Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems

2021 ◽  
Vol 21 (4) ◽  
Author(s):  
Seyede Fatemeh Ghoreishi ◽  
Mahdi Imani
Keyword(s):  
Uncertainty Analysis ◽  
Closed Form ◽  
Uncertainty Propagation ◽  
Uncertainty Reduction ◽  
System Level ◽  
Engineering Systems ◽  
Sample Point ◽  
Design Variables ◽  
Multidisciplinary Systems ◽  
Stationary Behavior

Abstract Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.

A Fractional-Factorial Numerical Technique for Stress Analysis of Glass-To-Metal Lead Seals

1994 ◽  
Vol 116 (2) ◽  
pp. 98-104 ◽  
Author(s):  
Barry Mathieu ◽  
Abhijit Dasgupta
Keyword(s):  
Closed Form ◽  
Numerical Technique ◽  
Mechanical Vibration ◽  
Maximum Principal Stress ◽  
Design Guidelines ◽  
Fractional Factorial ◽  
Design Variables ◽  
Load Carrying ◽  
Glass Seals ◽  
Glass Seal

Fracture of glass seals in metallic hermetic electronic packaging is a significant failure mode because it may lead to moisture ingress and also to loss of load carrying capacity of the glass seal. Seal glasses are intrinsically brittle and their fracture is governed by the stresses generated. This study investigates stresses in lead seals caused by handling, testing, mechanical vibration, and thermal excursions. Loads considered are axial tension, bending, and twisting of the lead. More general loading can be handled by superposition of these results. Factorial techniques, commonly used in multi-variable Design of Experiments (DoE), are used in conjunction with finite element parametric simulations, to formulate closed-form regression models which relate the maximum principal stress within the glass seal to the type of loading and geometry. The accuracy of the proposed closed-form equations are verified through analysis of residuals. The analysis reveals the sensitivity of the magnitude of the seal stress to design variables such as the materials and geometry of the seal, lead, and package. Manufacturing-induced problems such as defects and flaws are not considered. An additional purpose for presenting this study is to illustrate the use of design of experiment methods for developing closed-form models and design guidelines from simulation studies, in a multi-variable problem.

Uncertainty Evaluation on Multi-Hole Aerodynamic Pressure Probes

2020 ◽  
Author(s):  
Andrea Notaristefano ◽  
Paolo Gaetani ◽  
Vincenzo Dossena ◽  
Alberto Fusetti
Keyword(s):  
Uncertainty Analysis ◽  
Evaluation Method ◽  
Experimental Testing ◽  
Uncertainty Propagation ◽  
Calibration Procedure ◽  
Fast Response ◽  
Uncertainty Evaluation ◽  
Sensor Calibration ◽  
Pressure Probe ◽  
Aerodynamic Pressure

Abstract In the frame of a continuous improvement of the performance and accuracy in the experimental testing of turbomachines, the uncertainty analysis on measurements instrumentation and techniques is of paramount importance. For this reason, since the beginning of the experimental activities at the Laboratory of Fluid Machines (LFM) located at Politecnico di Milano (Italy), this issue has been addressed and different methodologies have been applied. This paper proposes a comparison of the results collected applying two methods for the measurement uncertainty quantification to two different aerodynamic pressure probes: sensor calibration, aerodynamic calibration and probe application are considered. The first uncertainty evaluation method is the so called “Uncertainty Propagation” method (UPM); the second is based on the “Monte Carlo” method (MCM). Two miniaturized pressure probes have been selected for this investigation: a pneumatic 5-hole probe and a spherical fast response aerodynamic pressure probe (sFRAPP), the latter applied as a virtual 4-hole probe. Since the sFRAPP is equipped with two miniaturized pressure transducers installed inside the probe head, a specific calibration procedure and a dedicated uncertainty analysis are required.

scholarly journals Uncertainty Analysis for Topographic Correction of Hyperspectral Remote Sensing Images

2020 ◽  
Vol 12 (4) ◽  
pp. 705 ◽  
Author(s):  
Zhaoning Ma ◽  
Guorui Jia ◽  
Michael E. Schaepman ◽  
Huijie Zhao
Keyword(s):  
Uncertainty Analysis ◽  
Uncertainty Propagation ◽  
Spectral Feature ◽  
Metrological Traceability ◽  
Soil Surface ◽  
Topographic Correction ◽  
Uncertainty In Measurement ◽  
Hyperion Data ◽  
Set Up ◽  
Hyperspectral Remote Sensing Images

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

A Model-Based Formulation of Robust Design

2005 ◽  
Vol 127 (3) ◽  
pp. 388-396 ◽  
Author(s):  
Khalid Al-Widyan ◽  
Jorge Angeles
Keyword(s):  
Covariance Matrix ◽  
Robust Design ◽  
Design Problem ◽  
Engineering Systems ◽  
Design Environment ◽  
Design Task ◽  
Model Based ◽  
Functional Relations ◽  
Design Variables ◽  
Environment Parameters

Laid down in this paper are the foundations on which the design of engineering systems, in the presence of an uncontrollable changing environment, can be based. The changes in environment conditions are accounted for by means of robustness. To this end, a theoretical framework as well as a general methodology for model-based robust design are proposed. Within this framework, all quantities involved in a design task are classified into three sets: the design variables (DV), grouped in vector x, which are to be assigned values as an outcome of the design task; the design-environment parameters (DEP), grouped in vector p, over which the designer has no control; and the performance functions (PF), grouped in vector f, representing the functional relations among performance, DV, and DEP. A distinction is made between global robust design and local robust design, this paper focusing on the latter. The robust design problem is formulated as the minimization of a norm of the covariance matrix of the variations in PF upon variations in the DEP, aka noise in the literature on robust design. Moreover, one pertinent concept is introduced: design isotropy. We show that isotropic designs lead to robustness, even in the absence of knowledge of the statistical properties of the variations of the DEP. To demonstrate our approach, a few examples are included.

Efficient Possibilistic Uncertainty Analysis of a Car Crash Scenario Using a Multifidelity Approach

2019 ◽  
Vol 5 (4) ◽  
Author(s):  
Markus Mäck ◽  
Michael Hanss
Keyword(s):  
Uncertainty Analysis ◽  
Large Scale ◽  
Uncertainty Propagation ◽  
Possibility Theory ◽  
Design Stage ◽  
High Fidelity ◽  
Zone Structure ◽  
Early Design Stage ◽  
Car Crash ◽  
Fidelity Model

Abstract The early design stage of mechanical structures is often characterized by unknown or only partially known boundary conditions and environmental influences. Particularly, in the case of safety-relevant components, such as the crumple zone structure of a car, those uncertainties must be appropriately quantified and accounted for in the design process. For this purpose, possibility theory provides a suitable tool for the modeling of incomplete information and uncertainty propagation. However, the numerical propagation of uncertainty described by possibility theory is accompanied by high computational costs. The necessarily repeated model evaluations render the uncertainty analysis challenging to be realized if a model is complex and of large scale. Oftentimes, simplified and idealized models are used for the uncertainty analysis to speed up the simulation while accepting a loss of accuracy. The proposed multifidelity scheme for possibilistic uncertainty analysis, instead, takes advantage of the low costs of an inaccurate low-fidelity model and the accuracy of an expensive high-fidelity model. For this purpose, the functional dependency between the high- and low-fidelity model is exploited and captured in a possibilistic way. This results in a significant speedup for the uncertainty analysis while ensuring accuracy by using only a low number of expensive high-fidelity model evaluations. The proposed approach is applied to an automotive car crash scenario in order to emphasize its versatility and applicability.

Decision-Based Collaborative Optimization Under Uncertainty

2000 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud ◽  
Leah M. Ashe ◽  
Stephen M. Batill ◽  
Amarjit S. Budhiraja ◽  
...  
Keyword(s):  
Systems Design ◽  
Collaborative Optimization ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Optimization Strategy ◽  
Business Decisions ◽  
Single Level ◽  
Optimization Framework ◽  
Multidisciplinary Systems ◽  
Decision Based Design

Abstract In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Designing and Harnessing the Metastable States of a Modular Metastructure for Programmable Mechanical Properties Adaptation

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
R. L. Harne ◽  
Z. Wu ◽  
K. W. Wang
Keyword(s):  
Mechanical Properties ◽  
Metastable States ◽  
System Level ◽  
Macroscopic System ◽  
Actin Networks ◽  
Module Design ◽  
Design Variables ◽  
Module Development ◽  
Important Design ◽  
Good Agreement

Recent studies on periodic metamaterial systems have shown that remarkable properties adaptivity and versatility are often the products of exploiting internal, coexisting metastable states. Motivated by this concept, this research develops and explores a local-global design framework wherein macroscopic system-level properties are sought according to a strategic periodic constituent composition and assembly. To this end and taking inspiration from recent insights in studies of multiphase composite materials and cytoskeletal actin networks, this study develops adaptable metastable modules that are assembled into modular metastructures, such that the latter are invested with synergistic features due to the strategic module development and integration. Using this approach, it is seen that modularity creates an accessible pathway to exploit metastable states for programmable metastructure adaptivity, including a near-continuous variation of mechanical properties or stable topologies and adjustable hysteresis. A model is developed to understand the source of the synergistic characteristics, and theoretical findings are found to be in good agreement with experimental results. Important design-based questions are raised regarding the modular metastructure concept, and a genetic algorithm (GA) routine is developed to elucidate the sensitivities of the properties variation with respect to the statistics amongst assembled module design variables. To obtain target multifunctionality and adaptivity, the routine discovers that particular degrees and types of modular heterogeneity are required. Future realizations of modular metastructures are discussed to illustrate the extensibility of the design concept and broad application base.

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