Showing 3 results for Experimental Study
Mr Mohammad Fakhari, Mr Ghanbar Ali Sheikhzadeh,
Volume 10, Issue 1 (3-2020)
Abstract
In this experimental study, heat transfer and pressure drop, ΔP, of a coolant nanofluid, obtained by adding alumina nanoparticles to Ethylene Glycol-water mixture (60:40 by mass), in a automotive radiator have been investigated. For this purpose, an experimental setup has been designed and constructed. The experiments have been performed for base fluid and nanofluid with different volume fractions of 0.003, 0.006, 0.009 and 0.012 and under laminar regime with various coolant flow rates of 9, 11 and 13 lit/min and two air velocities of 3.75 and 2.85 m/s. The thermophysical properties have been calculated using the recently presented temperature dependent models. According to the results, the heat transfer and ΔP increase with increasing the coolant flow and nanoparticles volume fraction. Increasing the air velocity causes enhancement of heat transfer. Although Nusselt number decreases when nanofluid is utilized, it enhances as the nanoparticles volume fraction increases. The performance evaluation using nanofluid in the car radiator shows remarkable enhancement in radiator thermal efficiency. However, the ratio of heat transfer rate to the needed pumping power (Merit parameter) decreases.
Mohammad Reza Azmoodeh, Prof. Ali Keshavarz, Alireza Batooei, Hojjat Saberinejad, Mohammad Payandeh Doost, Hossein Keshtkar,
Volume 10, Issue 3 (9-2020)
Abstract
A multi-objective optimization and thermal analysis is performed by both experimental and numerical approaches on a Stirling engine cooler and heater. The power generated is measured experimentally by an electrical engine coupled with the crank case, and the friction is estimated by the difference between the necessary power used for rotating the engine at a specific pressure and speed, versus the actual power measured experimentally. In the experimental approach, different conditions were considered; for example, the charge pressure varied from 5-9 bars, and the engine speed varied from 286-1146 rpm. The maximum power generated was 461.3 W and was reported at 9 bars of charge pressure and 1146 rpm engine speed. Numerical approach was carried to simulate thermal balance for investigations on the effect of friction, engine speed and efficiency on generated engine power. Average values of Nusselt number and coefficient of friction were suggested from simulation results.
The multi-objective optimization was held using DOE method for maximizing engine efficiency and power, and also minimizing pressure drop. The top and bottom boundary values for our optimization were 5-9 bars of pressure and 286-1146 rpm of engine speed; for both helium and carbon dioxide. To do so, all three significance factors (engine speed, efficiency and friction) were given different weights, thus different combinations of weight value was investigated
Amongst different interesting findings, results showed that if the efficiency weight factor changed from 1 to 3, for helium in a specific condition, the optimum engine speed would increase by approximately 30.6 %
Saeeda Ghulami, Dr Mansour Hakimollahi,
Volume 16, Issue 2 (6-2026)
Abstract
Accurate state-of-charge (SoC) estimation is a critical requirement for reliable battery management systems in electric vehicles. While data-driven and machine learning approaches have demonstrated high estimation accuracy, most existing studies assume ideal operating conditions and neglect the influence of mechanical disturbances. In practical automotive environments, lithium-ion batteries are continuously exposed to mechanical vibration, which may affect electrical signals and estimation reliability.
In this study, the impact of mechanical vibration on SoC estimation accuracy is experimentally investigated using standardized vibration tests conducted in accordance with IEC 62660-2. A cylindrical 18650 lithium-ion cell is subjected to random vibration along three orthogonal axes during charge–discharge cycles. Four ensemble-based machine learning models—Random Forest, Extra Trees, Gradient Boosting, and LightGBM—are developed and evaluated under vibration-free and vibration-exposed conditions.
Quantitative results based on RMSE and MAE metrics demonstrate that mechanical vibration leads to a noticeable degradation in SoC estimation accuracy for all models. However, the degree of sensitivity varies among algorithms. Extra Trees and LightGBM exhibit superior robustness to vibration-induced disturbances compared to Random Forest and Gradient Boosting. The findings highlight the importance of considering mechanical operating conditions when designing data-driven SoC estimation algorithms for real-world applications.
