Thermal–hydraulic enhancement of shell-and-tube heat exchangers through intelligent design of sinusoidal inserts using hybrid AI framework integrating GA-ANN and multi-objective arithmetic optimization

A 2D model of a pin-fin heat exchanger was optimized using the discrete adjoint method in ANSYS Fluent 16. The initial heat exchanger shape was modeled as staggered cylinders in a cross-flow. Two observables were monitored during the optimization cycles: the heat transfer and the pressure drop, and the objectives are the maximization of heat transfer and the minimization of pressure drop. However, improving the performance of the heat exchanger poses its own challenges since the heat transfer and pressure drop are usually two contradicting observables. In order to successfully improve both observables, single objective and multi-objective shape optimizations were studied. Both single and multi-objective optimizations were conducted under steady laminar flow conditions at Re = 10 and Re = 100. The single objective optimizations were done for different step sizes of the geometry change, e.g. different changes of pressure drop or heat transfer. While the optimized observable was set to improve linearly, the other unconstrained observable shows a nonlinear deterioration. The multi-objective optimizations were performed for different weight factors, leading to different end shapes. For the final optimized geometry, we could achieve up to 11% reduction in pressure drop and 11% increase in heat transfer.

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.

Multi-objective shape optimization of a plate-fin heat exchanger using CFD and multi-objective genetic algorithm

Increasing efficiency and decreasing cost are the main purposes in the design of the power generation systems. In this study two hybrid systems: solid oxide fuel cell (SOFC)-gas turbine (GT) and SOFC-GT-steam turbine (ST); are considered. Increasing the SOFC input temperature causes thermodynamics improvement in the hybrid system operation. For this purpose, using two set of SOFC reactants heat exchangers (primary heat exchangers and secondary heat exchangers) are recommended. Selection of The primary heat exchangers output temperature and therefore the secondary heat exchangers input temperature (heat exchangers mid-temperatures) influences on the thermodynamics and economics operation of the hybrid system. This work shows that the annualized cost (ANC) and the levelized cost of energy (LCOE) act in conflict with each other. The MatLab genetic optimization algorithms are used to obtain the optimum solutions. The maximum achievable efficiency is 0.599 and the minimum LCOE is 0.0163 $/kWh. Also results show that the heat exchangers mid-temperature of air has the main role in the operation of the hybrid system.

Heat removal capacity, coolant pumping pressure drop and surface temperature non-uniformity are three major challenges facing single-phase flow microchannel compact heat exchangers. In this paper multi-objective optimization has been performed to increase heat removal capacity, and decrease pressure drop and temperature non-uniformity in single-flow microchannels. Three-dimensional (3D) 4-floor branching networks have been applied to increase heat removal capacity of a microchannel from silicon substrate (15×15×2 mm). Each floor has four different branching sub-networks with opposite flow direction with respect to the next one. Each branching network has four inlets and one outlet. However, branching patterns of each of these sub-networks could be different from the others. Conjugate heat transfer analysis has been performed by developing a software package which uses quasi-1D thermo-fluid analysis and a 3D steady heat conduction analysis. These two solvers are coupled through their common boundaries representing surfaces of the cooling microchannels. Using quasi-1D solver significantly decreases computing time and its results are in good agreement with 3D Navier-Stokes equations solver for these types of application. The analysis package is capable of generating 3D branching networks with random topologies. 1341 random cooling networks were simulated using this analysis package. Multi-objective optimization using modeFrontier software was performed using response surface approximation and genetic algorithm. Diameters and branching pattern of each sub-network and coolant flow direction on each floor were design variables of multi-objective optimization. Maximizing heat removal capacity, minimizing pressure drop and temperature non-uniformity on the hot surface were three simultaneous objectives of the optimization. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with 3D 4-floor microchannel cooling networks.

A novel PINNs based surrogate model for multi-objective reliability-based design optimization of airfoil-shaped printed circuit heat exchangers

Thermo-economic assessment and multi-objective optimization of organic Rankine cycle driven by solar energy and waste heat

Waste heat recovery (WHR) from aeroengines via compact organic Rankine cycle (ORC) units may increase the fuel efficiency of air transportation. Heat exchangers are arguably the key components of ORC systems for aeronautical applications and their design must be optimized to guarantee the best trade-off between fluid pressure drop, weight and induced aircraft drag. At present, no heat exchangers design guidelines are available for waste heat recovery systems aboard aircraft. This study, thus, contributes to defining a proper design methodology for ORC systems of such applications. The chosen test case is a supercritical ORC system with cyclopentane as the working fluid, which recovers waste heat from the auxiliary power unit of an aircraft. The exhaust gas temperature and mass flow rate of the power unit are known and kept constant in the analysis, and so are the ambient conditions, which define the cold sink of the ORC turbogenerator. Three design strategies targeting minimum mass and maximum net power output of the ORC unit have been assessed. In the first one, the multi-objective optimization is performed by prescribing a priori the geometry and frontal area of the heat exchangers. Thus, only the cycle parameters are optimized. The second method tackles, instead, the simultaneous optimization of the geometric parameters of the condenser and the cycle parameters. It was found that the integrated design allows for system mass reduction by 10 - 12% for a given ORC power output, highlighting the importance of performing the simultaneous optimization of the thermodynamic process and the heat exchanger geometry. Finally, the third method addresses the same optimal design problem by leveraging a reduced-order model of the condenser to predict the optimal design space of this component. The generated Pareto front obtained with this method is very similar to that found by optimizing simultaneously the complete condenser geometry and the cycle parameters. The mean deviation is about 2%. With just one heat exchanger surrogate model, the Pareto front was generated in one fourth of the computational time. This is due to the lower number of optimization variables and the faster objective function evaluation.

Thermodynamic Optimization of Three-Fluid Cross-Flow Heat Exchanger Using GA and PSO Heuristics

In order to improve the scientificity and rationality of cold chain system design, the plate heat exchanger of a branch of the nuclear power plant cold chain system of nuclear power plants is taken as the research object. Based on the basic principles of optimization design, appropriate optimization calculation algorithms are developed and plate type heat exchangers are developed. The design program of the heat exchanger forms an optimal design analysis tool. Taking a plate heat exchanger as an example, taking its mass, flow resistance on the hot side and flow resistance on the cold side as the optimization objectives, a new hybrid genetic algorithm was used to carry out a multi-objective optimization case analysis. The calculation results show that there is a competitive relationship between the mass of the plate heat exchanger of the nuclear island cold chain system of the nuclear power plant and the flow resistance on the hot and cold sides, and the optimal solution cannot be obtained at the same time. There is a correlation between the flow resistance on the cold side and the flow resistance on the hot side. If the heat exchanger mass is reduced by 6.39%, the flow resistance of the hot side and cold side will increase by 6.55% and 7.71% respectively; if the flow resistance on the hot side and cold side are reduced by 32.22% and respectively 32.01%, its mass will increase by 2.16%; on the premise of keeping the mass of the heat exchanger basically unchanged, the flow resistance on the hot side of the heat exchanger is reduced by 23.84% and at the same time the flow resistance on the cold side is reduced by 19.77%, and the flow resistance on the cold side is reduced by the largest 23.35% and at the same time reduce the flow resistance on the hot side by 12.54%. Therefore, there is a lot of room for optimization in the design of nuclear island cold chain system of nuclear power plant and there are many optimization schemes. The research results can provide reference for the subsequent optimization of nuclear power plant cold chain system design and improve the scientific and economic efficiency of system design.

Organic Rankine Cycles (ORCs) have been shown to be feasible thermodynamically for electricity generation from organic fluids as working fluids with low temperature sources. Heat exchanger performance is strongly influenced by thermodynamic cycle efficiency. Minimizing heat losses and therefore maintenance costs is critical to attaining robust heat exchanger performance. As such heat exchanger optimization has emerged as a significant branch of thermal engineering design in the 21st century. We consider a plate heat exchanger as an evaporator and R123 as the working fluid based on ORC thermodynamics. Water vapor with entrance temperature of 150 Celsius is deployed as hot fluid. In this study, a multi-objective optimization method founded on genetic algorithms is implemented to obtain optimized geometrical parameters for the heat exchanger configuration which lead to pressure drop minimization and overall heat transfer coefficient maximization. In the optimization simulations, two objective function are conflicting with each other. Both single and two-phase flow scenarios are addressed. Therefore, in the present optimization method, a Pareto solution is used which permits the derivation of a mathematical relation between the two objective functions simultaneously and yields the optimal geometrical parameters for heat exchangers subject to constraints associated with the Pareto optimal set. A detailed sensitivity analysis has been conducted for each geometrical parameter and the effects of each parameter on key design characteristics have been evaluated.

Numerical optimization and experimental study of helically coiled tube heat exchanger based on Entransy degeneration theory

Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

<div class="section abstract"><div class="htmlview paragraph">Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy. The automation of CAD tasks is facilitated through custom Python code, leveraging the CadQuery library to parameterize CAD models and expedite the CAD process. Meshing and Computational Fluid Dynamics (CFD) simulations are seamlessly integrated within a Python environment, utilizing Ansys Fluent. Concurrently, a multi-objective Bayesian optimization is executed using a Gaussian process regression model facilitated by the GPflow library. By significantly reducing the time required for design tasks, this automation tool addresses a critical challenge that has long persisted in the industry. The tool automates the design processes and identifies an optimal design for the Shell and Tube Heat Exchanger. The tool explores the design space for new non-dominant designs. Three new designs are added to the space, with two dominant and one non-dominant design, further improving the pareto front. Similarly, this tool can be applied to multidisciplinary fields to identify the optimal design quickly with less human intervention in the design process.</div></div>

The transformation to a truly sustainable energy system will require taking better advantage of the waste heat. Integrating heat exchangers with the triply periodic minimal surface (TPMS) is a promising and efficient way to build waste heat recovery systems that harness heat emissions from the low pitch thermal systems. This is mainly due to the low hydrodynamic resistance and pressure drop in the TPMS while securing good heat transfer at low-temperature gradient. This study establishes a computational design and analysis of heat and mass transfer inside a heat exchanger based on the TPMS structure and determine thermal effectiveness, heat transfer coefficient, and pressure drop inside the channel. The non-linearity dependence of results to several design variables makes obtaining the optimal design configuration solely using conventional CFD or experimental study nearly impossible. Hence, a multi-objective optimization workflow based on a Genetic Algorithm for laminar flow is employed to reveal the underlying relationships between design variables for the optimal configurations. The results illustrate the local sensitivity of important parameters such as the heat transfer coefficient, Nusselt number, and thermal performance of the heat exchanger against various design variables. It is shown that the pressure drop is directly affected by gas inlet velocity, viscosity, and density, from high to low, respectively. The Pareto frontiers for the optimal thermal performance are extracted, and the correlation between design objectives is determined. This methodology provides a promising framework for heat exchangers’ design analysis, including multi-objective goals and design constraints.