Browse Topic: Thermodynamics
Linear time-invariant (LTI) reduced-order models (ROMs) have been widely used in battery thermal management simulations due to their low hardware requirements, high computational efficiency, and good accuracy. However, the inherent assumption of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid. To address this limitation, a novel ROM is developed by decomposing the entire battery thermal system into two subsystems. All solid components are modeled as a traditional LTI ROM, while the coolant channel is represented using Newton’s cooling law. The two subsystems are then coupled through the exchange of heat transfer rate and temperature at the fluid–solid interface between the coolant and the cold plate. Model fidelity is further enhanced by introducing a spatially distributed heat flux during the generation of the LTI ROM for solid components. Validation is performed against CFD simulations at both module and
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
A computational study based on a conjugate heat transfer (CHT) method in SimericsMP+ was performed to predict the winding temperatures in an X76 emotor. In this study, the thermal load was represented in the simulation through the solution of electromagnetic equations in SimericsMP+, where heat generation was driven by root-mean-square (RMS) current, while liquid cooling was applied at flow rates ranging from 1 LPM to 6 LPM. Simulations were conducted to measure the temperature on three thermocouple locations on each side of the winding crown and weld regions under steady operation. The computational strategy employed a loosely coupled approach. A fluid-only simulation was first carried out to establish stable flow conditions, followed by coupling with solid conduction where the winding acted as the heat source. The predicted temperature distributions were then compared with test data. Results obtained show good agreement, with differences remaining within an acceptable range, thereby
Oil churning and windage power losses in dip-lubricated gearboxes can significantly affect overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are
The demand for lightweight, high-efficiency components in electric vehicles (EVs) highlights the critical need for reliable Al-Cu joints with superior electrical and thermal conductivity. While diffusion bonding has emerged as a promising approach, interfacial impurities and voids often degrade joint quality and conductivity. Conventional manual polishing was initially employed to prepare Cu and Al surfaces; however, this method proved insufficient in consistently removing oxides and contaminants, leading to non-uniform bonding. In addition, the larger surface area of the samples made traditional polishing impractical, further motivating the use of electropolishing. To overcome these limitations, we introduce electropolishing pretreatment to achieve cleaner, void-free interfaces. Electropolishing effectively dissolves surface asperities and contaminants, enabling intimate atomic contact during bonding and minimizing the formation of brittle intermetallic phases. A systematic
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
This paper presents the emissions development of a heavy-duty hydrogen internal-combustion engine (H₂ICE) targeting ultra-low NOx with a design goal of 20 mg/hp-hr. The approach integrates advanced thermal management of the engine and aftertreatment, including engine out NOx management through air-fuel ratio controls and an electric heater to accelerate catalyst light-off and sustain activity at low-load/idle conditions. A diesel-derived aftertreatment system (ATS) is selected to maximize practicality and component commonality, and an integrated controls strategy spanning the engine and ATS is implemented to demonstrate ultra-low NOx capability over EPA certification cycles. The paper concludes with considerations for periodic SCR regeneration to ensure emission compliance.
This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning
This SAE Recommended Practice is applicable to all heat exchangers used in vehicle and industrial cooling systems. This document outlines the tests to determine the heat transfer and pressure drop performance of heat exchangers under specified conditions. This document has been reviewed and revised by adding several clarifying statements to Section 4.
This Aerospace Recommended Practice (ARP) outlines the causes and impacts of moisture and/or condensation in avionics equipment and provides recommendations for corrective and preventative action.
The objective of this paper is to evaluate the thermal performance of the brake discs in the design stage of its life cycle by developing a methodology to replicate dynamometer testing using multi-disciplinary Finite Element Analysis (FEA) methods. A simulation workflow was formulated in which Computational Fluid Dynamics (CFD) was used to create temperature and velocity dependent Heat Transfer Coefficients (HTC) which were in turn used in Computer Aided Engineering (CAE) to do a thermo-mechanical analysis. With this workflow various designs of the brake discs were analyzed. A sensitivity study was done to determine critical design features that affected its thermal performance. A final design was fixed that met both the weight and thermal performance targets. This design was evaluated in dynamometer testing, and 93% correlation was achieved. Thus, the developed simulation workflow ensured that a first-time right brake disc can be finalized in the design stage, which will meet the
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