Optimization under turbulence model uncertainty for aerospace design

Cook, Laurence W.; Mishra, Aashwin; Jarrett, Jerome P.; Willcox, Karen; Iaccarino, Gianluca

doi:10.1063/1.5118785

Cited by 30 publications

(16 citation statements)

References 30 publications

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“…To expedite computations, different levels of models and descriptions should be devoted to different zones and components of the problem in order to allocate computational resources more effectively and economically. This might be the case for many other coupled multiphysics systems relevant to fluid dynamics, such as heterogeneous multiscale problems [90,150,151,167,268,269], fluid–structure interaction [240,335], aerospatial [75,302,354], subsurface [48,206], and wind farm applications [309]. Since various zones in these systems are connected through interfaces, data sharing and consistent interface treatment among respective models are inevitable (e.g., Dirichlet–Neumann, Dirichlet–Robin, and Robin–Robin).…”

Section: Eclecticism and Interface Learningmentioning

confidence: 99%

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

2021

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Most modeling approaches lie in either of the two categories: physics‐based or data‐driven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for scientific applications. To leverage these developments, our aim in this perspective paper is centered around exploring numerous principle concepts to address the challenges of (i) trustworthiness and generalizability in developing data‐driven models to shed light on understanding the fundamental trade‐offs in their accuracy and efficiency and (ii) seamless integration of interface learning and multifidelity coupling approaches that transfer and represent information between different entities, particularly when different scales are governed by different physics, each operating on a different level of abstraction. Addressing these challenges could enable the revolution of digital twin technologies for scientific and engineering applications.

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Section: Eclecticism and Interface Learningmentioning

confidence: 99%

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

2021

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“…To expedite computations, different levels of models and descriptions should be devoted to different zones and components of the problem in order to allocate computational resources more effectively and economically. This might be the case for many other coupled multiphysics systems relevant to fluid dynamics, such as heterogeneous multiscale problems [260,[270][271][272][273][274] , fluid-structure interaction [154,275] , aerospatial [276][277][278] , subsurface [279,280] , and wind farm applications [281] . Since various zones in these systems are connected through interfaces, data sharing and consistent interface treatment among respective models are inevitable (e.g., Dirichlet-Neumann, Dirichlet-Robin, and Robin-Robin).…”

Section: Eclecticism and Interface Learningmentioning

confidence: 99%

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

San

Rasheed

Kvamsdal

2021

Preprint

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Most modeling approaches lie in either of the two categories: physics-based or datadriven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for scientific applications. To leverage these developments, our aim in this perspective paper is centered around exploring numerous principle concepts to address the challenges of (i) trustworthiness and generalizability in developing data-driven models to shed light on understanding the fundamental trade-offs in their accuracy and efficiency, and (ii) seamless integration of interface learning and multifidelity coupling approaches that transfer and represent information between different entities, particularly when different scales are governed by different physics, each operating on a different level of abstraction. Addressing these challenges could enable the revolution of digital twin technologies for scientific and engineering applications.

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“…The uncertainties are mostly related to the prediction of flow separation [49], recirculation zones [50], and sharp discontinuities [51]. Therefore, the use of CFD in the design optimization may lead to suboptimal solutions that could lack of reliability [52,53].…”

Section: Owc Chamber and Impulse Turbine Overviewmentioning

confidence: 99%

Wave-to-Wire Model of an Oscillating-Water-Column Wave Energy Converter and Its Application to Mediterranean Energy Hot-Spots

et al. 2020

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Oscillating water column (OWC) systems are among the most credited solutions for an effective conversion of the notable energy potential conveyed by sea waves. Despite a renewed interest, however, they are often still at a demonstration phase and additional research is required to reach industrial maturity. Within this framework, this study provides a wave-to-wire model for OWC systems based on an impulse air turbine. The model performs a comprehensive simulation of the system to estimate the attendant electric energy production for a specific sea state, based on analytical models of the primary (fixed chamber) and secondary (air turbine) converters coupled with the tertiary converter (electric generator). A rigid piston model is proposed to solve the hydrodynamics, thermodynamics, and hydrodynamics of the chamber, in a coupled fashion with the impulse turbine aerodynamics. This is solved with a novel method by considering the cascades as sets of blades, each one consisting of a finite number of airfoils stacked in the radial direction. The model was applied for two Mediterranean sites located in Tuscany and Sardinia (Italy), which were selected to define the optimal geometry of the turbine for a specified chamber. For each system, the developed analytical wave-to-wire model was applied to calculate the performance parameters and the annual energy production in environmental conditions typical of the Mediterranean Sea. The selected impulse turbines are able to convert 13.69 and 39.36 MWh/year, with an efficiency of 4.95% and 4.76%, respectively, thus proving the interesting prospects of the technology.

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Optimization under turbulence model uncertainty for aerospace design

Cited by 30 publications

References 30 publications

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

Wave-to-Wire Model of an Oscillating-Water-Column Wave Energy Converter and Its Application to Mediterranean Energy Hot-Spots

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