Effectively using multifidelity optimization  for wind turbine design

Jasa, John; Bortolotti, Pietro; Zalkind, Daniel; Barter, Garrett

doi:10.5194/wes-7-991-2022

Cited by 11 publications

(6 citation statements)

References 58 publications

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“…The availability of multiple levels of fidelity can be exploited to support the search procedure through a principled elicitation of information: fast low-fidelity models are used to massively explore different design configurations, and expensive high-fidelity models sparingly refine the solution of the MDO problem. Multifidelity methods have been successfully applied to a variety of MDO applications ranging from aircraft 21 , 22 and space vehicles 23 , 24 to ships 25 and unmanned underwater vehicles 26 , from electric 27 and hybrid 28 vehicles to green energy technologies 29 , 30 . In most cases, the complexity of the disciplinary analysis and couplings discourages the use of gradient-based optimization strategies: the computation of the derivatives might demand for massive high-fidelity data and increase the overall computational burden.…”

Section: Introductionmentioning

confidence: 99%

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

Di Fiore,

Mainini

2023

Sci Rep

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The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently combines information from multiple models at different levels of fidelity to accelerate the MDO procedure. State-of-the-art MFBO methods currently meet two major limitations: (i) the sequential adaptive sampling precludes parallel computations of high-fidelity models, and (ii) the search scheme measures the utility of new design evaluations only at the immediate next iteration. This paper proposes a Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) algorithm to sensitively accelerate MDO overcoming the limitations of standard methods. NM3-BO selects a batch of promising design configurations to be evaluated in parallel, and quantifies the expected long-term improvement of these designs at future steps of the optimization. Our learning scheme leverages an original acquisition function based on the combination of a two-step lookahead policy and a local penalization strategy to measure the future utility achieved evaluating multiple design configurations simultaneously. We observe that the proposed framework permits to sensitively accelerate the MDO of a space vehicle and outperforms popular algorithms.

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Section: Introductionmentioning

confidence: 99%

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

Di Fiore,

Mainini

2023

Sci Rep

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“…One way to accomplish that is by using a multiple start algorithm or by performing an initial search of the design space. Multi-fidelity optimization procedures [22] could also be useful to formally couple two or more levels of fidelity that do not necessarily agree, whether constraints are violated or not.…”

Section: Platform Design Using Raft Versus Openfastmentioning

confidence: 99%

Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform

Zalkind,

Bortolotti

2024

J. Phys.: Conf. Ser.

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We present a control co-design software framework that can be used to optimize floating wind turbines and their controllers. Because this framework has many options for design variables, constraints, and merit figures, along with modeling fidelity levels, we seek to demonstrate best practices for using the tool while designing a floating platform for the new 22 MW offshore reference wind turbine developed within the International Energy Agency Wind Technology Commercialization Programme 55 on Reference Wind Turbines and Farms. During these studies, we evaluate the use of different simulation fidelity levels, the effect of using different load cases for controller tuning, and the difference between sequential and simultaneous control co-design solutions. Based on these efforts, we suggest using an algorithm that performs an initial search of the design space before optimization. We find that solving smaller optimization problems, in a sequential manner, leads to more reliable outcomes in fewer iterations than larger, simultaneous control co-design solutions. However a simultaneous CCD solution produces a platform with a 2% lower mass than the sequential CCD outcome.

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“…aircraft engine consumption modeling [150], numerical integration [73], multi-fidelity sensitivity analysis [69], high-order robust finite elements methods [118,126], planning for photovoltaic solar energy [47], wind turbines design optimization [111], porous material optimization for a high pressure turbine vane [232], chemical process design [203] and many other applications.…”

Section: Motivation and Significancementioning

confidence: 99%

High-dimensional multidisciplinary design optimization for aircraft eco-design

SAVES

2024

Preprint

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Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel consumption and, as such, MDO arises as a powerful tool for automatically making interdisciplinary trade-offs. In the aircraft design context, the process generally involves mixed continuous and categorical design variables. For instance, the size of an aircraft’s structural parts can be described using continuous variables, while discrete variables may include either integer variables, like the number of panels, or categorical variables, like cross-sections or material choices. The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an efficient approach for optimizing a multidisciplinary black-box model when the optimization problem is constrained and involves a large number of mixed integer design variables (typically 100 variables). The targeted optimization approach, called EGO, is based on a sequential enrichment of an adaptive surrogate model and, in this context, GP surrogate models are one of the most widely used in engineering problems to approximate time-consuming high fidelity models. EGO is a heuristic BO method that performs well in terms of solution quality. However, like any other global optimization method, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft design problems, the typical size of the design variables can even exceed 100 and, thus, trying to solve directly the problems using EGO is ruled out. The latter is especially true when the problems involve both continuous and categorical variables increasing even more the size of the search space. In this Ph.D thesis, effective parameterization tools are investigated, including techniques like partial least squares regression, to significantly reduce the number of design variables. Additionally, Bayesian optimization is adapted to handle discrete variables and high-dimensional spaces in order to reduce the number of evaluations when optimizing innovative aircraft concepts such as the “DRAGON” hybrid airplane to reduce their climate impact.

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Effectively using multifidelity optimization for wind turbine design

Cited by 11 publications

References 58 publications

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform

High-dimensional multidisciplinary design optimization for aircraft eco-design

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