Physics-based fluid mechanics models are proposed to predict load-independent (spin) power losses of gear pairs due to oil churning and windage. The oil churning power loss model is intended to simulate spin losses in dip-lubricated conditions while the windage power loss model is intended to simulate spin power losses under jet-lubrication conditions. The total spin power loss, in either case, is defined as the sum of (i) power losses associated with the interactions of individual gears with the environment surrounding the gears, and (ii) power losses due to pumping of the oil or air-oil mixture at the gear mesh. Power losses in the first group are modeled through individual formulations for drag forces induced by the fluid, which is the lubricant in the case of oil churning power losses and air or air-oil mixture in the case of windage power losses, on a rotating gear body along its periphery and faces, as well as for eddies formed in the cavities between adjacent teeth. Gear mesh pocketing/pumping losses are predicted analytically as the power loss due to squeezing of the fluid as a consequence of volume contraction of the mesh space between mating gears as they rotate. The pocketing losses are modeled through means of an incompressible fluid flow approach in the case of oil churning power losses. When the gear pairs rotate under windage conditions, a compressible fluid flow methodology is considered for predicting the pocketing losses. The power loss models are applied to a family of unity-ratio spur gear pairs to quantify the individual contributions of each power loss component to the total spin power loss. The influence of operating conditions, gear geometry parameters and lubricant properties on spin power loss are also quantified.The oil churning and windage power loss models are validated through comparisons to extensive experiments performed on spur gear pairs under dip- and jet-lubricated conditions, over wide ranges of gear parameters and operating conditions. The direct comparisons between model predictions and measurements demonstrate that the model is indeed capable of predicting the measured spin power loss values as well as the measured parameter sensitivities reasonably well, reinforcing the possibility of utilizing the proposed model as a computationally effective design tool for predicting power losses in geared systems. The spin power loss model is further generalized to handle the several complex and varying gear configurations and operating conditions present in an actual manual transmission in order to come up with a transmission spin power loss model, which when coupled with a transmission mechanical power loss model and existing bearing power loss prediction methodologies, can predict the total power loss in a transmission. This transmission power loss model formed by these three power loss components is validated through comparison to actual power loss measurements from a six-speed example manual transmission, indicating that the transmission power loss model can indeed be used for design and product improvement activities.
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