Relevant bibliographies by topics / Computational Fluid Dynamics, Adjoint Optimization, Turbines

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Computational Fluid Dynamics, Adjoint Optimization, Turbines'

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Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Computational Fluid Dynamics, Adjoint Optimization, Turbines"

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Madsen, Mads H. Aa, Frederik Zahle, Niels N. Sørensen, and Joaquim R. R. A. Martins. "Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine." Wind Energy Science 4, no. 2 (2019): 163–92. http://dx.doi.org/10.5194/wes-4-163-2019.

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Abstract. The wind energy industry relies heavily on computational fluid dynamics (CFD) to analyze new turbine designs. To utilize CFD earlier in the design process, where lower-fidelity methods such as blade element momentum (BEM) are more common, requires the development of new tools. Tools that utilize numerical optimization are particularly valuable because they reduce the reliance on design by trial and error. We present the first comprehensive 3-D CFD adjoint-based shape optimization of a modern 10 MW offshore wind turbine. The optimization problem is aligned with a case study from International Energy Agency (IEA) Wind Task 37, making it possible to compare our findings with the BEM results from this case study and therefore allowing us to determine the value of design optimization based on high-fidelity models. The comparison shows that the overall design trends suggested by the two models do agree, and that it is particularly valuable to consult the high-fidelity model in areas such as root and tip where BEM is inaccurate. In addition, we compare two different CFD solvers to quantify the effect of modeling compressibility and to estimate the accuracy of the chosen grid resolution and order of convergence of the solver. Meshes up to 14×106 cells are used in the optimization whereby flow details are resolved. The present work shows that it is now possible to successfully optimize modern wind turbines aerodynamically under normal operating conditions using Reynolds-averaged Navier–Stokes (RANS) models. The key benefit of a 3-D RANS approach is that it is possible to optimize the blade planform and cross-sectional shape simultaneously, thus tailoring the shape to the actual 3-D flow over the rotor. This work does not address evaluation of extreme loads used for structural sizing, where BEM-based methods have proven very accurate, and therefore will likely remain the method of choice.
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Sanchez Torreguitart, Ismael, Tom Verstraete, and Lasse Mueller. "Optimization of the LS89 Axial Turbine Profile Using a CAD and Adjoint Based Approach †." International Journal of Turbomachinery, Propulsion and Power 3, no. 3 (2018): 20. http://dx.doi.org/10.3390/ijtpp3030020.

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The LS89 high pressure axial turbine vane was originally designed and optimized for a downstream isentropic Mach number of 0.9. This profile has been widely used for computational fluid dynamics (CFD) validation in the open literature but very few attempts have been made to improve the already optimized design. This paper presents a sound methodology to design and optimize the LS89 using computer-aided design (CAD) at design conditions. The novelty of the study resides in the parametrization of design space, which is done at the CAD level, and the detailed analysis of the aerodynamic performance of the optimized design. Higher level constraints are imposed on the shape, such as the trailing edge thickness, the axial chord length, and G2 geometric continuity between the suction side and pressure side at the leading edge. The gradients used for the optimization are obtained by applying algorithmic differentiation to the CAD kernel and grid generator and the discrete adjoint method to the CFD solver. A reduction of almost 12% entropy generation is achieved, which is equivalent to a 16% total pressure loss reduction. The entropy generation is reduced while keeping the exit flow angle as a flow constraint, which is enforced via the penalty formulation. The resulting unconstrained optimization problem is solved by the L-BFGS-B algorithm. The flow is governed by the Reynolds-averaged Navier-Stokes equations and the one-equation transport Spalart-Allmaras turbulence model. The optimal profile is compared and benchmarked against the baseline case.
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Leary, Stephen J., Atul Bhaskar, and Andrew J. Keane. "Global Approximation and Optimization Using Adjoint Computational Fluid Dynamics Codes." AIAA Journal 42, no. 3 (2004): 631–41. http://dx.doi.org/10.2514/1.9114.

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Costa, A., and R. Nara. "Computational Fluid Dynamics Erosion Investigation Using Single Objective Adjoint Shape Optimization." Journal of Pipeline Systems Engineering and Practice 11, no. 3 (2020): 06020001. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000468.

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Vilag, Valeriu, Ivanka Zheleva, Jenia Popescu, and Krasimir Tujarov. "Computational fluid dynamics calculus and analysis for gas and water turbines." MATEC Web of Conferences 145 (2018): 03014. http://dx.doi.org/10.1051/matecconf/201814503014.

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The paper presents the utilization of Computational Fluid Dynamics for calculating the flow through turbines. The first and most extended part of the paper is focused on gas turbines where the simulations are very precise and can be successfully used even for optimization of blade geometry. Flow details and results for an axial turbine are presented along with a proposal of optimization algorithm. The second part of the paper is dedicated to water turbines and there is presented the calculus realized for a kinetic water turbine. I this case, the flow around the turbine blades is presented and some data about the predicted performances along with many ways for improving the simulations. The conclusions of the paper are related to similarities and differences between the two types of simulations and to the many ways of using these simulations for practical applications.
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Jakubek, D., S. Herzog, and C. Wagner. "Shape Optimization of High Speed Trains using Adjoint-Based Computational Fluid Dynamics." International Journal of Railway Technology 1, no. 2 (2012): 67–88. http://dx.doi.org/10.4203/ijrt.1.2.4.

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Ayancik, Fatma, Erdem Acar, Kutay Celebioglu, and Selin Aradag. "Simulation-based design and optimization of Francis turbine runners by using multiple types of metamodels." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 8 (2016): 1427–44. http://dx.doi.org/10.1177/0954406216658078.

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In recent years, optimization started to become popular in several engineering disciplines such as aerospace, automotive and turbomachinery. Optimization is also a powerful tool in hydraulic turbine industry to find the best performance of turbines and their sub-elements. However, direct application of the optimization techniques in design of hydraulic turbines is impractical due to the requirement of performing computationally expensive analysis of turbines many times during optimization. Metamodels (or surrogate models) that can provide fast response predictions and mimic the behavior of nonlinear simulation models provide a remedy. In this study, simulation-based design of Francis type turbine runner is performed by following a metamodel-based optimization approach that uses multiple types of metamodels. A previously developed computational fluid dynamics-based methodology is integrated to the optimization process, and the results are compared to the results obtained from on-going computational fluid dynamics studies. The results show that, compared to the conventional methods such as computational fluid dynamics-based methods, metamodel-based optimization can shorten the design process time by a factor of 9.2. In addition, with the help of optimization, turbine performance is increased while cavitation on the turbine blades, which can be harmful for the turbine and reduce its lifespan, is reduced.
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Akbarzadeh, Siamak, Hassan Kassem, Renko Buhr, Gerald Steinfeld, and Bernhard Stoevesandt. "Adjoint-based calibration of inlet boundary condition for atmospheric computational fluid dynamics solvers." Wind Energy Science 4, no. 4 (2019): 619–32. http://dx.doi.org/10.5194/wes-4-619-2019.

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Abstract. A continuous adjoint solver is developed for calibration of the inlet velocity profile boundary condition (BC) for computational fluid dynamics (CFD) simulations of the neutral atmospheric boundary layer (ABL). The adjoint solver uses interior domain wind speed observations to compute the gradient of a calibration function with respect to inlet velocity speed and wind direction. The solver has been implemented in the open-source CFD package OpenFOAM coupled with the local gradient-based “CONMIN-frcg” solver of the DAKOTA optimization package. The feasibility of the optimizer output is continuously monitored during the calibration process. The inlet flow profile is considered acceptable only if it can be fitted to a logarithmic or power law function with a tolerance of 3 %. Otherwise, the optimization takes the last fitted profile and asks for a new gradient evaluation. The newly developed framework has been applied in two cases, namely the Ishihara case and Kassel domain. By using the measurements over the hill in the Ishihara case, the method was able to predict the velocity profiles upstream and downstream of the hill accurately. For the Kassel domain, despite the complexity of the site, the method managed to achieve the targeted profile within a reasonable number of the solver calls.
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Serafino, Aldo, Benoit Obert, and Paola Cinnella. "Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics." Algorithms 13, no. 10 (2020): 248. http://dx.doi.org/10.3390/a13100248.

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Efficient Robust Design Optimization (RDO) strategies coupling a parsimonious uncertainty quantification (UQ) method with a surrogate-based multi-objective genetic algorithm (SMOGA) are investigated for a test problem in computational fluid dynamics (CFD), namely the inverse robust design of an expansion nozzle. The low-order statistics (mean and variance) of the stochastic cost function are computed through either a gradient-enhanced kriging (GEK) surrogate or through the less expensive, lower fidelity, first-order method of moments (MoM). Both the continuous (non-intrusive) and discrete (intrusive) adjoint methods are evaluated for computing the gradients required for GEK and MoM. In all cases, the results are assessed against a reference kriging UQ surrogate not using gradient information. Subsequently, the GEK and MoM UQ solvers are fused together to build a multi-fidelity surrogate with adaptive infill enrichment for the SMOGA optimizer. The resulting hybrid multi-fidelity SMOGA RDO strategy ensures a good tradeoff between cost and accuracy, thus representing an efficient approach for complex RDO problems.
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Akolekar, Harshal D., Fabian Waschkowski, Yaomin Zhao, Roberto Pacciani, and Richard D. Sandberg. "Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning." Energies 14, no. 15 (2021): 4680. http://dx.doi.org/10.3390/en14154680.

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Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of `CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow.
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Dissertations / Theses on the topic "Computational Fluid Dynamics, Adjoint Optimization, Turbines"

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Müller, Lasse. "Adjoint-Based Optimization of Turbomachinery With Applications to Axial and Radial Turbines." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/280380/5/contratLM.pdf.

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Numerical optimization methods have made significant progress over the last decades and play an important role in modern industrial design processes. In most cases, gradient-free algorithms are used, which only require the value of the objective function in each optimization step. These methods are robust and can be integrated into a standard design process at low implementation effort. However, in aerodynamic design problems using high-fidelity Computational Fluid Dynamics (CFD), the computational cost is high, especially when a large number of design parameters are used. Gradient-based methods, on the other hand, are particularly suited for problems involving large design spaces and generally converge to a local optimum in a few design cycles. However, the computational efficiency of these methods is mainly determined by the gradient calculation.This thesis presents the development of an efficient gradient-based optimization framework for the aerodynamic design of turbomachinery applications. In particular, the adjoint approach is used to evaluate the gradients of the objective function with respect to all design parameters at low computational cost. The present work covers the various components of the optimization framework, including the solution of the flow governing equations, adjoint-based sensitivity analysis, geometry parameterization, and mesh generation. A substantial part of the thesis describes the implementation and validation of those components. The flow solver is a Reynolds-Averaged Navier-Stokes code applicable to multiblock structured grids. The spatial discretization is realized with a Roe-type upwind scheme with a MUSCL extrapolation for second order spatial accuracy. Viscous fluxes are centrally discretized, and for the turbulence closure problem the Spalart-Allmaras and the Shear-Stress Transport (SST) models are used. The code uses an implicit multistage Runge-Kutta time-stepping scheme, accelerated by local time-stepping and geometric multigrid. The corresponding discrete adjoint solver uses the same time marching scheme as the flow solver and features similar performance characteristics in terms of runtime and memory footprint. The adjoint solver has been implemented primarily by hand with selective use of algorithmic differentiation (AD) to simplify the development. The geometry parameterization is based on B-Spline representations which has two main advantages: (a) the simple integration of geometric constraints for structural requirements, and (b) the connection to Computer-Aided Design (CAD) software for manufacturing. The whole optimization framework is driven by a Sequential Quadratic Programming (SQP) algorithm. The proposed framework has been successfully applied to optimize axial and radial turbines on multiple operating points subject to aerodynamic and geometric constraints. The different studies show the effectiveness of the developed method in terms of improved performances and computational cost. In particular, a comparative study shows that the proposed method is able to find optimized blade shapes at a computational time which is about one order of magnitude lower compared to a gradient-free optimization algorithm.<br>Doctorat en Sciences de l'ingénieur et technologie<br>info:eu-repo/semantics/nonPublished
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Deng, Yun. "Design optimization of a micro wind turbine using computational fluid dynamics." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4098770X.

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Deng, Yun, and 鄧昀. "Design optimization of a micro wind turbine using computational fluid dynamics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4098770X.

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Gallard, François. "Optimisation de forme d’un avion pour sa performance sur une mission." Thesis, Toulouse, INPT, 2014. http://www.theses.fr/2014INPT0031/document.

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Les avions rencontrent de nombreuses conditions d’opérations au cours de leurs vols, comme le nombre de Mach, l’altitude et l’angle d’attaque. Leur prise en compte durant la conception améliore la robustesse du système et finalement la consommation des flottes d’avions. L’optimisation de formes aérodynamiques contribue à la conception des avions, et repose sur l’automatisation de la génération de géométries ainsi que la simulation numérique de la physique du vol. La minimisation de la trainée des formes aérodynamiques doit prendre en compte de multiples conditions d’opération, étant donne que l’optimisation a une unique condition de vol mène a des formes dont la performance se dégrade fortement quand cette condition de vol est perturbée. De plus, la flexibilité structurelle déforme les ailes différemment selon la condition de vol, et doit donc être simulée lors de telles optimisations. Dans cette thèse, la minimisation de la consommation de carburant au cours d’une mission est formulée en problème d’optimisation. Une attention particulière est apportée au choix des conditions d’opérations à inclure dans le problème d’optimisation, étant donne que celles-ci ont un impact majeur sur la qualité des résultats obtenus, et que le cout de calcul est proportionnel à leur nombre. Un nouveau cadre théorique est proposé pour adresser cette question, offrant un point de vue original et surmontant des difficultés révélées par les méthodes a l’état-de-l’ art en matière de mise en place de problèmes d’optimisation multipoints. Un algorithme appelé Gradient Span Analysis (GSA), est proposé pour automatiser le choix des conditions d’opération. Il est base sur la réduction de dimension de l’espace vectoriel engendre par les gradients adjoints aux différentes conditions de vol. Des contributions de programmation a la chaine d’optimisation ont permis d’évaluer les méthodes aux optimisations du profil académique RAE2822 et de la configuration voilure-fuselage XRF-1, représentative des avions de transport modernes. Alors que les formes résultant d’optimisation mono-point présentent de fortes dégradations de performance hors du point de conception, les optimisations multipoints adéquatement formulées fournissent de bien meilleurs compromis. Il est finalement montre que les interactions fluide-structure ajoutent de nouveaux degrés de liberté, et ont un impact sur les optimisations en de multiples conditions de vol, ouvrant des perspectives en matière d’adaptation passive de forme<br>An aircraft encounters a wide range of operating conditions during its missions, i.e. flight altitude, Mach number and angle of attack, which consideration at the design phase enhances the system robustness and consequently the overall fleet consumption. Numerical optimization of aerodynamic shapes contributes to aircraft design, and relies on the automation of geometry generation and numerical simulations of the flight physics. Minimization of aerodynamic shapes drag must take into account multiple operating conditions, since optimization at a single operating condition leads to a strong degradation of performance when this operating condition varies. Besides, structural flexibility deforms the wings differently depending on the operating conditions, so has to be simulated during such optimizations. In the present thesis, the mission fuel consumption minimization is formulated as an optimization problem. The focus is made on the choice of operating conditions to be included in the optimization problem, since they have a major impact on the quality of the results, and the computational cost is proportional to their number. A new theoretical framework is proposed, overcoming and giving new insights on problematic situations revealed by state-of-the-art methods for multipoint optimization problem setup. An algorithm called Gradient Span Analysis is proposed to automate the choice of operating conditions. It is based on a reduction of dimension of the vector space spanned by adjoint gradients obtained at the different operating conditions. Programming contributions to the optimization chain enabled the evaluation of the new method on the optimizations of the academic RAE2822 airfoil, and the XRF-1 wing-body configuration, representative of a modern transport aircraft. While the shapes resulting of single-point optimizations present strong degradations of the performance in off-design conditions, adequately formulated multi-Machmulti- lift optimizations present much more interesting performance compromises. It is finally shown that fluid-structure interaction adds new degrees of freedom, and has consequences on multiple flight conditions optimizations, opening the perspective of passive shape adaptation
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AZEGAMI, Hideyuki, Naoshi NISHIHASHI, Eiji KATAMINE, 秀幸 畔上, 直志 西橋 та 英次 片峯. "抗力最小化・揚力最大化を目的とした定常粘性流れ場の形状最適化". 一般社団法人日本機械学会, 2008. http://hdl.handle.net/2237/21115.

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Ceylanoglu, Arda. "An Accelerated Aerodynamic Optimization Approach For A Small Turbojet Engine Centrifugal Compressor." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611371/index.pdf.

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Centrifugal compressors are widely used in propulsion technology. As an important part of turbo-engines, centrifugal compressors increase the pressure of the air and let the pressurized air flow into the combustion chamber. The developed pressure and the flow characteristics mainly affect the thrust generated by the engine. The design of centrifugal compressors is a challenging and time consuming process including several tests, computational fluid dynamics (CFD) analyses and optimization studies. In this study, a methodology on the geometry optimization and CFD analyses of the centrifugal compressor of an existing small turbojet engine are introduced as increased pressure ratio being the objective. The purpose is to optimize the impeller geometry of a centrifugal compressor such that the pressure ratio at the maximum speed of the engine is maximized. The methodology introduced provides a guidance on the geometry optimization of centrifugal impellers supported with CFD analysis outputs. The original geometry of the centrifugal compressor is obtained by means of optical scanning. Then, the parametric model of the 3-D geometry is created by using a CAD software. A design of experiments (DOE) procedure is applied through geometrical parameters in order to decrease the computation effort and guide through the optimization process. All the designs gathered through DOE study are modelled in the CAD software and meshed for CFD analyses. CFD analyses are carried out to investigate the resulting pressure ratio and flow characteristics. The results of the CFD studies are used within the Artificial Neural Network methodology to create a fit between geometric parameters (inputs) and the pressure ratio (output). Then, the resulting fit is used in the optimization study and a centrifugal compressor with higher pressure ratio is obtained by following a single objective optimization process supported by design of experiments methodology.
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Amoignon, Olivier. "Numerical Methods for Aerodynamic Shape Optimization." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6252.

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Ceze, Marco Antonio de Barros. "Projeto inverso aerodinâmico utilizando o método adjunto aplicado às equações de Euler." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/3/3150/tde-30092008-175753/.

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Um desafio constante no projeto aerodinâmico de uma superfície é obter a forma geométrica que permite, baseado em uma determinada medida de mérito, o melhor desempenho possível. No contexto de projeto de aeronaves de transporte, o desempenho ótimo em cruzeiro é a principal meta do projetista. Nesse cenário, o uso da Dinâmica do Fluidos Computacional como não só uma ferramenta de análise mas também de síntese torna-se uma forma atrativa para melhorar o projeto de aeronaves que é uma atividade dispendiosa em termos de tempo e recursos financeiros. O método adotado para projeto aerodinâmico é baseado na teoria de controle ótimo. Essa abordagem para o problema de otimização aerodinâmica foi inicialmente proposta por Jameson (1997) e é chamada de método adjunto. Esse método apresenta uma grande diminuição de custo computacional se comparado com a abordagem de diferenças finitas para a otimização baseada em gradiente. Essa dissertação apresenta o método adjunto contínuo aplicado às equações de Euler. Tal método está inserido no contexto de um ciclo de projeto inverso aerodinâmico. Nesse ciclo, tanto o código computacional de solução das equações do escoamento quanto o código de solução das equações adjuntas foram desenvolvidos ao longo desse trabalho. Além disso, foi adotada uma metodologia de redução do gradiente da função de mérito em relação às variáveis de projeto. O algorítmo utilizado para a busca do mínimo da função de mérito é o steepest descent. Os binômios de Bernstein foram escolhidos para representar a geometria do aerofólio de acordo com a parametrização proposta por Kulfan e Bussoletti (2006). Apresenta-se um estudo dessa parametrização mostrando suas características relevantes para a otimização aerodinâmica. Os resultados apresentados estão divididos em dois grupos: validação do ciclo de projeto inverso e aplicações práticas. O primeiro grupo consiste em exercícios de projeto inverso nos quais são estabelecidas distribuições de pressão desejadas obtidas a partir de geometrias conhecidas, desta forma garante-se que tais distribuições são realizáveis. No segundo grupo, porém, as distribuições desejadas são propostas pelo projetista baseado em sua experiência e, portanto, não sendo garantida a realizabilidade dessas distribuições. Em ambos os grupos, incluem-se resultados nos regimes de escoamento transônico e subsônico incompressível.<br>A constant endeavor in aerodynamic design is to find the shape that yields optimum performance, according to some context-dependent measure of merit. In particular for transport aircrafts, an optimum cruise performance is usually the designers main goal. In this scenario the use of the Computational Fluid Dynamics (CFD) technique as not only an analysis tool but as a design tool becomes an attractive aid to the time and financial resource consuming activity that is aircraft design. The method adopted for aerodynamic design is based on optimal control theory. This approach to the design problem was first proposed by Jameson (1997) and it is called adjoint method. It shows a great computational cost advantage over the finite difference approach to gradient-based optimization. This dissertation presents an Euler adjoint method implemented in context of an inverse aerodynamic design loop. In this loop both the flow solver and the adjoint solver were developed during the course of this work and their formulation are presented. Further on, a gradient reduction methodology is used to obtain the gradient of the cost function with respect to the design variables. The method chosen to drive the cost function to its minimum is the steepest descent. Bernstein binomials were chosen to represent the airfoil geometry as proposed by Kulfan and Bussoletti (2006). A study of such geometric representation method is carried on showing its relevant properties for aerodynamic optimization. Results are presented in two groups: inverse design loop validation and practical application. The first group consists of inverse design exercises in which the target pressure distribution is from a known geometry, this way such distribution is guaranteed to be realizable. On the second group however, the target distribution is proposed based on the designers knowledge and its not necessarily realizable. In both groups the results include transonic and subsonic incompressible conditions.
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Hayashi, Marcelo Tanaka. "Estudo conceitual do problema adjunto baseado nas equações de Euler para aplicações de otimização aerodinâmica." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3150/tde-28072017-144405/.

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Ao longo da última década o método adjunto tem sido consolidado como uma das mais versáteis e bem sucedidas ferramentas de otimização aerodinâmica e projeto inverso na Dinâmica dos Fluidos Computacional. Ele se tornou uma área de pesquisa por si só, criando uma grande variedade de aplicações e uma literatura prolífica. Entretanto, alguns aspectos relevantes do método permanecem ainda relativamente pouco explorados na literatura. Como é o caso das condições de contorno adjuntas e, mais especificamente, com respeito a fronteiras permeáveis. Esta dissertação discute detalhadamente uma nova forma de tratar o problema de contorno, que tem como objetivo assegurar que as equações adjuntas sejam bem-postas. O principal objetivo da otimização aerodinâmica consiste na tentativa de minimizar (ou maximizar) uma determinada medida de mérito. As aplicações de projeto inverso são desenvolvidas para escoamentos Euler 2-D ao redor de aerofólios, representados com a parametrização CST (Class-Shape function Transformation) proposta por Kulfan e Bussoletti (2006), em regime de vôo transônico e com domínio discretizado por malhas não-estruturadas de triângulos através de um ciclo de projeto, que utiliza o método steepest descent como algoritmo de busca da direção que minimiza (ou maximiza) a função de mérito. As equações adjuntas são derivadas na sua formulação contínua e suas condições de contorno são determinadas por equações diferenciais características adjuntas e relações de compatibilidade compatíveis com as variações realizáveis da física do escoamento. As variáveis adjuntas são, então, vistas como forças de vínculo generalizadas, que asseguram a realizabilidade de variações do escoamento.<br>Over the last decade the adjoint method has been consolidated as one of the most versatile and successful tools of aerodynamic optimization and inverse design in Computational Fluid Dynamics. It has become a research area of its own, spawning a large variety of applications and a prolific literature. Yet, some relevant aspects of the method remain relatively less explored in the literature. Such is the case with the adjoint boundary conditions and, more specifically, with regard to permeable boundaries. This dissertation discusses at length a novel approach to the boundary problem, which aims at ensuring the well-posedness of the adjoint equations. The main goal of aerodynamic optimization consists in attempting to minimize (or maximize) a certain mesure of merit. The inverse design applications are developed for 2-D Euler flows around airfoils, represented with the CST (Class-Shape function Transformation) parameterization proposed by Kulfan and Bussoletti (2006), in the transonic flight regime and domain discretized by triangle unstructured meshes in a design loop which makes use of the steepest descent method as search direction that minimizes (or maximizes) the mesure of merit. Adjoint equations are derived in the continuous formulation and their boundary conditions are determined by adjoint characteristic differential equations and compatibility relations. The latter are derived so as to be compatible with the realizable variations of physical quantities. The adjoint variables are seen as generalized constraint forces, which ensure the realizability of flow variations.
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Berguin, Steven Henri. "A method for reducing dimensionality in large design problems with computationally expensive analyses." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53504.

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Strides in modern computational fluid dynamics and leaps in high-power computing have led to unprecedented capabilities for handling large aerodynamic problem. In particular, the emergence of adjoint design methods has been a break-through in the field of aerodynamic shape optimization. It enables expensive, high-dimensional optimization problems to be tackled efficiently using gradient-based methods in CFD; a task that was previously inconceivable. However, adjoint design methods are intended for gradient-based optimization; the curse of dimensionality is still very much alive when it comes to design space exploration, where gradient-free methods cannot be avoided. This research describes a novel approach for reducing dimensionality in large, computationally expensive design problems to a point where gradient-free methods become possible. This is done using an innovative application of Principal Component Analysis (PCA), where the latter is applied to the gradient distribution of the objective function; something that had not been done before. This yields a linear transformation that maps a high-dimensional problem onto an equivalent low-dimensional subspace. None of the original variables are discarded; they are simply linearly combined into a new set of variables that are fewer in number. The method is tested on a range of analytical functions, a two-dimensional staggered airfoil test problem and a three-dimensional Over-Wing Nacelle (OWN) integration problem. In all cases, the method performed as expected and was found to be cost effective, requiring only a relatively small number of samples to achieve large dimensionality reduction.
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Books on the topic "Computational Fluid Dynamics, Adjoint Optimization, Turbines"

1

Anderson, W. Kyle. Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation. National Aeronautics and Space Administration, Langley Research Center, 1997.

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V, Venkatakrishnan, and Langley Research Center, eds. Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation. National Aeronautics and Space Administration, Langley Research Center, 1997.

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1934-, Jameson Antony, and Research Institute for Advanced Computer Science (U.S.), eds. Supersonic wing and wing-body shape optimization using an adjoint formulation. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

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1934-, Jameson Antony, and Research Institute for Advanced Computer Science (U.S.), eds. Supersonic wing and wing-body shape optimization using an adjoint formulation. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

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1934-, Jameson Antony, and Research Institute for Advanced Computer Science (U.S.), eds. Supersonic wing and wing-body shape optimization using an adjoint formulation. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

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Book chapters on the topic "Computational Fluid Dynamics, Adjoint Optimization, Turbines"

1

Kim, Hyoung-Jin, Daisuke Sasaki, Shigeru Obayashi, and Kazuhiro Nakahashi. "Aerodynamic Optimization of Supersonic Transport Wing Using Unstructured Adjoint Method." In Computational Fluid Dynamics 2000. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56535-9_88.

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Carpentieri, Giampietro, Michel J. L. van Tooren, and Barry Koren. "Comparison of Exact and Approximate Discrete Adjoint for Aerodynamic Shape Optimization." In Computational Fluid Dynamics 2006. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92779-2_82.

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Conference papers on the topic "Computational Fluid Dynamics, Adjoint Optimization, Turbines"

1

Vorspel, Lena, Bernhard Stoevesandt, Joachim Peinke, and Ivan Herraez. "Towards the optimization of wind turbine rotor blades by means of computational fluid dynamics and the adjoint approach." In 34th AIAA Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3728.

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Wang, Cheng-Zhang, Kondala Rao Nagisetty, Federico Montanari, and D. Chris Hill. "Application of Adjoint Solver to Optimization of Fin Heat Exchanger." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43293.

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This paper presents an investigation to improve the design of a generic fin heat exchanger, using novel discrete adjoint solver tools in the computational fluid dynamics (CFD) software FLUENT. A baseline design is analyzed initially to evaluate flow resistance and heat transfer. Optimization is conducted by deploying the adjoint solver. The heat load and the drag force are combined into an objective function using a Reynolds analogy approach. Sensitivities of the objective function to geometric changes are predicted by the adjoint, and then the mesh is morphed, and the predictions are verified by the full CFD solutions. Predetermined, engineering driven, geometric changes are explored and compared, and the range of validity of the predictions is evaluated. An algorithm is then developed to implement steepest descent, constrained optimization based on the adjoint solution. The algorithm is applied iteratively on the fin heat exchanger, and a comparison is performed between the change in objective function predicted by the adjoint, and that calculated in full CFD solutions on morphed meshes. The insight gained on the directions of design changes and attending quantitative improvement of the design objective function is very useful to guide the optimization process. This is enabled by the adjoint solver’s capability to robustly evaluate the sensitivities of the objective function to all solution variables, and predict changes in observables.
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Zhang, Chaolei, and Zhenping Feng. "Aerodynamic Shape Design Optimization for Turbomachinery Cascade Based on Discrete Adjoint Method." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45805.

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Achieving higher aerodynamic performance in terms of efficiency, pressure ratio or stable operation range has been of interest to both researchers and engineers in the field of turbomachinery. The design of optimal shaped aerodynamic configurations based on Computational Fluid Dynamics (CFD) and predefined targets can be obtained by using deterministic search algorithms, which need to calculate the first and second order sensitivities of the objective function with respect to the design variables. With the characteristics of quick and exact sensitivity analysis, as well as less computational resource requirement, the adjoint method has become a research focus in aerodynamic shape design optimization over the past decades. In this paper, a discrete adjoint solver was developed and validated based on an in-house flow solver code. Moreover, a turbomachinery cascade optimization design system was established by coupling the flow solver, the discrete adjoint solver, the parameterization technology, the grid generation technology and the gradient-based optimization algorithms. During the development process of the discrete adjoint solver, the automatic differentiation tool was used in order to ease the construction of the discrete adjoint system based on the flow solver code. However, in order to save the memory requirement and to reduce the computational cost, the automatic differentiation tool was used selectively to build the fundamental subroutines. The top-most module of the discrete adjoint solver was established based on the discrete adjoint theory and the automatic differentiation technology manually. The treatments of the discontinuity in the flow field, such as strong shocks, and the imposition of strong boundary conditions which were implemented in the adjoint solver were discussed in detail. At the same time, several technologies were used to accelerate convergence. Based on the optimization system, a typical 2D transonic turbomachinery cascade was optimized under the viscous flow environment. The optimization results were analyzed in detail. The validity and efficiency of the present optimization design system were proved.
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Giebmanns, A., J. Backhaus, C. Frey, and R. Schnell. "Compressor Leading Edge Sensitivities and Analysis With an Adjoint Flow Solver." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94427.

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Based on the results of a prior study about fan blade degradation, which state a noticeable influence of small geometric changes on the fan performance, an adjoint computational fluid dynamics method is applied to systematically analyze the sensitivities of fan blade performance to changes of the leading edge geometry. As early as during manufacture, blade geometries vary due to fabrication tolerances. Later, when in service, engine operation results in blade degradation which can be reduced but not perfectly fixed by maintenance, repair and overhaul processes. The geometric irregularities involve that it is difficult to predict the blade’s aerodynamic performance. Therefore, the aim of this study is to present a systematic approach for analyzing geometric sensitivities for a fan blade. To demonstrate the potential, two-dimensional optimizations of three airfoil sections at different heights of a transonic fan blade are presented. Although the optimization procedure is limited to the small area of the leading edge, the resulting airfoil sections can be combined to a three-dimensional fan blade with an increased isentropic efficiency compared to the initial blade. Afterwards, an adjoint flow solver is applied to quasi-three-dimensional configurations of an airfoil section in subsonic flow with geometric leading edge variations in orders representative for realistic geometry changes. Validations with non-linear simulation results demonstrate the high quality of the adjoint results for small geometric changes and indicate physical effects in the leading edge region that influence the prediction quality.
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Kline, Heather L., Francisco Palacios, Thomas D. Economon, and Juan J. Alonso. "Adjoint-Based Optimization of a Hypersonic Inlet." In 22nd AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-3060.

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van Schrojenstein Lantman, Marnix, and Krzysztof Fidkowski. "Adjoint-Based Optimization of Flapping Kinematics in Viscous Flows." In 21st AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2848.

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Thompson, Peter M., Trevor T. Robinson, and C. Armstrong. "Efficient CAD-based Aerodynamic Design Optimization with Adjoint CFD Data." In 21st AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2847.

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Zingg, David, Timothy Leung, Laslo Diosady, Anh Truong, Samy Elias, and Marian Nemec. "Improvements to a Newton-Krylov Adjoint Algorithm for Aerodynamic Optimization." In 17th AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-4857.

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Dreyer, James, and Luigi Martinelli. "Hydrodynamic shape optimization of propulsor configurations using continuous adjoint approach." In 15th AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-2580.

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Nielsen, Eric, Boris Diskin, and Nail Yamaleev. "Discrete Adjoint-Based Design Optimization of Unsteady Turbulent Flows on Dynamic Unstructured Grids." In 19th AIAA Computational Fluid Dynamics. American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-3802.

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