Multi-objective optimization of guide vanes for axial flow cyclone using CFD, SVM, and NSGA II algorithm

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

Battery thermal management has significant effect on the performance of electric vehicles (EVs) under high current rates. In this research, a comprehensive thermal analysis and multi-objective optimization design framework is proposed to enhance the thermal performance of a novel air–liquid cooling coupled battery pack under higher discharging rate (3C). Computational fluid dynamics (CFD) numerical calculation is utilized to compare the cooling efficiency of the battery pack designs. Furthermore, a surrogate model is generated by using Latin hypercube sampling (LHS) and support vector machine. The design parameters include different mini-channels’ mass flowrates and the air flow inlet velocity, the objectives are the temperature rise, temperature distribution, and the energy consumption. Sensitivity analysis results indicate that the air flow inlet velocity is the main factor affecting the temperature rise and temperature distribution, while the mass flowrates of mini-channels have important influence on the pressure drop. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is used to select the optimal battery pack design, the maximum temperature, and temperature standard deviation (TSD) get improved by 1.8 K and 0.06 K, respectively. And the energy consumption of the cooling system can be controlled within the appropriate range after optimization design.

In order to obtain higher quality silicon single crystal, a hybrid strategy for modeling of Czochralski silicon crystal growth and optimizing of process parameters is presented in the paper. The hybrid strategy includes the computational fluid dynamics (CFD) method, neural network of group method of data handing (GMDH), and improved nondominated sorting genetic algorithm II (NSGA‐II). The shape variable of solid‐liquid interface h and the defect evaluation criteria V/G are set to objective functions according to engineering experience and process requirement. The polynomial of the objective functions is produced by GMDH and CFD. Ultimately, an improved elitist strategy and crowding distance NSGA‐II is proposed in the paper to obtain the Pareto optimum solution by the objective functions identified by the GMDH. Compared with other optimization algorithms, the improved NSGA‐II can increase the lateral diversity and the uniform distribution of the nondominated solutions. Engineering validation proved that the proposed hybrid strategy can effectively solve the complex uncertain multiobjective optimization problem of system and provides a new method for obtaining high‐quality crystal growth process parameters.

Multiobjective optimization of building energy consumption based on BIM-DB and LSSVM-NSGA-II

The design of rigid vortex generators (RVG) influences the thermal performance of various technologies. We employed Discrete Adjoint-Based Optimization to show the optimal development of vortex generators. Under turbulent flow conditions, different bi-objective functions on the RVG design were examined. Specifically, we aimed at an optimal RVG shape that minimizes the pressure drop and maximizes the local heat transfer in a rectangular channel. We show that an optimal design of an RVG can be obtained using computational fluid dynamics in conjunction with the Pareto Front at a computational cost of the order ~O(10^{-1}). We obtained three essential vortex generator shapes based on the RVG morphing technique. Compared to the baseline geometry of a delta winglet pair DWP, the first morphed design reduced the pressure drop by 39%, however, at the expense of a 21% reduction in the Nusselt number. The second vortex generator design enhanced the heat transfer by 18%, however, at the cost of a significant increase in pressure drop of about 40%. The final morphed design achieved the highest thermal performance factor of 1.28, representing a heat transfer enhancement of 6% with a moderate increase in pressure drop of about 13% compared to DWP vortex generators. Furthermore, we investigated the effect of introducing different size holes on the mass reduction of vortex generators and their thermal performances. The mass of vortex generators can be reduced by 9% and with an increase of 7% in thermal performance factor concerning the DWP baseline. The findings of this study will lead to highly efficient lightweight heat exchangers.

• VTPS Efficiency: The vane-type pipe separator (VTPS) is effective for compact, high-efficiency oil-water separation in constrained oilfield environments. • Advanced Modeling: A computational framework using the AIAD model within an Euler-Euler multiphase context is developed and experimentally validated. • Key Parameters Identified: Sensitivity analysis reveals guide vane angle and oil fraction as dominant factors affecting pressure drop and oil recovery. • Optimization Approach: Multi-objective optimization using SPEA2 and TOPSIS identifies the best trade-off between oil recovery and hydraulic loss. • Optimal Design Achieved: An optimal VTPS setup (25.4° vane angle, 3.57 m³/h flow rate, 18.4% oil fraction, 0.12 split ratio) achieves a 0.162 oil recovery factor at 2422 Pa pressure drop. Efficient oil-water separation is increasingly vital in mature oilfields, where enhanced water production challenges conventional downhole separation technologies. The vane-type pipe separator (VTPS) offers a promising solution due to its compact configuration and superior phase separation efficiency in constrained environments. However, the inherent complexities of swirling multiphase flow necessitate advanced modeling approaches for performance optimization. In this study, a comprehensive computational framework based on the Algebraic Interfacial Area Density (AIAD) model within an Euler-Euler multiphase context is developed and validated against benchmarked experimental data. Sensitivity analysis employing response surface methodology (RSM) and global-local indices quantifies the influence of key decision parameters, including guide vane installation angle, inlet flow rate, oil fraction, and split ratio, on separator performance. The study elucidates the parametric interplay affecting pressure drop and oil recovery, revealing guide vane angle and oil fraction as the dominant factors for hydraulic and separation performance, respectively. A multi-objective optimization is conducted using the Strength Pareto Evolutionary Algorithm 2 (SPEA2), with final selection by TOPSIS, to maximize oil recovery while minimizing hydraulic losses. The proposed methodology identifies an optimal configuration with a 25.4° vane angle, 3.57 m³/h flow rate, 18.4% oil fraction, and 0.12 split ratio, achieving a 0.162 oil recovery factor at a pressure drop of 2422 Pa. These findings provide actionable guidelines for VTPS design and operation, facilitating more efficient separation in oilfield applications and demonstrating the AIAD model's efficacy for industrial-scale multiphase flow optimization.

Effect of ship motion on separation efficiency in crude oil separator with coalescer

Computational fluid dynamics (CFD) models are powerful computational tools to simulate urban-landscape scale atmospheric dispersion events. They are proven to be very useful for security management and emergency response. Essential inputs to CFD models include landscape characteristics, which are often captured by various GIS data layers. While it is logical to couple GIS and CFD models to take advantage of available GIS data and the visualization and cartographic rendering capabilities of GIS, the integration of the two tools have been minimal. In this paper, we took the first step to evaluate the use of GIS data in CFD modeling. Specifically, we explore how efficient is to use GIS data in CFD models and how sensitive the CFD results are to different GIS data formats. Using campus topography and building data, and the FEFLO-URBAN CFD model, we performed atmospheric release simulations using topographic data in contour and raster formats. We found that using raster format was quite efficient and contour data required significant effort. Though the simulation outputs from the two data formats were not identical, their overall outcomes were similar and did not post alarming discrepancies. We concluded that using GIS data have tremendous potential for CFD modeling.

Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multi-objective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework, which integrates three steps. In the first step, we establish surrogate models for the constraints using Gaussian processes (GPs) and employ multi-objective Bayesian optimization to search for feasible points that satisfy the constraints. In the second step, we establish surrogate models for the objective function and constraints using GPs and utilize constrained multi-objective Bayesian optimization to search for an approximate Pareto front. In the third step, we perform a local search based on the approximate Pareto front. By employing the trust region filter method, we construct quadratic models for each constraint and objective function and refine the Pareto front to achieve local optimality. This framework demonstrates the efficiency of Bayesian optimization and the local optimality of the trust region method. A comparison with the popular evolutionary algorithm, Nondominated Sorting Genetic Algorithm II (NSGA-II), showed that this framework had a higher hypervolume of the Pareto front while halving the runtime and reducing the number of simulations by a factor of 20.

Gas cyclone has two main parameters for evaluating separation performance, separation efficiency and pressure drop through cyclone. They are closely influenced by the geometrical design variables of the cyclone. This study performed optimization of the cyclone performance for the cyclone shape based on computational fluid dynamics (CFD), GMDH type neural network and genetic algorithm (GA). First, CFD was used to obtain the data of the cyclone performance parameters. As result of the CFD validation, the errors of the reference model and CFD were 0.5 % and 2 % for the pressure drop and the separation efficiency. Secondly, the meta-model of the cyclone performance was derived by using GMDH algorithm based on supervised learning of machine learning. The fitness of the modelling results was shown using the correlation coefficient. As results of the GMDH, the correlation coefficients of meta models of the separation efficiency and the pressure drop were 98.9 %, 99.7 %, respectively. Finally, we performed optimization of the meta model by applying GA. When the optimal point was compared with the reference model, the performance of the optimal point was improved by 24.31 % and 8.32 % for pressure drop and the separation efficiency, respectively.

Multi-objective optimization of hydrocyclones using meta-heuristic algorithms and preference-informed decision-making

Vapor separator is a vital component in liquid-dominated steam field equipment. While various designs exist, the currently employed separator design was not so effective on separation and there is limitation in understanding the fluid behavior within the separator. Challenges arise from the difficulties in predicting the pressure distribution, flow regime and separation efficiency inside the separator vessel. Owing to this complexity, computational fluid dynamics (CFD) simulation of two-phase fluid movement inside a vapor separator was carried out using Cosmos Express software package. The modifications made to the existing vapor separator were done considering the major mechanisms such as gravity separation, inertial separation, centrifugal separation and surface tension force. This paper describes the CFD analysis of a modified vapor separator model. The design modification made in this vapor separator was to increase the efficiency of the system by reducing its drawbacks. The effect of turbulence and pressure drop on the existing vapor separator design was studied while improving their efficiency to 100% and 99.5% in two different designs. The separator efficiency was calculated by injecting water droplets after a converged solution was achieved. Though, pressure drop inside the vapor chamber is higher (1344.91 N m−2) for the first design compared to later having a lower value of 817.68 N m−2. CFD simulation results demonstrated a promising method to optimize the separator design.

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

A scheme of function approximation based on support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGAII) is proposed. By combining structural risk minimization principle of SVM, computational complexity of kernel function and function approximation error norm as a multi-objective optimization problem, parameters and kernel function for function approximation are automatically chosen by adopting NSGAII. Simulation shows the scheme has higher precision and less complex kernel function than conventional SVM function approximation

A novel multi-objective optimization scheme is implemented to enhance the heat transfer characteristics and to reduce pressure drop of heat exchanger in this article. The heat transfer efficiency and pressure drop of the finned heat exchanger are considered as the optimal objective function through the fine-tuning of the heat exchanger’s fin spacing and fin angle. Numerical simulations of the prototype heat exchanger well agree with the experimental findings. The fin spacing and fin angles of the heat exchanger are manipulated as the optimization variables to attain the maximum Nusselt number and the minimum pressure drop. The nonlinear fitting of the data is performed using an Artificial Neural Network (ANN) to obtain the establishment of two predictive models. The models are optimized using a multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II), ultimately yielding a Pareto frontier curve. Two excellent optimization schemes can be obtained for heat exchanger. The Nusselt number of the optimized model rises as much as 4% when the pressure drop is almost consistent with the heat transfer of the original heat exchanger. The pressure drop of the optimized model reduces as much as 9% when the Nusselt number is well consistent with the drag force of the original heat exchanger. The energy efficiency is effectively improved by the optimization models of these two types of heat exchangers and the energy-saving goals are achieved through multi-objective optimization using NSGA-II.