scholarly journals Sparse Gaussian Process Emulators for Surrogate Design Modelling

2018 ◽  
Vol 885 ◽  
pp. 18-31 ◽  
Author(s):  
Paul Gardner ◽  
Timothy J. Rogers ◽  
Charles Lord ◽  
Rob J. Barthorpe
Keyword(s):  
Gaussian Process ◽  
Structural Dynamics ◽  
Design Space ◽  
Design Stage ◽  
Probabilistic Framework ◽  
Computationally Efficient ◽  
Early Design ◽  
Early Design Stage ◽  
Sparse Approximations ◽  
Data Points

Efficient surrogate modelling of computer models (herein defined as simulators) becomes of increasing importance as more complex simulators and non-deterministic methods, such as Monte Carlo simulations, are utilised. This is especially true in large multidimensional design spaces. In order for these technologies to be feasible in an early design stage context, the surrogate model (oremulator) must create an accurate prediction of the simulator in the proposed design space. Gaussian Processes (GPs) are a powerful non-parametric Bayesian approach that can be used as emulators. The probabilistic framework means that predictive distributions are inferred, providing an understanding of the uncertainty introduced by replacing the simulator with an emulator, known as code uncertainty. An issue with GPs is that they have a computational complexity of O(N3) (where N is the number of data points), which can be reduced to O(NM2) by using various sparse approximations, calculated from a subset of inducing points (where M is the number of inducing points). This paper explores the use of sparse Gaussian process emulators as a computationally efficient method for creating surrogate models of structural dynamics simulators. Discussions on the performance of these methods are presented along with comments regarding key applications to the early design stage.

Predicting Average Product Lifetime Using Physics-Based Gaussian Process Method in Early Design Stage

2020 ◽  
Author(s):  
Xinpeng Wei ◽  
Daoru Han ◽  
Xiaoping Du
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Design Stage ◽  
Average Lifetime ◽  
Small Data ◽  
Time To Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Design Variables ◽  
Lifetime Testing

Abstract Average lifetime, or mean time to failure (MTTF), of a product is an important metric to measure the product reliability. Current methods of evaluating MTTF are mainly statistics or data based. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict MTTF in the design stage by means of physics-based models. The advantage is that the design can be continually improved by changing design variables until reliability measures, including MTTF, are satisfied. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). We develop an adaptive supervised training method based on Gaussian process regression, and the method can then quickly predict MTTF with minimized number of calling the physics-based models. The effectiveness of the method is demonstrated by two examples.

Design Space Analysis Method for Support of System Design Under the Consideration of Uncertainties in the Early Design Stage

2021 ◽  
Author(s):  
Kazuhiro Aoyama ◽  
Kazuya Oizumi ◽  
Yoshihiro Uchibori ◽  
Shigeki Hiramatsu ◽  
Shuichi Kondo ◽  
...  
Keyword(s):  
System Design ◽  
Design Space ◽  
Design Stage ◽  
Analysis Method ◽  
Early Design ◽  
Early Design Stage ◽  
Space Analysis

scholarly journals Macro-Modelling of Laser Micro-Joints for Understanding Joint Strength in Electric Vehicle Battery Interconnects

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3552
Author(s):  
Abhishek Das ◽  
Richard Beaumont ◽  
Iain Masters ◽  
Paul Haney
Keyword(s):  
Joint Strength ◽  
Structural Model ◽  
Design Stage ◽  
Structural Modelling ◽  
Laser Weld ◽  
Computationally Efficient ◽  
Early Design Stage ◽  
Lap Shear ◽  
Battery Module ◽  
Modelling Approach

Laser micro-welding is increasingly being used to produce electrically conductive joints within a battery module of an automotive battery pack. To understand the joint strength of these laser welds at an early design stage, micro-joints are required to be modelled. Additionally, structural modelling of the battery module along with the electrical interconnects is important for understanding the crash safety of electric vehicles. Fusion zone based micro-modelling of laser welding is not a suitable approach for structural modelling due to the computational inefficiency and the difficulty of integrating with the module model. Instead, a macro-model which computationally efficient and easy to integrate with the structural model can be useful to replicate the behaviour of the laser weld. A macro-modelling approach was adopted in this paper to model the mechanical behaviour of laser micro-weld. The simulations were based on 5 mm diameter circular laser weld and developed from the experimental data for both the lap shear and T-peel tests. This modelling approach was extended to obtain the joint strengths for 3 mm diameter circular seams, 5 mm and 10 mm linear seams. The predicted load–displacement curves showed a close agreement with the test data.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

2018 ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

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