Browse Topic: Heat exchangers
In recent years, especially in high-performance spark-ignition engines, the thermal stress of pistons has gradually increased due to the implementation of various technologies, aimed at meeting emission reduction and specific power increase requirements. If the heat is not properly dissipated, cracking and plastic deformation of the material as well as formation of hot spots triggering pre-ignition in the combustion chamber mixture can occur. This last aspect is even more true considering innovative fuels such as hydrogen. To overcome these problems, one or more jets of oil are directed towards the piston under-crown region, impacting at high speed. This technique ensures immediate cooling and allows the engine performance to be increased without compromising the useful life. In order to optimize the oil jet effectiveness, 3D-CFD can be proficiently adopted. In this regard, the aim of this work is to define a robust numerical methodology able to simulate oil jet impingement and piston thermal field. In particular, a 3D-CFD Volume-of-Fluid (VoF) simulation is used to numerically assess the oil jet impact and provide a map of heat transfer coefficients, which, in turn, is adopted in a 3D-CHT model to estimate the piston thermal field. The proposed methodology is validated against experimental data on a high-performance engine piston. In particular, a pair of oil jets is investigated and the resulting heat transfer coefficient map is exploited to obtain the thermal field of the piston, which is finally compared to the available experimental temperature measurements. The results show that the predicted temperatures agree with the experimental data within an error lower than 2.5%.
The design of thermal components (such as automotive heat exchangers) requires balancing multiple competing objectives—thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design workflows rely on manual Computer Aided Design (CAD) modeling and iterative simulations, which are both labor-intensive and time-consuming. Recent advances in Large Language Models (LLMs) present untapped potential for automating parametric CAD generation. However, current LLM-based approaches primarily handle simple, isolated geometric primitives rather than complex multi-component assemblies. This work introduces a progressive framework that leverages fine-tuned LLMs (Qwen2.5-3B-SFT) integrated with the CadQuery CAD kernel to automatically generate parametric geometries from natural language descriptions. As a foundational study, this work focuses on Step 1 of the framework: generating and optimizing isolated geometric primitives (cylinders, pipes, etc.) that form the building blocks of complex assemblies. The generated models are automatically exported to standard CAD formats and seamlessly integrated within a multi-objective Bayesian optimization pipeline using Gaussian Process regression. By decoupling natural language-driven CAD code generation from traditional manual scripting, this work demonstrates how LLMs can accelerate design space exploration while eliminating the need for engineers to write geometry-specific Python scripts. Case studies on parametric pipe optimization demonstrate the framework's efficiency gains and establish a foundation for future steps: handling constraints, multi-component assemblies, and full thermal component designs. This work contributes to next-generation Artificial Intelligence (AI) assisted engineering design by demonstrating LLM-powered automation as a practical pathway toward fully automated design-to-optimization workflows.
Battery thermal management is crucial for ensuring the safety, efficiency, and longevity of lithium-ion battery packs, particularly in electric vehicles (EVs). The primary purpose of a lithium-ion battery in an electric vehicle is to store and provide electrical energy for vehicle propulsion while maintaining safety under different operating conditions. This work proposes a thermal correlation between 1D CFD simulation and experimental test data under passive environmental heat exchange conditions without active coolant flow of a battery pack comprising four modules. An environmental exchange test was conducted using a 50% state of charge (SOC) battery pack, which is stabilized at 25°C to assess passive heat dissipation, thermal soak behavior, temperature distribution, and potential thermal runaway risks. The simulation predictions correlate well within a 1.5°C range compared to test results using ambient temperature and flow inputs, which confirms the reliability of the modeling approach. The simulation work was carried out using the GT-SUITE software. This study improves battery thermal management strategies by enhancing predictive accuracy and optimizing simulation frameworks for real-world applications. It minimizes overheating risks in practical scenarios, such as prolonged exposure to high ambient temperatures.
The present work demonstrates a Fluid-Structure Interaction (FSI) based methodology that couples a Finite Volume Method (FVM) and Finite Element Method (FEM) based tools to estimate air guide deformation, thereby predicting accurate aerothermal performance. The method starts with a digital assembly step where the assembly shape and the induced stress due to assembly is predicted. A full vehicle Aerodynamic simulation is performed to extract the surface pressure on the air guide which is then used to estimate the extent of deformation of the air guides. Based on the extent a subsequent Aerodynamic simulation may be carried out to predict thermal efficiency. Comparison against pressure data and deflection data extracted from the wind tunnel experiments of vehicles has shown reasonable match demonstrating the accuracy and usefulness of the method.
In automotive systems, efficient thermal management is essential for refining vehicle performance, enhancing passenger comfort, and reducing MAC Power Consumption. The performance of an air conditioning system is linked to the performance of its condenser, which in turn depends on critical parameters such as the opening area, radiator fan ability and shroud design sealing. The opening area decides the airflow rate through the condenser, directly affecting the heat exchange efficiency. A larger opening area typically allows for greater airflow, enhancing the condenser's ability to dissipate heat. The shroud, which guides the airflow through the condenser, plays a vital role in minimizing warm air recirculation. An optimally designed shroud can significantly improve the condenser's thermal performance by directing the airflow more effectively. Higher fan capacity can increase the airflow through the condenser, improving heat transfer rates. However, it is essential to balance fan capacity with energy consumption to achieve optimal performance. This study investigates the impact of varying these parameters on vehicle-level A/C performance and MAC Power Consumption. By systematically altering the condenser opening area, changing the shroud configuration, and adjusting the radiator fan capacity, we aim to find best conditions that enhance A/C performance and effect MAC Power Consumption. Experimental data were collected through a series of controlled tests, the results were analysed to decide the correlation between these variables A/C performance metrics such as average grill temperatures, refrigerant pressure and MAC Power Consumption. The findings provide valuable insights for automotive engineers and designers, highlighting the importance of these factors in achieving efficient as well as effective A/C systems in passenger vehicles.
Thermal management is critical for modern vehicles, particularly for Zero Emission Vehicles (ZEVs), where maintaining optimal temperature ranges directly influences thermal system efficiency and vehicle range. Accurate prediction of underhood airflow behavior is essential for effective thermal management and also to estimate overall energy consumption by cooling system, with air-side dynamics playing a pivotal role in heat transfer over the heat exchangers of cooling package. Simulation tools like GT-Suite are indispensable for this purpose, enabling engineers to evaluate complex thermal interactions without the cost and time constraints of extensive physical testing. While 3D Computational Fluid Dynamics (CFD) models offer detailed insights into flow characteristics, they are computationally expensive and time consuming. In contrast, 1D models provide faster simulation times, making them ideal for system-level analysis and iterative design processes. However, 1D models inherently lack the ability to capture detailed flow phenomena, which can compromise the accuracy of thermal predictions. To mitigate this, calibration using 3D CFD data or experimental measurements becomes critical, ensuring that air-side behavior is represented as accurately as possible. One of the key challenges in this calibration process arises at low fan speeds, where matching flow rates becomes difficult due to the unavailability of windmilling data. Along with calibration of normal operating conditions, this paper also presents a methodology for tuning fan maps under such constraints, focusing on strategies to enhance model fidelity and novel methodology to calculate fan mechanical power. We explore simulation-based techniques, leveraging steady-state operating conditions to refine fan characteristics. The study further discusses sensitivity analysis, validation strategies, and potential inaccuracies introduced by missing windmilling effects and method to accurately fill in the missing fan map data. The proposed methodology ensures improved predictive accuracy of underhood airflow behavior, enhancing thermal system design for automotive applications. This method improves the reliability of underhood airflow predictions, ultimately contributing to more accurate thermal management system predictions out of digital tools.
Zero emission vehicles are essential for achieving sustainable and clean transportation. Hybrid vehicles such as Fuel Cell Electric Vehicles (FCEVs) use multiple energy sources like batteries and fuel cell stacks to offer extended driving range without emitting greenhouse gases. Optimal performance and extended life of the important components like the high voltage battery and fuel-cell stack go a long way in achieving cost benefits as well as environmental safety. For this, energy management in FCEVs, particularly thermal management, is crucial for maintaining the temperature of these components within their specified range. The fuel cell stack generates a significant amount of waste heat, which needs to be dissipated to maintain optimal performance and prevent degradation, whereas the battery system needs to be operated within an optimal temperature range for its better performance and longevity. Overheating of batteries can lead to reduced efficiency and potential safety hazards, while low temperatures can decrease battery performance and range. The multiple temperature control loops in the thermal system design of the current FCEVs require significant energy for continuous heating and cooling. This is due to the fact that each of them exchanges energy directly with an external source or sink without redistributing energy among themselves. This can lead to energy losses during the heat exchange process. Our goal is to optimize thermal energy usage while maintaining the same performance and efficiency of both battery electric system and the fuel cell stack in a vehicle. In this paper, an analysis of thermal energy utilization of a single system is compared to the exchange of thermal energy across multiple systems, considering various heating and cooling scenarios. We compare our proposed strategy (with redistribution) with the existing strategy (without redistribution) quantitatively with respect to controller effort/ energy spent in achieving thermal target.
Researchers used an innovative approach to the geometry and design of pipes that flow hot and cold fluids through heat exchangers. University of Wisconsin-Madison, Madison, WI By combining topology optimization and additive manufacturing, a team of University of Wisconsin-Madison engineers created a twisty high-temperature heat exchanger that outperformed a traditional straight channel design in heat transfer, power density and effectiveness. And they used an innovative technique to 3D print - and test - the metal proof of concept.
By combining topology optimization and additive manufacturing, a team of University of Wisconsin-Madison engineers created a twisty high-temperature heat exchanger that outperformed a traditional straight channel design in heat transfer, power density and effectiveness.
This paper presents an advanced control system design for an engine cooling system in an internal combustion engine (ICE) vehicle. Building upon our previous work, we have derived models for crucial temperatures within the engine, including combustion wall temperature, coolant-out temperature, block temperature, as well as temperatures in external components such as heat exchangers and radiator. To accurately predict these temperatures in a rapid manner, we have utilized a lumped parameter concept with a mean-value approach. This approach allows for precise temperature estimation while maintaining computational efficiency. Given the complexity of the cooling system, we have proposed a linear time-varying (LTV) model predictive control (MPC) system to regulate the temperatures. This control system linearizes the model at each time step and applies linear MPC over the control and prediction horizons. By doing so, we effectively control the highly nonlinear and time-delayed system. Simulation results demonstrate the superiority and effectiveness of the proposed advanced engine cooling system. The control system can successfully regulate the temperatures within their desired range, showcasing its capability to optimize engine performance and ensure efficient cooling.
The thermoelectric generator system is regarded as an advanced technology for recovering waste heat from automotive exhaust. To address the issue of uneven temperature distribution within the heat exchanger that limits the output performance of the system, this study designs a novel thermoelectric generation system integrated with turbulence enhancers. This configuration aims to enhance convective heat transfer at the rear end of the heat exchanger and improve overall temperature uniformity. A multiphysics coupled model is established to evaluate the impact of the turbulence enhancers on the system's temperature distribution and electrical output, comparing its performance with that of traditional systems. The findings indicate that the integration of turbulence enhancers significantly increases the heat transfer rate and temperature uniformity at the rear end of the heat exchanger. However, it also leads to an increase in exhaust back pressure, which negatively affects system performance. At lower exhaust flow velocities, the gains in output power attributable to the turbulence enhancers considerably outweigh the increases in exhaust back pressure. Specifically, under conditions of 550 K and 20 m/s, the output power, net output power, and temperature uniformity coefficient increase by 39.2%, 33.6%, and 8.5%, respectively. As exhaust temperature rises, the gains from the turbulence enhancers become even more pronounced. Nevertheless, under high flow conditions, the rise in exhaust back pressure can potentially degrade the system's net output performance. Therefore, it is recommended that exhaust flow be appropriately diverted in practical applications to ensure optimal performance. This research provides essential theoretical guidance for the design and performance optimization of automotive thermoelectric generation systems.
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