scholarly journals Reduction of Epistemic Model Uncertainty in Simulation-Based Multidisciplinary Design

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Zhen Jiang ◽  
Shishi Chen ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Resource Allocation ◽  
Model Uncertainty ◽  
Epistemic Uncertainty ◽  
Complex Structure ◽  
Data Gathering ◽  
Simulation Models ◽  
Multidisciplinary Design ◽  
Novel Approach ◽  
Multidisciplinary System ◽  
Epistemic Model

Model uncertainty is a significant source of epistemic uncertainty that affects the prediction of a multidisciplinary system. In order to achieve a reliable design, it is critical to ensure that the disciplinary/subsystem simulation models are trustworthy, so that the aggregated uncertainty of system quantities of interest (QOIs) is acceptable. Reduction of model uncertainty can be achieved by gathering additional experiments and simulations data; however, resource allocation for multidisciplinary design optimization (MDO) and analysis remains a challenging task due to the complex structure of the system, which involves decision makings about where (sampling locations), what (disciplinary responses), and which type (simulations versus experiments) for allocating more resources. Instead of trying to concurrently make the above decisions, which would be generally intractable, we develop a novel approach in this paper to break the decision making into a sequential procedure. First, a multidisciplinary uncertainty analysis (MUA) is developed to identify the input settings with unacceptable amounts of uncertainty with respect to the system QOIs. Next, a multidisciplinary statistical sensitivity analysis (MSSA) is developed to investigate the relative contributions of (functional) disciplinary responses to the uncertainty of system QOIs. The input settings and critical responses to allocate resources are selected based on the results from MUA and MSSA, with the aid of a new correlation analysis derived from spatial-random-process (SRP) modeling concepts, ensuring the sparsity of the selected inputs. Finally, an enhanced preposterior analysis predicts the effectiveness of allocating experimental and/or computational resource to answer the question about which type of resource to allocate. The proposed method is applied to a benchmark electronic packaging problem to demonstrate how epistemic model uncertainty is gradually reduced via resource allocation for data gathering.

Resource Allocation for Reduction of Epistemic Uncertainty in Simulation-Based Multidisciplinary Design

2015 ◽  
Author(s):  
Zhen Jiang ◽  
Shishi Chen ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Resource Allocation ◽  
Epistemic Uncertainty ◽  
Complex Structure ◽  
Simulation Models ◽  
Multidisciplinary Design ◽  
Input Space ◽  
Novel Approach ◽  
Critical Responses ◽  
Reliable Design ◽  
Multidisciplinary System

Epistemic model uncertainty is a significant source of uncertainty that affects a multidisciplinary system. In order to achieve a reliable design, it is critical to ensure that the disciplinary/subsystem simulation models are trustworthy, so that the aggregated uncertainty of system quantities of interest (QOIs) is acceptable. Uncertainty reduction can be achieved by gathering additional experiments and simulations data; however resource allocation for multidisciplinary design optimization (MDO) remains a challenging task due to the complex structure of a multidisciplinary system. In this paper, we develop a novel approach by integrating multidisciplinary uncertainty analysis (MUA) and multidisciplinary statistical sensitivity analysis (MSSA) to answer the questions about where (sampling locations), what (disciplinary responses), and which (simulations versus experiments) for allocating more resources. To manage the complexity in making the above decisions, a sequential procedure is proposed. First, the input space of a multidiscipline system is explored to identify the locations with unacceptable amounts of uncertainty with respect to the system QOIs. Next, these input locations are selected through a correlation check so that they are sparsely located in the input space, and their corresponding critical responses are identified based on MSSA. Finally, using a preposterior analysis, decisions are made about what type of resources (experimental or computational) should be allocated to the critical responses at the chosen input locations. The proposed method is applied to a benchmark electronic packaging problem to demonstrate how epistemic uncertainty is gradually reduced via gathering more data.

A System Uncertainty Propagation Approach With Model Uncertainty Quantification in Multidisciplinary Design

2014 ◽  
Author(s):  
Zhen Jiang ◽  
Wei Li ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Uncertainty Quantification ◽  
Model Uncertainty ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Statistical Moments ◽  
Multidisciplinary Design ◽  
Design System ◽  
System Uncertainty ◽  
The Impact ◽  
Multidisciplinary System

The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g. input variability) or epistemic (e.g. model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplinary design system, uncertainty propagation refers to the analysis that quantifies the overall uncertainty of system QOIs resulting from all sources of aleatory and epistemic uncertainty originating in the individual disciplines. However, due to the complexity of multidisciplinary simulation, especially the coupling relationships between individual disciplines, many uncertainty propagation approaches in the existing literature only consider aleatory uncertainty and ignore the impact of epistemic uncertainty. In this paper, we address the issue of efficient uncertainty quantification of system QOIs considering both aleatory and epistemic uncertainties. We propose a spatial-random-process (SRP) based multidisciplinary uncertainty analysis (MUA) method that, subsequent to SRP-based disciplinary model uncertainty quantification, fully utilizes the structure of SRP emulators and leads to compact analytical formulas for assessing statistical moments of uncertain QOIs. The proposed method is applied to a benchmark electronics packaging problem. To demonstrate the effectiveness of the method, the estimated low-order statistical moments of the QOIs are compared to the results from Monte Carlo simulations.

Deciding Degree of Conservativeness in Initial Design Considering Risk of Future Redesign

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Nathaniel B. Price ◽  
Nam-Ho Kim ◽  
Raphael T. Haftka ◽  
Mathieu Balesdent ◽  
Sébastien Defoort ◽  
...  
Keyword(s):  
Design Process ◽  
Model Uncertainty ◽  
Epistemic Uncertainty ◽  
Design Stage ◽  
High Fidelity ◽  
Design Performance ◽  
Initial Design ◽  
Final Design ◽  
Fidelity Model ◽  
Epistemic Model

Early in the design process, there is often mixed epistemic model uncertainty and aleatory parameter uncertainty. Later in the design process, the results of high-fidelity simulations or experiments will reduce epistemic model uncertainty and may trigger a redesign process. Redesign is undesirable because it is associated with costs and delays; however, it is also an opportunity to correct a dangerous design or possibly improve design performance. In this study, we propose a margin-based design/redesign method where the design is optimized deterministically, but the margins are selected probabilistically. The final design is an epistemic random variable (i.e., it is unknown at the initial design stage) and the margins are optimized to control the epistemic uncertainty in the final design, design performance, and probability of failure. The method allows for the tradeoff between expected final design performance and probability of redesign while ensuring reliability with respect to mixed uncertainties. The method is demonstrated on a simple bar problem and then on an engine design problem. The examples are used to investigate the dilemma of whether to start with a higher margin and redesign if the test later in the design process reveals the design to be too conservative, or to start with a lower margin and redesign if the test reveals the design to be unsafe. In the examples in this study, it is found that this decision is related to the variance of the uncertainty in the high-fidelity model relative to the variance of the uncertainty in the low-fidelity model.

A Reliability-Based Multidisciplinary Design Optimization Method with Evidence Theory and Probability Theory

2018 ◽  
Vol 25 (01) ◽  
pp. 1850003 ◽  
Author(s):  
Chen Guoqiang ◽  
Tan Jianping ◽  
Tao Yourui
Keyword(s):  
Probability Theory ◽  
Design Optimization ◽  
Reliability Analysis ◽  
Computational Efficiency ◽  
Multidisciplinary Design Optimization ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Optimization Method ◽  
Multidisciplinary Design ◽  
Multidisciplinary System

Uncertainties, including aleatory and epistemic uncertainties, always exist in multidisciplinary system. Due to the discontinuous nature of epistemic uncertainty and the complex coupled relation among subsystems, the computational efficiency of reliability-based multidisciplinary design optimization (RBMDO) with mixed aleatory and epistemic uncertainties is extremely low. A novel RBMDO procedure is presented in this paper based on combined probability theory and evidence theory (ET) to deal with hybrid-uncertainties and improve the computational efficiency. Firstly, based on Bayes method, a novel method to define the probability density function of the aleatory variables is proposed. Secondly, the conventional equivalent normal method (J-C method) is modified to reliability analysis with hybrid-uncertainties. Finally, a novel RBMDO procedure is suggested by integrating the modified J-C method into the frame of sequence optimization and reliability analysis (SORA). Numerical examples and engineering example are applied to demonstrate the performance of the proposed method. The examples show the excellence of the RBMDO method both in computational efficiency and accuracy. The proposed method provides a practical and effective reliability design method for multidisciplinary system.

A Spatial-Random-Process Based Multidisciplinary System Uncertainty Propagation Approach With Model Uncertainty

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Zhen Jiang ◽  
Wei Li ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Uncertainty Quantification ◽  
Random Process ◽  
Model Uncertainty ◽  
Electronic Packaging ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Statistical Moments ◽  
Robust Design Optimization ◽  
System Uncertainty ◽  
Multidisciplinary System

The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g., input variability) or epistemic (e.g., model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplinary design system, uncertainty propagation (UP) refers to the analysis that quantifies the overall uncertainty of system QOIs resulting from all sources of aleatory and epistemic uncertainty originating in the individual disciplines. However, due to the complexity of multidisciplinary simulation, especially the coupling relationships between individual disciplines, many UP approaches in the existing literature only consider aleatory uncertainty and ignore the impact of epistemic uncertainty. In this paper, we address the issue of efficient uncertainty quantification of system QOIs considering both aleatory and epistemic uncertainties. We propose a spatial-random-process (SRP) based multidisciplinary uncertainty analysis (MUA) method that, subsequent to SRP-based disciplinary model uncertainty quantification, fully utilizes the structure of SRP emulators and leads to compact analytical formulas for assessing statistical moments of uncertain QOIs. The proposed method is applied to a benchmark electronic packaging design problem. The estimated low-order statistical moments of the QOIs are compared to the results from Monte Carlo simulations (MCSs) to demonstrate the effectiveness of the method. The UP result is then used to facilitate the robust design optimization of the electronic packaging system.

The 6C model for accurately capturing the patient’s medical history

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Taro Shimizu
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Patient Safety ◽  
Information Processing ◽  
Safety Concern ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Accurate Diagnosis ◽  
History Taking ◽  
Novel Approach

Abstract Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. We report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.

scholarly journals Water–Energy–Food Nexus Simulation: An Optimization Approach for Resource Security

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 667 ◽  
Author(s):  
Albert Wicaksono ◽  
Gimoon Jeong ◽  
Doosun Kang
Keyword(s):  
Resource Management ◽  
Simulation Models ◽  
Decision Support Tool ◽  
Limited Resources ◽  
Optimization Approach ◽  
Support Tool ◽  
Sustainable Resource Management ◽  
Novel Approach ◽  
Management Plans ◽  
Optimal Resource

The water–energy–food nexus (WEF nexus) concept is a novel approach to manage limited resources. Since 2011, a number of studies were conducted to develop computer simulation models quantifying the interlinkage among water, energy, and food sectors. Advancing a nationwide WEF nexus simulation model (WEFSiM) previously developed by the authors, this study proposes an optimization module (WEFSiM-opt) to assist stakeholders in making informed decisions concerning sustainable resource management. Both single- and multi-objective optimization modules were developed to maximize the user reliability index (URI) for water, energy, and food sectors by optimizing the priority index and water allocation decisions. In this study, the developed models were implemented in Korea to determine optimal resource allocation and management decisions under a plausible drought scenario. This study suggests that the optimization approach can advance WEF nexus simulation and provide better solutions for managing limited resources. It is anticipated that the proposed WEFSiM-opt can be utilized as a decision support tool for designing resource management plans.

From the Linnett–Gillespie model to the polarization of the spin valence shells of metals in complexes

2020 ◽  
Vol 22 (42) ◽  
pp. 24201-24212
Author(s):  
David I. Ramírez-Palma ◽  
Fernando Cortés-Guzmán
Keyword(s):  
Metal Complex ◽  
Complex Structure ◽  
Novel Approach ◽  
Spin Densities

In this paper, we present a novel approach to track the origin of the metal complex structure from the topology of the α and β spin densities as an extension of the Linnett–Gillespie model.

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