent study to examine stochastic stability. More Specifically, the output power is a random process that is derived numerically. Various thunderstorm features and variable apparatus configurations are evaluated. Numerical investigations confirm the detrimental effect of non-ideal, thunderstorms on harvester performance with, on average, an adverse increment of operational speed (about +30%). Besides nonlinear damping, the benign flutter-prone effect is controlled by the square of the flapping angle. Since flapping amplitudes are moderate at sustained flutter, activation of the apparatus is delayed and exacerbated by the non-stationary outflow and aeroelastic load features. Finally, efficiency is carefully investigated by quantification of output power and “quality factor”.
{"title":"Improving Output Power of a Torsional-Flutter Harvester in Stochastic Thunderstorms by Duffing - Van Der Pol Restoring Torque","authors":"Luca Caracoglia","doi":"10.1115/1.4065532","DOIUrl":"https://doi.org/10.1115/1.4065532","url":null,"abstract":"\u0000 Wind energy harvesters are usually designed to operate in the low wind speed range. They rely on smaller swept areas, as a complement to larger horizontal-axis wind turbines. A torsional-flutter-based apparatus is investigated herein to extract wind energy. A nonlinear hybrid restoring toque mechanism, installed at equally spaced supports, is used to produce energy through limit-cycle vibration. Energy conversion and storage from the wind flow are enabled by eddy currents. The apparatus is used during thunderstorm outflows to explore the efficiency in non-ideal wind conditions. The thunderstorm flow model accounts for both non-stationary turbulence and slowly varying mean speed, replicating thunderstorm's intensification and decay stages. This paper evolves from a recent study to examine stochastic stability. More Specifically, the output power is a random process that is derived numerically. Various thunderstorm features and variable apparatus configurations are evaluated. Numerical investigations confirm the detrimental effect of non-ideal, thunderstorms on harvester performance with, on average, an adverse increment of operational speed (about +30%). Besides nonlinear damping, the benign flutter-prone effect is controlled by the square of the flapping angle. Since flapping amplitudes are moderate at sustained flutter, activation of the apparatus is delayed and exacerbated by the non-stationary outflow and aeroelastic load features. Finally, efficiency is carefully investigated by quantification of output power and “quality factor”.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963699","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}
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