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SENSITIVITY ANALYSES OF A MULTI-PHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS 蒸汽发生器多物理场长期堵塞模型的敏感性分析
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-03-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051489
Vincent Chabridon, Edgar Jaber, Emmanuel Remy, Michaël Baudin, Didier Lucor, Mathilde Mougeot, Bertrand Iooss
Long-term operation of nuclear steam generators can result in the occurrence of clogging, a deposition phenomenon that may increase the risk of mechanical and vibration loadings on tube bundles and internal structures as well as potentially affecting their response to hypothetical accidental transients.To manage and prevent this issue, a robust maintenance program that requires a fine understanding of the underlying physics is essential. This study focuses on the utilizationof a clogging simulation code developed by EDF R&D. This numerical tool employs specific physical models to simulate the kinetics of clogging and generates time dependent clogging rate profiles for particular steam generators. However,certain parameters in this code are subject to uncertainties. To address these uncertainties, Monte Carlo simulations are conducted to assess the distribution of the clogging rate. Subsequently, polynomial chaos expansions are used inorder to build a metamodel while time-dependent Sobol’ indices are computed to understand the impact of the random input parameters throughout the whole operating time. Comparisons are made with a previous published study andadditional Hilbert-Schmidt independence criterion sensitivity indices are computed. Key input-output dependencies are exhibited in the different chemical conditionings and new behavior patterns in high-pH regimes are uncovered by the sensitivity analysis. These findings contribute to a better understanding of the clogging phenomenon while openingfuture lines of modeling research and helping in robustifying maintenance planning.
核蒸汽发生器的长期运行可能会导致堵塞现象的发生,这种沉积现象可能会增加管束和内部结构承受机械和振动负荷的风险,并可能影响其对假定事故瞬态的响应。本研究的重点是利用 EDF R&D 开发的堵塞模拟代码。该数值工具采用特定的物理模型模拟堵塞动力学,并生成特定蒸汽发生器随时间变化的堵塞率曲线。然而,该代码中的某些参数存在不确定性。为了解决这些不确定性,我们进行了蒙特卡罗模拟,以评估堵塞率的分布。随后,使用多项式混沌展开建立元模型,同时计算随时间变化的索布尔指数,以了解随机输入参数在整个运行时间内的影响。与之前发表的一项研究进行了比较,并计算了额外的希尔伯特-施密特独立标准敏感性指数。敏感性分析显示了不同化学条件下关键的输入输出依赖关系,并揭示了高pH条件下的新行为模式。这些发现有助于更好地理解堵塞现象,同时开辟了未来的建模研究方向,并有助于加强维护规划。
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引用次数: 0
Application of global sensitivity analysis for identification of probabilistic design spaces 应用全局敏感性分析确定概率设计空间
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-02-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051384
Sergei Kucherenko, Dimitris Giamalakis, Nilay Shah
The design space (DS) is defined as the combination of materials and process conditions, which provides assurance of quality for a pharmaceutical product. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space. We demonstrate that application of global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying DS by screening out non-important uncertain parameters. The novelty of this approach in that the usage of an indicator function which takes only binary values as a model function allows to apply a straightforward GSA based on Sobol’ sensitivity indices and to avoid using more costly Monte Carlo filtering or GSA for constrained problems. We consider an application from the chemical industry to illustrate how this formulation results in model reduction and dramatic reduction of the number of required model runs.
设计空间(DS)被定义为材料和工艺条件的组合,它为药品的质量提供了保证。采用基于模型的方法来确定基于概率的 DS,需要在整个工艺参数空间(确定的)和不确定的模型参数空间内进行代价高昂的模拟。我们证明,应用全局灵敏度分析 (GSA) 可以显著降低模型的复杂性,并通过筛选出不重要的不确定参数来减少识别和量化 DS 的计算时间。这种方法的新颖之处在于,使用只取二进制值的指标函数作为模型函数,可以直接应用基于索博尔灵敏度指数的 GSA,并避免使用成本更高的蒙特卡罗过滤或 GSA 来处理受限问题。我们考虑了化学工业中的一个应用,以说明这种表述方式如何减少了模型,并显著减少了所需的模型运行次数。
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引用次数: 0
Stochastic Galerkin method and port-Hamiltonian form for linear first-order ordinary differential equations 线性一阶常微分方程的随机伽勒金法和端口-哈密顿形式
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-02-01 DOI: 10.1615/int.j.uncertaintyquantification.2024050099
Roland Pulch, Olivier Sète
We consider linear first-order systems of ordinary differential equations (ODEs) in port-Hamiltonian (pH) form. Physical parameters are remodelled as random variables to conduct an uncertainty quantification. A stochastic Galerkin projection yields a larger deterministic system of ODEs, which does not exhibit a pH form in general. We apply transformations of the original systems such that the stochastic Galerkin projection becomes structure-preserving. Furthermore, we investigate meaning and properties of the Hamiltonian function belonging to the stochastic Galerkin system. A large number of random variables implies a high-dimensional stochastic Galerkin system, which suggests itself to apply model order reduction (MOR) generating a low-dimensional system of ODEs. We discuss structure preservation in projection-based MOR, where the smaller systems of ODEs feature pH form again. Results of numerical computations are presented using two test examples.
我们考虑的是端口-哈密尔顿(pH)形式的线性一阶常微分方程(ODE)系统。物理参数被重塑为随机变量,以进行不确定性量化。随机伽勒金投影产生了一个更大的确定性 ODE 系统,该系统一般不呈现 pH 形式。我们对原始系统进行了变换,从而使随机伽勒金投影变得具有结构保护性。此外,我们还研究了属于随机 Galerkin 系统的哈密顿函数的含义和性质。大量的随机变量意味着一个高维的随机 Galerkin 系统,这就建议应用模型阶次削减(MOR)生成一个低维的 ODE 系统。我们讨论了基于投影的 MOR 中的结构保持,其中较小的 ODE 系统再次以 pH 形式为特征。通过两个测试实例介绍了数值计算的结果。
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引用次数: 0
Uncertainty quantification and global sensitivity analysis of seismic fragility curves using kriging 利用克里格法对地震脆性曲线进行不确定性量化和全球敏感性分析
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2023046480
Clement Gauchy, Cyril Feau, Josselin Garnier
Seismic fragility curves have been introduced as key components of Seismic Probabilistic Risk Assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into account the inherent uncertainties in such studies, the so-called epistemic uncertainties (i.e. coming from the uncertainty on the mechanical parameters of the structure) and the aleatory uncertainties (i.e. coming from the randomness of the seismic ground motions). For simulation-based approaches we propose a methodology to build and calibrate a Gaussian process surrogate model to estimate a family of non-parametric seismic fragility curves for a mechanical structure by propagating both the surrogate model uncertainty and the epistemic ones. Gaussian processes have indeed the main advantage to propose both a predictor and an assessment of the uncertainty of its predictions. In addition, we extend this methodology to sensitivity analysis. Global sensitivity indices such as aggregated Sobol indices and kernel-based indices are proposed to know how the uncertainty on the seismic fragility curves is apportioned according to each uncertain mechanical parameter. This comprehensive Uncertainty Quantification framework is finally applied to an industrial test case consisting in a part of a piping system of a Pressurized Water Reactor.
地震脆性曲线是地震概率风险评估研究的关键组成部分。它们表示机械结构在地震烈度测量条件下的破坏概率,必须考虑到此类研究中固有的不确定性,即所谓的认识不确定性(即来自结构机械参数的不确定性)和已知不确定性(即来自地震地面运动的随机性)。对于基于模拟的方法,我们提出了一种建立和校准高斯过程代用模型的方法,通过传播代用模型的不确定性和认识的不确定性来估算机械结构的非参数地震脆性曲线系列。高斯过程的主要优点是既能提出预测模型,又能评估其预测的不确定性。此外,我们还将这一方法扩展到了敏感性分析。我们提出了全局灵敏度指数,如综合索布尔指数和基于核的指数,以了解地震脆性曲线的不确定性是如何根据每个不确定的力学参数进行分配的。这一全面的不确定性量化框架最终被应用到一个工业测试案例中,该案例包括压水堆管道系统的一部分。
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引用次数: 0
A Bayesian neural network approach to Multi-fidelity surrogate modelling 多保真度代理模型的贝叶斯神经网络方法
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-12-05 DOI: 10.1615/int.j.uncertaintyquantification.2023044584
Baptiste Kerleguer, C. Cannamela, J. Garnier
This paper deals with surrogate modelling of a computer code output in a hierarchical multi-fidelity context, i.e., when the output can be evaluated at different levels of accuracy and computational cost. Using observations of the output at low- and high-fidelity levels, we propose a method that combines Gaussian process (GP) regression and Bayesian neural network (BNN), in a method called GPBNN. The low-fidelity output is treated as a single-fidelity code using classical GP regression. The high-fidelity output is approximated by a BNN that incorporates, in addition to the high-fidelity observations, well-chosen realisations of the low-fidelity output emulator. The predictive uncertainty of the final surrogate model is then quantified by a complete characterisation of the uncertainties of the different models and their interaction. GPBNN is compared with most of the multi-fidelity regression methods allowing to quantify the prediction uncertainty.
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引用次数: 1
A domain-decomposed VAE method for Bayesian inverse problems 贝叶斯逆问题的领域分解 VAE 方法
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-12-01 DOI: 10.1615/int.j.uncertaintyquantification.2023047236
Zhihang Xu, Yingzhi Xia, Qifeng Liao
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-encoder Markov chain Monte Carlo (DD-VAE-MCMC) method to tackle these challenges simultaneously. Through partitioning the global physical domain into small subdomains, the proposed method first constructs local deterministic generative models based on local historical data, which provide efficient local prior representations.Gaussian process models with active learning address the domain decomposition interface conditions.Then inversions are conducted on each subdomain independently in parallel and in low-dimensional latent parameter spaces. The local inference solutions are post-processed through the Poisson image blending procedure to result in an efficient global inference result. Numerical examples are provided to demonstrate the performance of the proposed method.
当前瞻模型受复杂偏微分方程(PDEs)支配时,贝叶斯逆问题通常在计算上具有挑战性。这通常是由于昂贵的前向模型评估和高维的前验参数化造成的。本文提出了一种域分解变异自动编码器马尔可夫链蒙特卡罗(DD-VAE-MCMC)方法,以同时应对这些挑战。通过将全局物理域划分为小的子域,本文提出的方法首先基于本地历史数据构建本地确定性生成模型,从而提供高效的本地先验表示。通过泊松图像混合程序对局部推理解进行后处理,从而得到高效的全局推理结果。我们提供了一些数值示例来证明所提议方法的性能。
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引用次数: 0
MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS 利用皮尔逊系统进行模型误差估计,并应用于可压缩流中的非线性波
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-12-01 DOI: 10.1615/int.j.uncertaintyquantification.2023048277
Ferdinand Uilhoorn
In data assimilation, the description of the model error uncertainty is utmost important because incorrectly defined may lead to information loss about the real state of the system. In this work, we proposed a novel approach that finds the optimal distribution for describing the model error uncertainty within a particle filtering framework. The method was applied to nonlinear waves in compressible flows. We investigated the influence of observation noise statistics, resolution of the numerical model, smoothness of the solutions and sensor location. The results showed that in almost all situations the Pearson Type I is preferred, but with different curve-shape characteristics, namely, skewed, nearly symmetric, ∩-, ∪- and J-shaped. The distributions became in most cases ∪-shaped when the sensors were located nearby the discontinuities.
在数据同化过程中,对模型误差不确定性的描述至关重要,因为不正确的定义可能会导致系统真实状态信息的丢失。在这项工作中,我们提出了一种新方法,在粒子滤波框架内找到描述模型误差不确定性的最佳分布。该方法被应用于可压缩流中的非线性波。我们研究了观测噪声统计、数值模型分辨率、解的平滑度和传感器位置的影响。结果表明,几乎在所有情况下,Pearson I 型都是首选,但具有不同的曲线形状特征,即偏斜、近乎对称、∩形、∪形和 J 形。在大多数情况下,当传感器位于不连续点附近时,曲线分布呈"∪"形。
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引用次数: 0
Decision theoretic bootstrapping 决策理论引导
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-12-01 DOI: 10.1615/int.j.uncertaintyquantification.2023038552
Peyman Tavallali, Peyman Tavallali, Hamed Hamze Bajgiran, Danial Esaid, Houman Owhadi
The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution. Although these two distributions are identical and identifiable when the data set is infinite, they are imperfectly known when the data is finite (and possibly corrupted), and this uncertainty must be taken into account for robust Uncertainty Quantification (UQ). An important case is when the test distribution is coming from a modal or localized area of the finite sample distribution. We present a general decision-theoretic bootstrapping solution to this problem: (1) partition the available data into a training subset, and a UQ subset (2) take $m$ subsampled subsets of the training set and train $m$ models (3) partition the UQ set into $n$ sorted subsets and take a random fraction of them to define $n$ corresponding empirical distributions $mu_{j}$ (4) consider the adversarial game where Player I selects a model $iinleft{ 1,ldots,mright} $, Player II selects the UQ distribution $mu_{j}$ and Player I receives a loss defined by evaluating the model $i$ against data points sampled from $mu_{j}$ (5) identify optimal mixed strategies (probability distributions over models and UQ distributions) for both players. These randomized optimal mixed strategies provide optimal model mixtures, and UQ estimates given the adversarial uncertainty of the training and testing distributions represented by the game. The proposed approach provides (1) some degree of robustness to in-sample distribution localization/concentration (2) conditional probability distributions on the output.
有监督机器学习模型的设计和测试结合了两个基本分布:(1) 训练数据分布 (2) 测试数据分布。虽然这两种分布在数据集为无限时是相同且可识别的,但在数据为有限(可能已损坏)时,它们是不完全已知的,因此必须考虑到这种不确定性,以实现稳健的不确定性量化(UQ)。一个重要的情况是,测试分布来自有限样本分布的模态或局部区域。针对这一问题,我们提出了一种通用的决策理论引导解决方案:(1) 将可用数据划分为一个训练子集和一个 UQ 子集 (2) 从训练集中提取 $m$ 子采样子集并训练 $m$ 模型 (3) 将 UQ 集划分为 $n$ 排序子集并从中随机提取一部分来定义 $n$ 相应的经验分布 $mu_{j}$ (4) 考虑对抗博弈,其中玩家 I 选择一个模型 $iinleft{ 1、玩家 II 选择 UQ 分布 $mu_{j}$,玩家 I 收到损失,损失的定义是根据从 $mu_{j}$ 中采样的数据点评估模型 $i$ (5) 为两个玩家确定最优混合策略(模型和 UQ 分布的概率分布)。考虑到博弈所代表的训练和测试分布的对抗不确定性,这些随机化的最优混合策略提供了最优模型混合物和 UQ 估计值。建议的方法提供了(1)对样本内分布定位/集中的一定程度的稳健性(2)输出的条件概率分布。
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引用次数: 0
MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY 不确定条件下无导数优化的多电平蒙特卡罗估计
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-11-01 DOI: 10.1615/int.j.uncertaintyquantification.2023048049
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl, Youssef Marzouk, Michael S. Eldred, Hans-Joachim Bungartz
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected byuncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Op-timization under uncertainty (OUU) deals with this endeavor and requires uncertainty quantification analyses atseveral design locations. The cost of OUU is proportional to the cost of performing a forward uncertainty analysis ateach design location visited, which makes the computational burden too high for high-fidelity simulations with sig-nificant computational cost. From a high-level standpoint, an OUU workflow typically has two main components: aninner loop strategy for the computation of statistics of the quantity of interest, and an outer loop optimization strategytasked with finding the optimal design, given a merit function based on the inner loop statistics. In this work, wepropose to alleviate the cost of the inner loop uncertainty analysis by leveraging the so-called Multilevel Monte Carlo(MLMC) method. MLMC has the potential of drastically reducing the computational cost by allocating resources overmultiple models with varying accuracy and cost. The resource allocation problem in MLMC is formulated by mini-mizing the computational cost given a target variance for the estimator. We consider MLMC estimators for statisticsusually employed in OUU workflows and solve the corresponding allocation problem. For the outer loop, we considera derivative-free optimization strategy implemented in the SNOWPAC library; our novel strategy is implemented andreleased in the Dakota software toolkit. We discuss several n
优化是科学和工程应用的关键工具,然而,在存在受不确定性影响的模型时,优化公式需要扩展以考虑感兴趣数量的统计。不确定性优化(OUU)处理了这一问题,需要在多个设计位置进行不确定性量化分析。OUU的成本与所访问的每个设计位置执行前向不确定性分析的成本成正比,这使得计算负担过高,无法进行具有显著计算成本的高保真仿真。从高层次的角度来看,OUU工作流通常有两个主要组成部分:用于计算兴趣数量统计的内环策略,以及基于内环统计的给定价值函数的外环优化策略,其任务是找到最佳设计。在这项工作中,我们提出利用所谓的多层蒙特卡罗(MLMC)方法来减轻内环不确定性分析的成本。MLMC通过在不同精度和成本的多个模型上分配资源,具有大幅降低计算成本的潜力。MLMC中的资源分配问题是在给定估计器目标方差的情况下,将计算成本最小化。我们考虑了统计上常用于OUU工作流的MLMC估计器,并解决了相应的分配问题。对于外环,我们考虑了在SNOWPAC库中实现的无导数优化策略;我们的新策略在Dakota软件工具包中实现并发布。我们讨论几个n。
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引用次数: 0
Roles of noncoding RNAs in chronic obstructive pulmonary disease. 非编码rna在慢性阻塞性肺疾病中的作用
IF 4.9 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2023-07-05 eCollection Date: 2023-06-01 DOI: 10.2478/jtim-2023-0084
Xin Qiao, Yuxiao Ding, Abdullah Altawil, Yan Yin, Qiuyue Wang, Wei Wang, Jian Kang
Chronic obstructive pulmonary disease (COPD) is a chronic heterogeneous disease characterized by persistent airflow obstruction and variable clinical presentations.[1,2] A lack of understanding regarding the molecular mechanisms underlying COPD makes the identification of critical molecules involved in COPD crucial for the development of novel diagnostic measures and therapeutic strategies. In recent decades, wide-ranging profiling methods such as microarrays and next-generation sequencing have made it easier to identify RNA transcripts that do not encode proteins, referred to as noncoding RNAs (ncRNAs).[3] NcRNAs comprise a diverse range of RNA species, characterized according to their length, shape, and location. Many ncRNAs are involved in epigenetic and posttranscriptional gene regulation, including microRNAs (miRNAs), tRNA-derived small RNAs (tsRNAs) and PIWI-interacting RNAs (piRNAs).[4] Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) can fold into complex secondary structures that facilitate their interactions with DNA, RNA, and protein.[4] Additionally, lncRNAs and circRNAs can bind to miRNAs in a competitive endogenous RNA (ceRNA) network that prevents targeted mRNA degradation.[5,6] Recent studies have shown that ncRNAs play crucial roles in multiple pathophysiological processes associated with COPD.[5,7,8] A better understanding of the role of ncRNAs in COPD could contribute to the detection of biomarkers and the identification of new therapeutic targets. Here, we summarize the current findings regarding the potential role of ncRNAs, especially miRNAs, lncRNAs, and circRNAs. Additionally, we propose considerations regarding present and future research in this area.
慢性阻塞性肺疾病(COPD)是一种慢性异质性疾病,其特征是持续的气流阻塞和多种临床表现。[1,2]由于对慢性阻塞性肺病的分子机制缺乏了解,因此鉴定参与慢性阻塞性肺病的关键分子对于开发新的诊断措施和治疗策略至关重要。近几十年来,微阵列和下一代测序等广泛的分析方法使鉴定不编码蛋白质的RNA转录本(称为非编码RNA (ncRNAs))变得更容易。[3]NcRNAs包括各种各样的RNA物种,根据它们的长度、形状和位置来表征。许多ncrna参与表观遗传和转录后基因调控,包括microRNAs (miRNAs), trna衍生的小rna (tsRNAs)和piwi相互作用rna (piRNAs)。[4]长链非编码RNA (lncRNAs)和环状RNA (circRNAs)可以折叠成复杂的二级结构,促进它们与DNA、RNA和蛋白质的相互作用。[4]此外,lncrna和circrna可以在竞争性内源性RNA (ceRNA)网络中与mirna结合,从而阻止靶向mRNA降解。[5,6]最近的研究表明,ncrna在COPD相关的多个病理生理过程中发挥重要作用。[5,7,8]更好地了解ncrna在COPD中的作用有助于检测生物标志物和确定新的治疗靶点。在这里,我们总结了目前关于ncrna,特别是mirna、lncrna和circrna的潜在作用的研究结果。此外,我们还就该领域当前和未来的研究提出了几点考虑。
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引用次数: 0
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International Journal for Uncertainty Quantification
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