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Optimization of a Positive Displacement Type Supercharger Using Response Surface Modeling (RSM) Tafe Motors and Tractors, Limited
- Format:
- Book
- Conference/Event
- Author/Creator:
- Satre, Santosh Dadasaheb, author.
- Conference Name:
- Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2026
- Summary:
- Air suction in a naturally aspirated engine is a crucial influencing parameter to dictate the specific fuel consumption and emissions. For a multi-cylinder engine, a turbocharger can well address this issue. However, due to the lack of availability of continuous exhaust energy pulses, in a single or two-cylinder engine, the usage of turbocharger is not recommended. A supercharger solution comes handy in this regard for a single or two-cylinder engine. In this exercise, we explore the possibility of the usage of a positive displacement type supercharger, to enhance the air flow rate of a single cylinder, naturally aspirated, diesel engine for genset application, operating at 1500 rpm. The supercharger parametric 3D CAD model has been prepared in Creo, with three design parameters id est (a) Generating radius, (b) depth of blower and (c) clearance between lobes and lobe and casing. The optimum roots blower design is expected to fulfil the target boost pressure, power consumption and hydraulic efficiency requirements. The baseline DoE using Sobol algorithm generates 28 designs, which has been simulated using the Ansys CFX software via modeFRONTIER process automation. A sensitivity analysis of the input variables on the response variables establishes that generating radius is the most dominant parameter influencing the pressure, efficiency and power consumption. A detailed Response Surface analysis using 12 different algorithms showed that, Anisotropic Kriging captures the pressure variable accurately, while Gaussian Process captures the efficiency and power consumption with the best accuracy as per R-squared comparison. A virtual optimization conducted using the favorite RSMs using the MOGA algorithm generated an optimum roots blower design which complies all the constraints for pressure, efficiency and power. RSM optimized design is further validated in the CFX software, and the results for response variables are accurate within 6% error margin
- Notes:
- Vendor supplied data
- Publisher Number:
- 2026-26-0429
- Access Restriction:
- Restricted for use by site license
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