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

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

2020 ◽  
Author(s):  
Kazuhiro Aoyama ◽  
Yoshihiro Uchibori ◽  
Kazuya Oizumi ◽  
Shigeki Hiramatsu ◽  
Hiroshi Unesaki ◽  
...  
Keyword(s):  
System Design ◽  
Design Space ◽  
Solution Space ◽  
Design Stage ◽  
Design Solution ◽  
Analysis Method ◽  
Early Design Stage ◽  
Design Variables ◽  
Full Picture ◽  
Set Based Design

Abstract In this study, following the concept of set-based design, after preparing global calculation results, we introduced the approach of setting the design solution area that satisfies the product performance goals of the system design. In this approach, from the viewpoint of considering uncertainty, we aimed to develop an analysis method that can get the organic relationship between target variables and design variables. And more, under the assumption that it is difficult to comprehend the full picture of products that are becoming more sophisticated and complex with the knowledge that has been fostered by skilled engineers, the proposed system uses the objective calculation indices that is provided knowledge of the designer. Specifically, the following method are proposed to solve the problem. - Implementation of meta-modeling of design space. - Classified solution space using a density-based clustering method to detect that the solution spaces are divided into multiple disconnected space. - Defined an index called distribution concentration and expressed the possibility of dealing with the uncertainty of the solution domain. - The network diagram based on the calculated index values was proposed to confirm the change in the characteristics of the solution space when the performance target of the product was changed. Finally, the effectiveness of the proposed method was verified by applying it to actual simulation results.

System Design Priority Order Considering Uncertainty in Early Stages

2021 ◽  
Author(s):  
Takumi Kuroyanagi ◽  
Shuho Yamada ◽  
Shigeki Hiramatsu ◽  
Hiroshi Unesaki ◽  
Shuichi Kondo ◽  
...  
Keyword(s):  
System Design ◽  
Design Space ◽  
Solution Space ◽  
Design Stage ◽  
Early Design Stage ◽  
Development Processes ◽  
Priority Order ◽  
Target Performance ◽  
Set Based Design ◽  
Development Risk

Abstract Herein, we have confirmed the importance of formulating product proposals and product-development processes equipped to cope effectively with uncertainty in the early design stage. The objective of this study was to derive the target performance and design priority order taking into account uncertainties in system design. Following the concept of set-based design, the approach adopted was to secure a set of solutions as design space that satisfy the target variables demands, dividing the design space into several clusters and evaluating each of the clusters, then gradually narrowing the cluster as the design progresses, and finally extracting the solution space that is desirable. Priority order of design was developed based on the strategy of increasing the degree of freedom of the subsequent process. The effectiveness of the proposed method was verified using the model of a plug-in hybrid vehicle. From the results, we confirmed the existence of a trade-off between design and target variables preference and development risk, that it is possible to determine the extent to which the solution space can be narrowed, that the shape of the solution space determines the design priorities, and we were able to derive a desirable design priority order according to the target performance.

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.

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.

An Analytical Approach to Functional Design

2002 ◽  
Author(s):  
Kai-Lu Wang ◽  
Yan Jin
Keyword(s):  
Design Stage ◽  
Application Software ◽  
Functional Design ◽  
Functional Dependencies ◽  
Analysis Method ◽  
Modeling And Analysis ◽  
Early Design Stage ◽  
And Function ◽  
Selected Functions ◽  
Function Structures

Functional design is a process in engineering design that dominates the key features of the result to be developed. Designing good functions that both satisfies the requirements and leads to better results is a challenge due to uncertainties on the consequences of the selected functions, and the lack of analysis methods for identifying the properties of function structures. Therefore, extensive experiences are usually required for functional design. This research argues that the physical relationships among the resulting components of a design are the consequences of functional dependencies developed during the functional design process. Therefore based on the understanding of functions and functional dependencies, a reasoning procedure can be developed to predict the performance properties of the design so that the effectiveness of the functional design can be evaluated at an early design stage. This paper proposes a dependency-based function modeling and analysis method that can be applied to represent and assess functions and function structures at the functional design stage. Designers can predict the properties of the functions they designed without having to have similar design experiences. An application software is also developed to implement the method and demonstrate its effectiveness.

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