Singlepoint and Multipoint Robust Design of Airfoils using CST Functions

Piperni, Pat; Rahman, S. M. Mahbobur

doi:10.2514/6.2021-1359

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…There is a variety of approaches to develop such workflows by interconnecting models and parameters, examples are given in ref. [13][14][15][16][17][18][19]. However, these approaches do not address the MBSE approach: There is no link to parameters of the functional architecture and therefore the data is not centrally organized.…”

Section: State Of the Artmentioning

confidence: 99%

Reusable workflows for virtual testing of multidisciplinary products in system models

Berges

Spütz

Zhang

et al. 2023

Forsch Ingenieurwes

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Developing increasingly complex multidisciplinary products in short development cycles is one major challenge in today’s product development. Model-based Systems Engineering (MBSE) approaches are well suited to address this challenge. With MBSE, products are virtually represented in central system models. For the efficient verification of customer requirements and to avoid exhaustive physical testing with prototypes, virtual domain models (e.g. FE-models) are integrated into the system model. To perform a virtual test, domain models need to be executed in a sequence, so-called workflows.Current workflows link several product system levels in one workflow and are often only valid for one specific system architecture. As the number of requirements and system complexity increases, these workflows become also more complex. The effort for creating new comprehensible workflows is currently high and the reusability cannot be ensured. To solve these deficits, a method for the systematic formalization of reusable workflows in system models as well as their structured integration is presented. Behavior diagrams in the modelling language SysML are used to control the execution order of the domain models of different purposes and fidelities. Modular sub-workflows are developed for each system level. These sub-workflows can be reused and combined modularly to form larger workflows. The approach shows a high potential to easily build and organize workflows in reusable libraries thereby supporting automated virtual testing in product development. To demonstrate the approach, workflows for bearing lifetime calculation and shaft fatigue testing of a wind turbine drive train as well as their integration into the SysML system model are presented.

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Section: State Of the Artmentioning

confidence: 99%

Reusable workflows for virtual testing of multidisciplinary products in system models

Berges

Spütz

Zhang

et al. 2023

Forsch Ingenieurwes

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show abstract

“…The literature offers a variety of approaches for the creation and identification of valid model networks by interconnecting models and parameters; examples are provided in ref. [17][18][19][20][21][22]. In these approaches, the analysis of model networks in the literature is usually limited to domain-specific tools, model signatures and software interfaces.…”

Section: State Of the Artmentioning

confidence: 99%

Automated Identification of Valid Model Networks Using Model-Based Systems Engineering

Berges

Spütz

Jacobs

et al. 2022

Systems

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To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development.

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“…First, GANs (Goodfellow et al 2020) adopt zero-sum game theory to train the networks, which are difficult to train and prone to mode collapse. Second, they cannot work if the input performance requirements belong to different working conditions in multi-point design problems (Piperni and Rahman 2021;Mangano and Martins 2021;Li, Bai, and Qu 2022). For example, they can only generate new aircraft designs given lift coefficient (CL) and drag coefficient (CD) under only one working condition.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-Point Design Sampling After the training process, the model should be applied to generation problems. In real scenarios, the engineering design problem can be a multipoint design problem (Piperni and Rahman 2021;Mangano and Martins 2021;Li, Bai, and Qu 2022), which means the generated designs should have different specific performances under different working conditions. For example, we want the generated designs x concurrently satisfies both y 1 under working condition w c1 and y 2 under working condition w c2 .…”

mentioning

confidence: 99%

CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design

Zhao,

Zhang,

Sun

et al. 2024

AAAI

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Engineering design methods aim to generate new designs that meet desired performance requirements. Past work has directly introduced conditional Generative Adversarial Networks (cGANs) into this field and achieved promising results in single-point design problems(one performance requirement under one working condition). However, these methods assume that the performance requirements are distributed in categorical space, which is not reasonable in these scenarios. Although Continuous conditional GANs (CcGANs) introduce Vicinal Risk Minimization (VRM) to reduce the performance loss caused by this assumption, they still face the following challenges: 1) CcGANs can not handle multi-point design problems (multiple performance requirements under multiple working conditions). 2) Their training process is time-consuming due to the high computational complexity of the vicinal loss. To address these issues, A Continuous conditional Diffusion Probabilistic Model (CcDPM) is proposed, which the first time introduces the diffusion model into the engineering design area and VRM into the diffusion model. CcDPM adopts a novel sampling method called multi-point design sampling to deal with multi-point design problems. Moreover, the k-d tree is used in the training process of CcDPM to shorten the calculation time of vicinal loss and speed up the training process by 2-300 times in our experiments. Experiments on a synthetic problem and three real-world design problems demonstrate that CcDPM outperforms the state-of-the-art GAN models.

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Singlepoint and Multipoint Robust Design of Airfoils using CST Functions

Cited by 5 publications

References 28 publications

Reusable workflows for virtual testing of multidisciplinary products in system models

Reusable workflows for virtual testing of multidisciplinary products in system models

Automated Identification of Valid Model Networks Using Model-Based Systems Engineering

CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design

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