Topology optimization of fluid flow by using Integer Linear Programming

Souza, Bruno Carlos de; Yamabe, Paulo Vinícius Miyuki; Sá, Luís Fernando Nogueira de; Ranjbarzadeh, Shahin; Picelli, Renato; Silva, E. C. N.

doi:10.1007/s00158-021-02910-6

Cited by 27 publications

(7 citation statements)

References 27 publications

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“…In their formulation, the initial optimization problem is transformed into SLP, which is then solved by integer linear programming (ILP). They expanded binary structures method into continuum structures subject to fluid structure, fluid flow, and thermal expansion loads via such a fundamental innovation [135][136][137]. And, they released the open-source code based on Matlab TM for distribution [138].…”

Section: Augmented Lagrangementioning

confidence: 99%

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

Long,

Saeed,

Zhang

et al. 2024

CMES

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This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures. These structures, commonly encountered in engineering applications, often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables. As a result, sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges. Over the past several decades, topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds. In comparison to the large-scale equation solution, sensitivity analysis, graphics post-processing, etc., the progress of the sequential approximation functions and their corresponding optimizers make sluggish progress. Researchers, particularly novices, pay special attention to their difficulties with a particular problem. Thus, this paper provides an overview of sequential approximation functions, related literature on topology optimization methods, and their applications. Starting from optimality criteria and sequential linear programming, the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables. In addition, recent advancements have led to the emergence of approaches such as Augmented Lagrange, sequential approximate integer, and non-gradient approximation are also introduced. By highlighting real-world applications and case studies, the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area. Furthermore, to provide a comprehensive overview, this paper offers several novel developments that aim to illuminate potential directions for future research.

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Section: Augmented Lagrangementioning

confidence: 99%

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

Long,

Saeed,

Zhang

et al. 2024

CMES

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show abstract

“…However, gray scale in the results of modeling the fluid flow field can make the contour of the fluid area inaccurate, which often occurs in the design of double channels and elbows. To avoid gray scale and obtain clear boundaries, Souza et al [7] proposed applying the Topology of Binary Structures (TOBS) in fluid flow design. In fluid topology optimization design, considering the density method, the material distribution characteristics are preserved, the gray problem is successfully eliminated, and the boundary between fluid and solid becomes clear.…”

Section: Research Status Of Optimal Design Of Fluid Machinerymentioning

confidence: 99%

A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques

Deng

Liu

et al. 2023

JMSE

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The design of fluid machinery is a complex task that requires careful consideration of various factors that are interdependent. The correlation between performance parameters and geometric parameters is highly intricate and sensitive, displaying strong nonlinear characteristics. Machine learning techniques have proven to be effective in assisting with optimal fluid machinery design. However, there is a scarcity of literature on this subject. This study aims to present a state-of-the-art review on the optimal design of fluid machinery using machine learning techniques. Machine learning applications primarily involve constructing surrogate models or reduced-order models to explore the correlation between design variables or the relationship between design variables and performance. This paper provides a comprehensive summary of the research status of fluid machinery optimization design, machine learning methods, and the current application of machine learning in fluid machinery optimization design. Additionally, it offers insights into future research directions and recommendations for machine learning techniques in optimal fluid machinery design.

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“…The di culty of topology optimization is mainly due to the huge amount of computation. When convective heat transfer is included in a 3D space, the computation of the topology optimization can be tedious (Alexandersen et al, 2016;Souza et al, 2021). In order to reduce the di culty of optimization, it usually has to simplify the process of topology optimization, such as considering only a 2D model or ignoring convection.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization of heat sinks in half-open space: a study on design domain

Yu,

Liu,

2024

Preprint

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Efficient heat sinks are the key components of heat dissipation devices. Although topology optimization can theoretically design high-performance heat sinks, screening a design domain in an open space is still a challenge. This work studies the topology optimization of three-dimensional (3D) heat sinks for LED chips using the variable density method, without considering air convection to save computing resources. A theoretical analysis proposes that the design domain should be approximately a hemisphere. Topology optimization is performed in three shaped design domains, namely cylindrical, conical frustum, and inverted conical frustum. By 3D printing three topology-optimized heat sinks, their actual heat dissipation effects on LED chips are compared. The experiment and simulation results consistently show that the heat sink in the conical frustum design domain has the best heat dissipation effect. Equipped with such a heat sink, a 5-W LED chip is 5.1°C cooler than that with the traditional finned heat sink. Our results show that a well-selected design domain can effectively balance the heat conduction and heat exchange, eventually leading to a better heat sink.

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Topology optimization of fluid flow by using Integer Linear Programming

Cited by 27 publications

References 27 publications

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques

Topology optimization of heat sinks in half-open space: a study on design domain

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