Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Harsha, V.

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    Performance Testing of Diesel Engine using Cardanol-Kerosene oil blend
    (EDP Sciences edps@edpsciences.com, 2018) Ravindra, n.; Mangalpady, M.; Harsha, V.
    Awareness of environmental pollution and fossil fuel depletion has necessitated the use of biofuels in engines which have a relatively cleaner emissions. Cardanol is a biofuel, abundantly available in India, which is a by-product of cashew processing industries. In this study performance of raw Cardanol blended with kerosene has been tested in diesel engine. Volumetric blend BK30 (30% kerosene and 70% Cardanol) has been used for the test. The properties like flash point, viscosity and calorific value of the blend have been determined. The test was carried out in four stroke diesel engine connected with an eddy current dynamometer. Performance of the engine has been analysed by finding the brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The results showed that the brake thermal efficiency of the blend is 29.87%, with less CO and smoke emission compared to diesel. The results were also compared with the performance of Cardanol diesel blend and Cardanol camphor oil blend, which were already tested in diesel engines by other researchers. Earlier research work reveals that the blend of 30% camphor oil and 70% Cardanol performs very closer to diesel fuel with a thermal efficiency of 29.1%. Similarly, higher brake thermal efficiency was obtained for 20% Cardanol and 80% diesel blend. © The Authors, published by EDP Sciences, 2018.
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    Adaptive Workload Management for Enhanced Function Performance in Serverless Computing
    (Association for Computing Machinery, Inc, 2025) Birajdar, P.A.; Harsha, V.; Satpathy, A.; Addya, S.K.
    Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First-Serve (FCFS) scheduling algorithm. © 2025 Copyright held by the owner/author(s).