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Browse AllThis specification covers a corrosion-resistant steel in the form of investment castings homogenized and solution and precipitation heat treated to 180 ksi (1241 MPa) tensile strength.
The HVAC (Heating, Ventilation, and Air conditioning) system is designed to fulfil the thermal comfort requirement inside a vehicle cabin. Human thermal comfort primarily depends upon an occupant’s physiological and environmental condition. Vehicle AC performance is evaluated by mapping air velocity and local air temperature at various places inside the cabin. There is a need to have simulation methodology for cabin heating applications for cold climate to assess ventilation system effectiveness considering thermal comfort. Thermal comfort modelling involves human manikin modeling, cabin thermal model considering material details and environmental conditions using transient CAE simulation. Present study employed with LBM (Lattice-Boltzmann Method) based PowerFLOW solver coupled with finite element based PowerTHERM solver to simulate the cabin heat up. Human thermal comfort needs physiological modelling; thus, the in-built Berkeley human comfort library is used in simulation. Human
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full-frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and
As the brain and the core of the electric powertrain, the traction inverter is an essential part of electric vehicles (EVs). It controls the power conversion from DC to AC between the electric motor and the high-voltage battery to enable effective propulsion and regenerative braking. Strong and scalable inverter testing solutions are becoming more essential as EV adoption rises, particularly in developing nations like India. In India, traditional testing techniques that use actual batteries and e-motors present several difficulties, such as significant safety hazards, inadequate infrastructure, expensive battery prices, and a shortage of prototype-grade parts. This paper presents a comprehensive approach for traction inverter validation using the AVL Inverter TS™ system incorporating an advanced Power Hardware-in-the-Loop (PHiL) test system based on e-motor emulation technology. It enables safe, efficient, and reliable testing eradicating the need for actual batteries or mechanical
This paper elucidates the implementation of software-controlled synchronous rectification and dead time configuration for bi-directional controlled DC motors. These motors are extensively utilized in applications such as robotics and automotive systems to prolong their operational lifespan. Synchronous rectification mitigates large current spikes in the H-bridge, reducing conduction losses and improving efficiency [1]. Dead time configuration prevents shoot-through conditions, enhancing motor efficiency and longevity. Experimental results demonstrate significant improvements in motor performance, including reduced thermal stress, decreased power consumption, and increased reliability [2]. The reduction in power consumption helps to minimize thermal stress, thereby enhancing the overall efficiency and longevity of the motor.
Generally, in an electric sports utility vehicle with rear mounted powertrain the mass distribution is greater in the rear compared to front. This higher rear to front weight distribution results in oversteer behavior during high-speed cornering deteriorating vehicle handling & risking passenger safety. To compensate this inherent oversteer nature of such vehicles & produce understeer behavior, the steering rack is placed frontwards of the front wheel center for toe-out behavior due to lateral compliance during cornering. This compensation measure results in lower Ackermann percentage resulting in higher turning circle diameter deteriorating vehicle maneuverability. This paper proposes a design to obtain ideal understeer gradient with minimal turning circle diameter through utilization of split link technology with a McPherson Strut based suspension framework & frontwards placed steering rack. This suspension is utilized in our Mahindra Inglo platform. This paper elaborates on how