Browse Topic: Engine cooling systems
SAE JA6097 (“Using a System Reliability Model to Optimize Maintenance”) shows how to determine which maintenance to perform on a system when that system requires corrective maintenance to achieve the lowest long-term operating cost. While this document may focus on applications to Jet Engines and Aircraft, this methodology could be applied to nearly any type of system. However, it would be most effective for systems that are tightly integrated, where a failure in any part of the system causes the entire system to go off-line, and the process of accessing a failed component can require additional maintenance on other unrelated components.
Three levels of fan structural analysis are included in this practice: a. Initial structural integrity. b. In-vehicle testing. c. Durability (laboratory) test methods. The initial structural integrity section describes analytical and test methods used to predict potential resonance and, therefore, possible fatigue accumulation. The in-vehicle (or machine) section enumerates the general procedure used to conduct a fan strain gage test. Various considerations that may affect the outcome of strain gage data have been described for the user of this procedure to adapt/discard depending on the particular application. The durability test methods section describes the detailed test procedures for a laboratory environment that may be used depending on type of fan, equipment availability, and end objective. The second and third levels build upon information derived from the previous level. Engineering judgment is required as to the applicability of each level to a different vehicle environment
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of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid.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
This SAE Recommended Practice was developed primarily for passenger car and truck applications but may be used in marine, industrial, and similar applications. It addresses nonmetallic caps and both metallic and nonmetallic filler necks.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost
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.
This SAE Recommended Practice was developed primarily for passenger car and truck applications but may be used in marine, industrial, and similar applications.
In the evolving landscape of energy efficiency and sustainability, understanding machine behavior in real-world operating conditions is essential. This solution introduces a data-driven Energy Management Dashboard designed to analyze and report critical machine parameters by leveraging LFI (Leverage Fleet Intelligence) and LFI Data (Local Field Intelligence Data). The tool serves as a robust solution for engineering and operations teams to gain actionable insights into machine performance and exposure. By tracking key parameters—such as engine fan speed, coolant temperature, and machine speed—across a fleet of machines (with support for over 1100 unique signals), the solution enables real-time monitoring and historical analysis. It helps identify when parameters go outside their specified limits and assesses the resulting impact on overall machine performance. The core functionality includes: Monitoring machine operating conditions under real field environments. Correlating parameter
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