Browse Topic: Engine cooling systems
This SAE Standard establishes a test method and a definition for disclosing the performance of suction/blower fans when applied to self-propelled sweepers that solely use a pneumatic conveyance means for the collection and transfer of “sweepings” into a collection hopper.
The deployment of high-power DC charging infrastructure for electric vehicles introduces new challenges in managing noise, particularly in public environments where acoustic comfort and regulatory compliance are essential. Noise emissions from both charging stations and vehicles during charging are a concern for operators of charging parks regarding customer experience and noise immission regulations. AVL employed a structured three-step approach to develop a non-expert tool for assessing the noise radiation of charging stations and vehicles during the charging phase. In a first step, AVL characterized the noise emissions with sound power measurements. Secondly, the measurement results were transferred to the virtual domain. To achieve this, the vehicles and charging station were characterized in the simulation with multiple monopole sources supported by transfer function measurements. This simulation model was validated against the sound power measurement results. After successful correlation of the simulation model, AVL implemented a user-friendly noise mapping tool for predictive planning of charging parks moving from a 3D FEM simulation approach to a simplified noise radiation model according to ISO 9613. Measurements were conducted on a representative high-power DC charging station and two electric vehicles. The vehicle cooling fan was identified as the dominant noise source on the vehicle side, while internal cooling fans of the charging station were the primary contributors to the noise radiated by the charger. Noise emissions were found to increase with higher charging currents, indicating a strong dependency on thermal load. The project established a reusable workflow for acoustic source identification enabling the creation of a scalable database. The noise mapping tool allows easy prediction of noise radiation from multiple charging stations and vehicles during the planning phase of charging parks and supports the design of countermeasures to meet regulatory requirements.
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 pack levels, under constant and varying flow rates. The results demonstrate that the proposed ROM achieves high accuracy while requiring several orders of magnitude less computational time than the corresponding CFD models.
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 breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
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 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 anomalies with performance degradation. Identifying exposure and usage trends based on location and operating conditions. Recommendations on potential impact of parameter value variations on the Engine Torque, malfunction in specific features of the tractor (machine). This solution architecture integrates seamlessly with existing data pipelines and leverages LFI data for contextual insights. The development process involved collaboration with the Ruse squad to ensure relevance to on-field challenges. The expected outcomes include improved visibility into machine usage, early detection of potential issues, and enhanced data-driven decision-making for field operations and energy management. By transforming raw machine data into clear visual insights, this solution empowers teams to take proactive measures in improving efficiency and reliability.
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