Collaborative Research: Enabling Large-scale Multidisciplinary Design Optimization with Unsteady Simulations: A Hybrid Pseudo-spectral Approach

协作研究:通过非定常模拟实现大规模多学科设计优化:混合伪谱方法

基本信息

项目摘要

This project will develop a breakthrough multidisciplinary design optimization (MDO) framework that uses unsteady multiphysics computer simulations to optimize system performance automatically. The research is motivated by the lack of effective numerical algorithms to shorten the design period for large-scale engineered systems with unsteady processes, such as spacecraft, aircraft, and wind turbines. This issue is further exacerbated by ever-increasing expectations for system performance and safety. The automated MDO framework will significantly reduce the design cycle time for transformative systems that are poised to improve the nation’s economic prosperity and change how people live and connect, such as urban air taxis and systems supporting space travel. Furthermore, this project will advance the knowledge of complex mechanisms and interactions in large-scale engineered systems, which would otherwise be hard to obtain solely by human intuition. This project will also conduct educational and outreach activities for underrepresented minority and K-12 students to encourage STEM engagement, promote diversity and inclusion, and stimulate students' interest in engineering design and optimization.The research objective of this project is to enable the gradient-based multidisciplinary design optimization (MDO) of large-scale engineered systems governed by unsteady processes. The project will develop a new hybrid pseudo-spectral (HPS) adjoint algorithm to compute unsteady gradients for a broad range of disciplines efficiently. The originality of the HPS algorithm is that it effectively combines the robustness of time-accurate analysis and the speed of pseudo-spectral adjoint to enable efficient computation of high-dimensional unsteady gradients. The project will investigate the fundamental characteristics of the HPS algorithm and develop a modular architecture to couple any number of disciplines for large-scale unsteady MDO. It will demonstrate the framework by conducting urban air mobility electric aircraft and offshore wind turbine MDO that considers the unsteady coupling between fluid mechanics, structures, heat transfer, and dynamics. With further development, the framework can be extended to more disciplines, such as control and multiphase flow. The unsteady MDO framework will be open to the public to promote collaborations in the engineering design community. The HPS algorithm is general and expected to benefit many other fundamental research areas beyond MDO, including surrogate modeling, error and uncertainty analyses, and machine learning. Moreover, this project is anticipated to create a catalytic effect in the engineering design industry to transform the traditional, human-supervised design process into a more automated one.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目将开发一个突破性的多学科设计优化(MDO)框架,使用非定常多物理场计算机模拟来自动优化系统性能。该研究的动机是缺乏有效的数值算法,以缩短设计周期的大型工程系统的非定常过程,如航天器,飞机和风力涡轮机。对系统性能和安全性的期望不断提高,进一步加剧了这一问题。自动化MDO框架将大大缩短转型系统的设计周期,这些系统有望改善国家的经济繁荣,改变人们的生活和联系方式,例如城市空中出租车和支持太空旅行的系统。此外,该项目将推进大规模工程系统中复杂机制和相互作用的知识,否则仅凭人类直觉很难获得。该项目还将为代表性不足的少数族裔和K-12学生开展教育和外展活动,以鼓励STEM参与,促进多样性和包容性,并激发学生对工程设计和优化的兴趣。该项目的研究目标是实现基于梯度的非稳态过程大型工程系统的多学科设计优化。该项目将开发一种新的混合伪谱(HPS)伴随算法,以有效地计算广泛学科的非定常梯度。HPS算法的独创性在于它有效地结合了时间精确分析的鲁棒性和伪谱伴随的速度,从而能够有效地计算高维非定常梯度。该项目将调查的HPS算法的基本特征,并开发一个模块化的架构,耦合任何数量的大规模非定常MDO学科。它将通过进行城市空中机动电动飞机和海上风力涡轮机多学科设计优化,考虑流体力学,结构,传热和动力学之间的非定常耦合来演示该框架。随着进一步的发展,该框架可以扩展到更多的学科,如控制和多相流。不稳定的MDO框架将向公众开放,以促进工程设计界的合作。HPS算法是通用的,并有望使MDO以外的许多其他基础研究领域受益,包括代理建模,误差和不确定性分析,以及机器学习。此外,该项目预计将在工程设计行业产生催化效应,将传统的人工监督设计过程转变为更自动化的过程。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Joaquim Martins其他文献

Genetic Diversity of Xylella fastidiosa Plasmids Assessed by Comparative Genomics
通过比较基因组学评估苛养木杆菌质粒的遗传多样性
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    P. Pierry;Guillermo Uceda;O. Feitosa;Joaquim Martins;W. O. de Santana;H. Coletta;Paulo A Zaini;A. D. da
  • 通讯作者:
    A. D. da
Paleodistributions and Comparative Molecular Phylogeography of Leafcutter Ants (Atta spp.) Provide New Insight into the Origins of Amazonian Diversity
切叶蚁(Atta spp.)的古分布和比较分子系统地理学为亚马逊多样性的起源提供了新的见解
  • DOI:
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    S. E. Solomon;M. Bacci;Joaquim Martins;Giovanna Gonçalves Vinha;U. Mueller
  • 通讯作者:
    U. Mueller
Comparative genomics of Xylella fastidiosa suggests determinants of host-specificity and expands its mobile genetic elements repertoire
苛养木杆菌的比较基因组学揭示了宿主特异性的决定因素并扩展了其移动遗传元件库
  • DOI:
    10.1101/2021.10.17.464729
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Guillermo Uceda;O. Feitosa;C. Santiago;P. Pierry;Paulo A Zaini;W. O. de Santana;Joaquim Martins;Deibs Barbosa;L. Digiampietri;J. Setubal;A. D. da
  • 通讯作者:
    A. D. da

Joaquim Martins的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Joaquim Martins', 18)}}的其他基金

Enabling the Design of Large-Scale Complex Engineered Systems using Self-Organizing Optimization Algorithms
使用自组织优化算法实现大规模复杂工程系统的设计
  • 批准号:
    1435188
  • 财政年份:
    2014
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: Workshop: The Future of Multidisciplinary Design Optimization - Advancing the Design of Complex Systems, Fort Worth, Texas, September 16, 2010
协作研究:研讨会:多学科设计优化的未来 - 推进复杂系统的设计,德克萨斯州沃思堡,2010 年 9 月 16 日
  • 批准号:
    1042740
  • 财政年份:
    2010
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: Enabling Cloud-Permitting and Coupled Climate Modeling via Nonhydrostatic Extensions of the CESM Spectral Element Dynamical Core
合作研究:通过 CESM 谱元动力核心的非静水力扩展实现云允许和耦合气候建模
  • 批准号:
    2332469
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Continuing Grant
Collaborative Research: SHF: Medium: Enabling Graphics Processing Unit Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods
合作研究:SHF:中:通过轻量级仿真方法实现大规模工作负载的图形处理单元性能仿真
  • 批准号:
    2402804
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: CPS: NSF-JST: Enabling Human-Centered Digital Twins for Community Resilience
合作研究:CPS:NSF-JST:实现以人为本的数字孪生,提高社区复原力
  • 批准号:
    2420846
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Medium: Enabling GPU Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods
合作研究:SHF:中:通过轻量级仿真方法实现大规模工作负载的 GPU 性能仿真
  • 批准号:
    2402806
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Medium: Enabling GPU Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods
合作研究:SHF:中:通过轻量级仿真方法实现大规模工作负载的 GPU 性能仿真
  • 批准号:
    2402805
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: Enabling Cloud-Permitting and Coupled Climate Modeling via Nonhydrostatic Extensions of the CESM Spectral Element Dynamical Core
合作研究:通过 CESM 谱元动力核心的非静水力扩展实现云允许和耦合气候建模
  • 批准号:
    2332468
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Continuing Grant
Collaborative Research: SII-NRDZ: SweepSpace: Enabling Autonomous Fine-Grained Spatial Spectrum Sensing and Sharing
合作研究:SII-NRDZ:SweepSpace:实现自主细粒度空间频谱感知和共享
  • 批准号:
    2348589
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: CPS: NSF-JST: Enabling Human-Centered Digital Twins for Community Resilience
合作研究:CPS:NSF-JST:实现以人为本的数字孪生,提高社区复原力
  • 批准号:
    2420847
  • 财政年份:
    2024
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: OAC Core: An Integrated Framework for Enabling Temporal-Reliable Quantum Learning on NISQ-era Devices
合作研究:OAC Core:在 NISQ 时代设备上实现时间可靠的量子学习的集成框架
  • 批准号:
    2311950
  • 财政年份:
    2023
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Standard Grant
Collaborative Research: GCR: Convergence on Phosphorus Sensing for Understanding Global Biogeochemistry and Enabling Pollution Management and Mitigation
合作研究:GCR:融合磷传感以了解全球生物地球化学并实现污染管理和缓解
  • 批准号:
    2317826
  • 财政年份:
    2023
  • 资助金额:
    $ 33.28万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了