New Evolutionary Techniques for Optimization of Energy Systems Utilizing Computational Fluid Dynamics

Efficient reduced order model for heat transfer in a battery pack of an electric vehicle

Simulation-based optimization of thermal systems

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

Compact heat exchangers have been widely used in various applications in thermal fluid systems including automotive thermal fluid systems. Radiators for engine cooling systems, evaporators and condensers for HVAC systems, oil coolers and inter coolers are typical examples that can be found in ground vehicles. Recent development of nanotechnology brings out a new heat transfer coolant called 'nanofluids'. These fluids exhibit larger thermal properties than conventional coolants (water, ethylene glycol, engine oil etc.) due to the presence of suspended nanosized particles in them such as Al2O3, Cu, CuO, TiO2 etc. In this paper, a theoretical analysis was carried with the e-NTU rating method by using Al2O3 + H2O nanofluid as coolant on flat tube plain fin compact heat exchanger and different characteristics are graphically presented.

Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems.

Computational fluid dynamics (CFD) simulations are a fundamental tool in aerodynamic design. Unfortunately, accurate, high-fidelity CFD models may be computationally too expensive to conduct the design using numerical optimization procedures. Recently, variable-fidelity optimization algorithms have attracted attention for their ability to reduce high CPU-cost related to the design process solely based on accurate CFD models. Low-fidelity simulation models are the most critical components of such algorithms. They normally employ the same CFD solver as the high-fidelity model but with reduced discretization density and reduced number of flow solver iterations. Typically, the selection of the appropriate model parameters has only been guided by the designer experience. In this chapter, an automated low-fidelity model selection technique is described. By defining the model setup task as a constrained nonlinear optimization problem, suitable grid and flow solver parameters are obtained. The approach is compared to two conventional methods of generating a family of variable-fidelity models. Comparison of the standard and the proposed approach is carried out in the context of aerodynamic design of transonic airfoils using a multi-level optimization algorithm. The results obtained for several test cases indicate that the automated model generation may lead to significant computational savings of the CFD-based airfoil design process. Illustration of the entire optimization cycle involving automated low-fidelity model preparation and B-spline-parameterized airfoil design using space mapping algorithm is also provided.

Multi-objective optimization of U-type air-cooled thermal management system for enhanced cooling behavior of lithium-ion battery pack

This research presents a comprehensive computational framework integrating Computational Fluid Dynamics (CFD) with Adaptive Neuro-Fuzzy Inference System (ANFIS) for parameter-driven efficiency evaluation of parabolic trough solar thermal collectors. The methodology combines high-fidelity CFD simulations with advanced ANFIS machine learning techniques to predict thermal performance parameters across diverse operational conditions. A validated CFD model was developed using ANSYS Fluent to simulate the complex thermal and fluid dynamic behavior within parabolic trough collector systems, generating 920 numerical datasets encompassing varying flow rates, inlet temperatures, solar irradiation levels, and geometric configurations. The ANFIS model demonstrated exceptional predictive capabilities, achieving coefficient of determination (R²) values of 0.94 for thermal efficiency predictions. The integrated CFD-ANFIS framework reduces computational time by 82% compared to traditional CFD approaches while maintaining prediction accuracy within 4.2% error bounds. Key findings indicate that solar irradiation intensity and heat transfer fluid mass flow rate are the most influential parameters affecting collector efficiency, with optimal performance achieved at Reynolds numbers between 10000-15000. The developed framework enables rapid parametric optimization and real-time performance assessment for parabolic trough solar thermal collector systems, providing significant advantages for design engineers and system operators in concentrating solar power applications.

The active clearance control system (ACC) of turbine is one of the core components,which can reduce the fuel consumption of civil aero-engine. And the tip-clearance change is greatly affected by the aerodynmaic performance of intake grille. To improve the performance, in this work, an aerodynamic optimization method of intake grille is proposed. This method is divided into three parts, such as the parameterization, the fitness evaluation and the optimization algorithm. Based on the commercial software NX 12.0, the grille is parameterized according to seven key geometric parameters. In order to evaluate the aerodynamic performance of grille accurately, a special fitness function is presented and figured out with the help of an adaptive back-propagation multi-layer feedforward artificial neural network (BP-MLANN). Lastly, utilizing the continuous swarm particle optimization-modified very fast simulated annealing algorithm (CPSO-MVFSA), the optimal aerodynamic performance of intake grille is achieved. Through analyzing the aerodynamic experiments and the computational fluid dynamic (CFD) simulation of intake grille, it is found that the aerodynamic performance of the optimized grille is better than that of the original. Therefore, this aerodynamic optimization method is feasible to improve the aerodynamic performance of intake grille.

In thermal systems, combination of PCMs with porous fillers with high thermal conductivity is a proper method to improve their performance. In photovoltaic thermal (PVT) systems, as porous fillers are introduced to the collector tube and PCM bulk, the thermal efficiency increases. Moreover, since the temperature of the PV unit has dropped, the electrical efficiency has improved too. However, the properties of the porous insertion, i.e., permeability and porosity, might affect the pumping work, and consequently, electrical efficiency. In the present study, the effect of metal foam insertions on thermal and electrical performances is analyzed using a computational fluid dynamics (CFD) method. The results show that installing metal foams with porosities of 0.70–0.94 inside the collector and PCM container of a PVT system improves the thermal efficiency without any considerable decrement in electrical efficiency. When copper foam with a porosity of 0.705 is installed within the collector and PCM tank, the highest thermal efficiency of 66.70% is achieved; while, when copper foam with a porosity of 0.94 is used, the maximum electrical efficiency of 13.24% is resulted.

Multi-objective design optimization of battery thermal management system for electric vehicles

Computational Fluid Dynamics (CFD) is becoming fundamental to predict turbomachinery performance. However, only using advanced numerical models coupled with high fidelity grid generation is possible to reach a very good matching with test data. In this regard, secondary flow modeling plays a critical role in the accuracy of performance prediction for centrifugal compressor stages. This study analyses the effects of cavity models on centrifugal compressor stages performance across the full range of impeller flow coefficients used in common industrial applications. Both bi-dimensional low flow coefficients with splitter and non splitter blades and three-dimensional high flow coefficients stages have been used as test cases to compare the numerical prediction with test data. Furthermore the effects of secondary flows modeling have been assessed when comparing detailed flow features with advanced experimental data both in terms of 1D profiles and 2D maps. The effects of cavity flows modeling is growing, as expected, moving to very low flow coefficients, reaching several points of difference in efficiency calculation with respect to simpler models. Furthermore, the agreement with experimental data is very good both in terms of overall performance and detailed flow features. Finally, the high fidelity CFD is capable to give deep in-sides into the flow evolution inside the machine allowing aero designers to design centrifugal compressor stages with higher performance. It should be remarked here that a good matching of CFD prediction with test data is possible only by using high fidelity models.

Multi-objective optimization of a novel hybrid battery thermal management system using response surface method

CHAPTER 5 - Fluid and Thermal Systems

Expedited constrained multi-objective aerodynamic shape optimization by means of physics-based surrogates