An investigation into the influence of viscosity on gear churning losses by considering the effective immersion depth

We present an experimental investigation into the influence of oil viscosity on gear churning losses in splash-lubricated transmission systems. The inertia rundown method was used to perform tests on a single gear within a cylindrical housing with several oils of different viscosities at several immersion depths. A complex and nonmonotonic relationship between churning torque and viscosity was observed that was highly influenced by the rotational speed, with higher viscosity oils resulting in lower churning torque at higher speeds in some cases. This was attributed to a reduction in effective immersion depth due to oil being centrifugally distributed around the casing by the rotating gear, an effect that was observed to be more pronounced with higher viscosity oils. An effective immersion depth parameter, dependent on the rotational speed of the gear and the lubricant viscosity, was defined to account for this phenomenon. Gear churning losses could be better predicted using an existing empirical model when this parameter was used instead of the nominal immersion depth as is usually done

Prediction of electric vehicle transmission efficiency using a new thermally coupled lubrication model

We present a new method to predict the power losses in electric vehicle (EV) transmission systems using a thermally coupled gearbox efficiency model. Friction losses in gear teeth contacts are predicted using an iterative procedure to account for the thermal coupling between the tooth temperature, oil viscosity, film thickness, friction, and oil rheology during a gear mesh cycle. Crucially, the prediction of the evolution of the coefficient of friction (COF) along the path of contact incorporates measured lubricant rheological parameters as well as measured boundary friction. This allows the model to differentiate between nominally similar lubricants in terms of their impact on EV transmission efficiency. Bearing and gear churning losses are predicted using existing empirical relationships. The effects of EV motor cooling and heat transfers in the heat exchanger on oil temperature are considered. Finally, heat transfer to the surroundings is accounted for so that the evolution of gearbox temperature over any given drive cycle can be predicted. The general approach presented here is applicable to any automotive gearbox while incorporating features specific to EVs. The model predictions are compared to real road measurements made on a popular current EV, and good agreement is shown over a range of road conditions. It should be noted that at high input speeds, the current model somewhat overpredicts the gearbox losses due to limitations in existing empirical bearing and churning loss models. Analyses of transmission losses breakdown at constant input power show that at low speeds/high torques, it is the losses in the gear meshes and high-load bearings that are most significant whereas at high speeds/low torques the losses in high-speed input shaft bearings, as well as gear churning losses, become more important. It is shown that the gearbox losses can account for 15-25% of the overall power losses in an EV depending on road conditions; a much higher proportion than in an internal combustion engine (ICE) vehicle, thus demonstrating that reducing transmission losses offers an important avenue for improving EV efficiency. Finally, the influence of oil properties on EV transmission losses is demonstrated by applying the model to predict losses over the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) drive cycle. The presented model can help to optimize both gearbox design and lubricant properties to minimize EV transmission losses and hence improve EV range

Climate-Smart Groundnuts for Achieving High Productivity and Improved Quality: Current Status, Challenges, and Opportunities

About 90% of total groundnut is cultivated in the semi-arid tropic (SAT) regions of the world as a major oilseed and food crop and provides essential nutrients required by human diet. Climate change is the main threat to yield and quality of the produce in the SAT regions, and effects are already being seen in some temperate areas also. Rising CO2 levels, erratic rainfall, humidity, short episodes of high temperature and salinity hamper the physiology, disease resistance, fertility and yield as well as seed nutrient levels of groundnut. To meet growing demands of the increasing population against the threats of climate change, it is necessary to develop climate-smart varieties with enhanced and stable genetic improvements. Identifying key traits affected by climate change in groundnut will be important for developing an appropriate strategy for developing new varieties. Fast-changing scenarios of product ecologies as a consequence of climate change need faster development and replacement of improved varieties in the farmers’ fields to sustain yield and quality. Use of modern genomics technology is likely to help in improved understanding and efficient breeding for climate-smart traits such as tolerance to drought and heat, and biotic stresses such as foliar diseases, stem rot, peanut bud necrosis disease, and preharvest aflatoxin contamination. The novel promising technologies such as genomic selection and genome editing need to be tested for their potential utility in developing climate-smart groundnut varieties. System modeling may further improve the understanding and characterization of the problems of target ecologies for devising strategies to overcome the problem. The combination of conventional breeding techniques with genomics and system modeling approaches will lead to a new era of system biology assisted breeding for sustainable agricultural production to feed the ever-growing population

Climate-Smart groundnuts for achieving high productivity and improved quality: Current status, challenges, and opportunities

About 90% of total groundnut is cultivated in the semi-arid tropic (SAT) regions of the world as a major oilseed and food crop and provides essential nutrients required by human diet. Climate change is the main threat to yield and quality of the produce in the SAT regions, and effects are already being seen in some temperate areas also. Rising CO2 levels, erratic rainfall, humidity, short episodes of high temperature and salinity hamper the physiology, disease resistance, fertility and yield as well as seed nutrient levels of groundnut. To meet growing demands of the increasing population against the threats of climate change, it is necessary to develop climate-smart varieties with enhanced and stable genetic improvements. Identifying key traits affected by climate change in groundnut will be important for developing an appropriate strategy for developing new varieties. Fast-changing scenarios of product ecologies as a consequence of climate change need faster development and replacement of improved varieties in the farmers’ fields to sustain yield and quality. Use of modern genomics technology is likely to help in improved understanding and efficient breeding for climate-smart traits such as tolerance to drought and heat, and biotic stresses such as foliar diseases, stem rot, peanut bud necrosis disease, and preharvest aflatoxin contamination. The novel promising technologies such as genomic selection and genome editing need to be tested for their potential utility in developing climate-smart groundnut varieties. System modeling may further improve the understanding and characterization of the problems of target ecologies for devising strategies to overcome the problem. The combination of conventional breeding techniques with genomics and system modeling approaches will lead to a new era of system biology assisted breeding for sustainable agricultural production to feed the ever-growing population