Komal Mohan Garse

@cms.sinhgad.edu

Assistant Professor, Mechanical Engineering
Sinhgad College of Engineering, Pune

Komal Mohan Garse


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https://researchid.co/komalgarse

EDUCATION

PhD (Pursuing) from University of Technology, Jaipur
ME- Mechanical (Heat Power Engg) Savitribai Phule Pune University
BE (Mechanical Engg) Savitribai Phule Pune University

RESEARCH, TEACHING, or OTHER INTERESTS

Mechanical Engineering, Renewable Energy, Sustainability and the Environment, Energy
9

Scopus Publications

45

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Design, modelling and experimental validation of a two-zone combined-flow evaporative condenser for vapour compression refrigeration systems
    Ranjit R. Mali, Vijay W. Bhatkar, D.P. Kamble, Komal Garse
    Applied Thermal Engineering, 2026
  • The rise of advanced 2D semiconductors: a panoramic view
    Sandhya Shinde, D. N. Hire, Priya Charles, Komal Garse, Vanita Daddi, Anupama Patil
    Journal of the Korean Physical Society, 2026
  • Real-time prediction of state of charge and state of health in lithium-ion battery systems using machine learning techniques
    Komal Garse, Kedar Bairwa, Ranjit Mali, Amar Pandhare, Anindita Roy, Chinmay Bhalerao
    Aip Conference Proceedings, 2026
  • 2D–3D perovskite material-based solar cell device stability: a review
    Komal Garse, Priyanka Sharad Jawale, Shubham Chandgude, Harsh Singh, Reeya Agrawal
    Journal of the Korean Physical Society, 2026
  • Deep learning base faults diagnosis of centrifugal pumps
    Amar P. Pandhare, Vishal Naranje, Vinod Shukla, Gábor Balogh, Atul Kulkarni, Gaurav S. Dave, Dhananjay V. Khankal, Komal Garse, Timotei István Erdei
    Pollack Periodica, 2026
    Centrifugal pumps are critical components in industrial systems, where early fault detection is essential to reduce downtime and maintenance costs. This study proposes a deep learning based framework for intelligent health monitoring of centrifugal pumps using time series vibration signals. Recurrent neural networks, long short term memory networks, and hybrid convolutional neural network long short term memory models are evaluated for operating condition classification and fault detection. The vibration data are preprocessed and normalized to capture temporal characteristics effectively. Experimental results based on accuracy, precision recall metrics, and confusion matrices show that the convolutional neural network long short term memory model achieves superior performance and generalization, making it suitable for fault diagnosis of rotating machinery.
  • Optimization and Analysis of Electric Vehicle Charging Systems using PMLP-PSVM-MLR Approach
    Hans John Dcruz, N. M. G. Kumar, Komal Mohan Garse, Malini. T, K. K. Senthilkumar, Pankaj Kumar Dubey
    2025 International Conference on Intelligent Computing and Knowledge Extraction Icicke 2025, 2025
    Electrifying transport represents a highly promising alternative to reduce CO2 emissions from the transport industry and prevent possibly catastrophic climate change. Electric vehicles have the potential to transform transportation; yet, their extensive adoption presents issues for the efficient operation of electric power networks, especially at the distribution level. An effectively administered Electric Vehicle Charging System is crucial for maintaining network stability. This paper examines the potential impacts of a comprehensive Electric Vehicle Charging System on a distributed network, proposes control measures to alleviate those impacts, and investigates the benefits of such a system from a performance perspective. The methodology consists of three phases: data preparation, feature extraction, and model training. Data preprocessing encompasses transformation, normalisation, and partitioning, whilst feature extraction is executed via the Hough transform, which converts data from a rectangular grid to a polar coordinate system. Models are subsequently trained utilising PMLP, PSVM, and MLR methodologies. The suggested solution surpasses conventional alternatives, PSVM and MLP, improving network efficiency and stability. The results demonstrate that optimised control systems can markedly mitigate the adverse effects of electric vehicle charging on distribution networks. Subsequent study ought to concentrate on enhancing these tactics and broadening their application to more extensive networks.
  • Mechanical-wear behavior and microstructure analysis of Al2214 alloy with B4C and graphite particles hybrid composites
    Revanna Kambaiah, Ramappa Suresh, Madeva Nagaral, Virupaxi Auradi, Chandrashekar Anjinappa, Komal Garse, Amar Pradeep Pandhare, Anteneh Wogasso Wodajo
    Engineering Reports, 2024
    In the present work, hybrid composites made of Al2214 alloy with B4C and graphite were produced by using a liquid metallurgical process. Al2214 alloy was utilized to create hybrid composites that had 1.5–6 wt% of B4C particles and a constant 3 wt% of graphite particles. Microstructural analysis using scanning electron microscope (SEM), energy dispersion spectroscopy (EDS), and X‐ray diffraction (XRD) was done on the produced composites. The density, hardness, ultimate, yield strength, and elongation as a percentage were carried out using ASTM E8 for tensile and E10 standard for hardness test. The wear behavior of Al2214‐B4C and graphite composites was examined as per ASTM G99 standard using a wear testing device under a variety of loads and rotation speeds. Graphite and boron carbide particles were equally dispersed throughout the Al2214 alloy, according to SEM photographs. Graphite and B4C particles were detected in the Al2214 alloy by EDS and XRD analyses. The density of Al alloy composites was decreased by adding dual particles to the matrix. The Al2214 alloy's hardness, ultimate strength, yield strength, and wear resistance were all enhanced by the inclusion of dual particles, which increased these properties by 15.4%, 40.4%, and 46.7%, respectively. The presence of hybrid particles in the Al2214 alloy was revealed by EDS and XRD patterns. The density of Al alloy composites was decreased by adding dual particles to the matrix. Tensile force micrographs provided further evidence of the unique fracture behaviors shown by the Al2214 alloy and its composites. In order to examine the wear mechanisms and different morphologies of worn surfaces, scanning electron micrographs were taken.
  • Exploring the Impact of Al2O3 Additives in Gasoline on HCCI-DI Engine Performance: An Experimental, Neural Network, and Regression Analysis Approach
    Lionus Leo George Mary, Subramanian Manivel, Shalini Garg, Vinoth Babu Nagam, Komal Garse, Ranjit Mali, T. M. Yunus Khan, Rahmath Ulla Baig
    ACS Omega, 2023
    This study delves into the influence of incorporating alumina (Al2O3) nanoparticles with waste cooking oil (WCO) biofuels in a gasoline engine that employs premixed fuel. During the suction phase, gasoline blends with atmospheric air homogeneously at the location of the inlet manifold. The biodiesel, enhanced with Al2O3 nanoparticles and derived from WCO, is subsequently directly infused into the combustion chamber at 23° before the top dead center. The results highlight that when gasoline operates in the homogeneous charge compression ignition with direct injection (HCCI-DI) mode, there is a notable enhancement in thermal efficiency by 4.23% in comparison to standard diesel combustion. Incorporating the Al2O3 nanoparticles with the WCO biodiesel contributes to an extra rise of 6.76% in thermal efficiency. Additionally, HCCI-DI combustion paves the way for a reduction in nitrogen oxides and smoke emissions, whereas biodiesel laced with Al2O3 nanoparticles notably reduces hydrocarbon and carbon monoxide discharges. Predictive tools such as artificial neural networks and regression modeling were employed to forecast engine performance variables.
  • Artificial Intelligence-Based Control Strategies for Unmanned Aerial Vehicles
    S. Prabagar, Ali Khudhair Al-Jiboory, Prabha Shreeraj Nair, Pawan Mandal, Komal Mohan Garse, Natrayan L
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2023, 2023
    Drones, also known as Unmanned Aerial Vehicles, or UAVs, are becoming more and more well-liked as a multifunctional tool that can be used in a variety of industries, including environmental monitoring, search and rescue, agricultural, and surveillance. In this work, we explore the potential for significantly increased UAV efficiency, versatility, and durability with AI-based control techniques. The research technique combines simulation, experimental validation, and mathematical modelling to fully investigate AI-based control systems. We develop a mathematical model of UAV dynamics and use a deep reinforcement learning (DRL) technique based on Proximal Policy Optimisation (PPO) to control the drones. Both theoretical analysis and real-world testing on a DJI Matrice 210 RTK platform validate the method's efficacy. The algorithm's capacity to provide precise and accurate answers is shown by the average location error's steady decline over time. It is very resilient, remaining stable in the face of wind gusts, and remarkably flexible, with quick response times. These patterns are shown graphically. According to the study's findings, AI-based control algorithms might greatly advance UAV technology by increasing its precision, adaptability, and dependability. Future priorities will include advanced machine learning techniques, multi-agent systems, safety measures, ethical frameworks, human-AI collaboration, environmental impact assessments, and urban integration. Through the creation of new capabilities and the resolution of urgent concerns, these activities have significant potential to impact UAV development in the future.

RECENT SCHOLAR PUBLICATIONS

  • Design, modelling and experimental validation of a two-zone combined-flow evaporative condenser for vapour compression refrigeration systems
    RR Mali, VW Bhatkar, DP Kamble, K Garse
    Applied Thermal Engineering, 131456 , 2026
    2026.0
  • Deep learning base faults diagnosis of centrifugal pumps
    AP Pandhare, V Naranje, V Shukla, G Balogh, A Kulkarni, GS Dave, ...
    Pollack Periodica, 606.2026. 01550 , 2026
    2026.0
  • Real-time prediction of state of charge and state of health in lithium-ion battery systems using machine learning techniques
    K Garse, K Bairwa, R Mali, A Pandhare, A Roy, C Bhalerao
    AIP Conference Proceedings 3369 (1), 040037 , 2026
    2026.0
  • The rise of advanced 2D semiconductors: a panoramic view
    S Shinde, DN Hire, P Charles, K Garse, V Daddi, A Patil
    Journal of the Korean Physical Society, 1-28 , 2026
    2026.0
  • 2D–3D perovskite material-based solar cell device stability: a review: K. Garse et al.
    K Garse, PS Jawale, S Chandgude, H Singh, R Agrawal
    Journal of the Korean Physical Society 88 (2), 141-163 , 2026
    2026.0
  • Modelling Techniques and Power Management Strategies for Dual Battery Systems in Electric Vehicles-A Review.
    KM Garse, KN Bairwa, A Roy
    Journal of Mines, Metals & Fuels 73 (8) , 2025
    2025.0
  • Recent Advances In Combined Flow Evaporative Condensers: A Comprehensive Review
    MRR Mali, VW Bhatkar, DP Kamble, MKM Garse
    International Journal of Environmental Sciences 11 (12s), 1331-1338 , 2025
    2025.0
  • Optimization and Analysis of Electric Vehicle Charging Systems using PMLP-PSVM-MLR Approach
    HJ Dcruz, NMG Kumar, KM Garse, KK Senthilkumar, PK Dubey
    2025 International Conference on Intelligent Computing and Knowledge … , 2025
    2025.0
    Citations: 4
  • Mechanical‐wear behavior and microstructure analysis of Al2214 alloy with B 4 C and graphite particles hybrid composites
    R Kambaiah, R Suresh, M Nagaral, V Auradi, C Anjinappa, K Garse, ...
    Engineering Reports 6 (10), e12876 , 2024
    2024.0
    Citations: 7
  • Techniques for SOC and SOH Estimation in Li-Ion Batteries: A Detailed Review
    KNB Komal Mohan Garse
    Journals of Mechatronics Machine Design and Manufacturing 6 (2), 7-16 , 2024
    2024.0
  • Design and Analysis of Solar-Operated Clothes Dryer
    SB Ingle, RV Sonawane, VR Nilawar, SB Wakade, KM Garse
    Journal of Modern Thermodynamics in Mechanical System 6 (2), 1-9 , 2024
    2024.0
  • Hybrid random forest regression and artificial neural networks for modelling and monitoring the state of health of li-ion battery
    A Roy
    J. Electr. Syst. 20 (2), 2231-2243 , 2024
    2024.0
    Citations: 14
  • Exploring the Impact of Al 2 O 3 Additives in Gasoline on HCCI-DI Engine Performance: An Experimental, Neural Network, and Regression Analysis Approach
    LLG Mary, S Manivel, S Garg, VB Nagam, K Garse, R Mali, ...
    ACS omega 8 (50), 47701-47713 , 2023
    2023.0
    Citations: 8
  • Artificial intelligence-based control strategies for unmanned aerial vehicles
    S Prabagar, AK Al-Jiboory, PS Nair, P Mandal, KM Garse
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023
    2023.0
    Citations: 11
  • A REVIEW ON MPPT TECHNIQUES FOR WIND ENERGY
    MKM Garse, DV Khankal, AP Pandhare
    YMER 21 (09) , 2022
    2022.0
  • SIX SIGMA IN HEALTHCARE- A REVIEW OF LITERATURE
    DVSB Mr. Komal M. Garse
    International Journal of Research and Analytical Reviews (IJRAR) 8 (3), 137-141 , 2021
    2021.0
  • STUDY OF SIX SIGMA METHODOLOGY TO REDUCE CESAREAN SECTION RATE IN INDIAN HOSPITAL
    DVSB Mr. Komal M. Garse.
    Journal of Emerging Technologies and Innovative Research 8 (3) , 2021
    2021.0
    Citations: 1
  • Manufacturing Strategy the Key to Business Success – A Review
    SB Komal Mohan Garse
    International Journal of Information Technology and Management [IJITM] 14 (1 … , 2019
    2019.0
  • A Review on Heat Transfer Enhancement Using Nano Fluids
    MK Garse
    International Journal for Research & Development in Technology , 2018
    2018.0
  • Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation
    KM Garse, KN Bairwa

MOST CITED SCHOLAR PUBLICATIONS

  • Hybrid random forest regression and artificial neural networks for modelling and monitoring the state of health of li-ion battery
    A Roy
    J. Electr. Syst. 20 (2), 2231-2243 , 2024
    2024.0
    Citations: 14
  • Artificial intelligence-based control strategies for unmanned aerial vehicles
    S Prabagar, AK Al-Jiboory, PS Nair, P Mandal, KM Garse
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023
    2023.0
    Citations: 11
  • Exploring the Impact of Al 2 O 3 Additives in Gasoline on HCCI-DI Engine Performance: An Experimental, Neural Network, and Regression Analysis Approach
    LLG Mary, S Manivel, S Garg, VB Nagam, K Garse, R Mali, ...
    ACS omega 8 (50), 47701-47713 , 2023
    2023.0
    Citations: 8
  • Mechanical‐wear behavior and microstructure analysis of Al2214 alloy with B 4 C and graphite particles hybrid composites
    R Kambaiah, R Suresh, M Nagaral, V Auradi, C Anjinappa, K Garse, ...
    Engineering Reports 6 (10), e12876 , 2024
    2024.0
    Citations: 7
  • Optimization and Analysis of Electric Vehicle Charging Systems using PMLP-PSVM-MLR Approach
    HJ Dcruz, NMG Kumar, KM Garse, KK Senthilkumar, PK Dubey
    2025 International Conference on Intelligent Computing and Knowledge … , 2025
    2025.0
    Citations: 4
  • STUDY OF SIX SIGMA METHODOLOGY TO REDUCE CESAREAN SECTION RATE IN INDIAN HOSPITAL
    DVSB Mr. Komal M. Garse.
    Journal of Emerging Technologies and Innovative Research 8 (3) , 2021
    2021.0
    Citations: 1
  • Design, modelling and experimental validation of a two-zone combined-flow evaporative condenser for vapour compression refrigeration systems
    RR Mali, VW Bhatkar, DP Kamble, K Garse
    Applied Thermal Engineering, 131456 , 2026
    2026.0
  • Deep learning base faults diagnosis of centrifugal pumps
    AP Pandhare, V Naranje, V Shukla, G Balogh, A Kulkarni, GS Dave, ...
    Pollack Periodica, 606.2026. 01550 , 2026
    2026.0
  • Real-time prediction of state of charge and state of health in lithium-ion battery systems using machine learning techniques
    K Garse, K Bairwa, R Mali, A Pandhare, A Roy, C Bhalerao
    AIP Conference Proceedings 3369 (1), 040037 , 2026
    2026.0
  • The rise of advanced 2D semiconductors: a panoramic view
    S Shinde, DN Hire, P Charles, K Garse, V Daddi, A Patil
    Journal of the Korean Physical Society, 1-28 , 2026
    2026.0
  • 2D–3D perovskite material-based solar cell device stability: a review: K. Garse et al.
    K Garse, PS Jawale, S Chandgude, H Singh, R Agrawal
    Journal of the Korean Physical Society 88 (2), 141-163 , 2026
    2026.0
  • Modelling Techniques and Power Management Strategies for Dual Battery Systems in Electric Vehicles-A Review.
    KM Garse, KN Bairwa, A Roy
    Journal of Mines, Metals & Fuels 73 (8) , 2025
    2025.0
  • Recent Advances In Combined Flow Evaporative Condensers: A Comprehensive Review
    MRR Mali, VW Bhatkar, DP Kamble, MKM Garse
    International Journal of Environmental Sciences 11 (12s), 1331-1338 , 2025
    2025.0
  • Techniques for SOC and SOH Estimation in Li-Ion Batteries: A Detailed Review
    KNB Komal Mohan Garse
    Journals of Mechatronics Machine Design and Manufacturing 6 (2), 7-16 , 2024
    2024.0
  • Design and Analysis of Solar-Operated Clothes Dryer
    SB Ingle, RV Sonawane, VR Nilawar, SB Wakade, KM Garse
    Journal of Modern Thermodynamics in Mechanical System 6 (2), 1-9 , 2024
    2024.0
  • A REVIEW ON MPPT TECHNIQUES FOR WIND ENERGY
    MKM Garse, DV Khankal, AP Pandhare
    YMER 21 (09) , 2022
    2022.0
  • SIX SIGMA IN HEALTHCARE- A REVIEW OF LITERATURE
    DVSB Mr. Komal M. Garse
    International Journal of Research and Analytical Reviews (IJRAR) 8 (3), 137-141 , 2021
    2021.0
  • Manufacturing Strategy the Key to Business Success – A Review
    SB Komal Mohan Garse
    International Journal of Information Technology and Management [IJITM] 14 (1 … , 2019
    2019.0
  • A Review on Heat Transfer Enhancement Using Nano Fluids
    MK Garse
    International Journal for Research & Development in Technology , 2018
    2018.0
  • Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation
    KM Garse, KN Bairwa

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

1. UK Design Patent on “Hygiene Detection Device for Hospital”, Design No: 6305796, Status- Granted on 13/09/2023.
2. UK Design Patent on “Artificial Intelligence Based Nerve Activation Device for Healthcare treatment”, Design No: 6304729, Status- Granted on 22/08/2023.
3. Indian Design Patent on “Charging Station for Electric Vehicle, Application No- 391702-001, Status- filed on 02/08/2023.
4. Indian Patent on “Design and Usability of Interfaces for Automated Vehicles”, Application No- 202341037112A, Status- Published on 16/06/2023.

COPYRIGHTS

1. Copyright on “Revolutionizing Energy Storage: Unveiling the Potential of Plasma Technology”, Dairy No: 24050/2023-CO/L, Status- Filed on 07/09/2023, Granted on 03/11/2023.
2. Copyright on “Beyond Traditional Cooling Systems: Thermo Acoustics Refrigeration”, Dairy No: 23839/2023-CO/L, Status- Filed on 05/09/2023, Granted on 29/02/2024.

Books:
1. Ranjit Mali, Komal Garse, Dr. Amar Pandhare, Sandesh Rasal, Dattatray Bankar, “Principles of Renewable Energy Systems” Vinsa Publishing, ISBN 978-81-971527-4-0.
2. B. L. Singhal, Komal M. Garse, Ranjit R. Mali, “Renewable Energy Technologies” TechKnowledge Publications, Pune, ISBN 978-93-5563-311-8.
3. Dr. A. Sarvanan, Mr. Komal M. Garse, Dr. C. Udhaya Shankar, Dr. S. Arulkumar, “Handbook of Electric Vehicle Technology”, Scientific International Publishing House (SIPH), ISBN 978-93-5757-154-8.
4. Dr. Manjushree R, Mr. Vijayakumar K, Mr. Komal Mohan Garse, Dr. Niharika Gokhale, Amit Kumar S

SOCIAL, ECONOMIC, or ACADEMIC BENEFITS

• Best Book Author Award, at Wisdom Educational Iconic Award 2024 by Wisdom Educare Academy, Tamil Nadu.
• Research Excellence Award 2022, by Institute of Scholars (InSc), Bengaluru.
• Editorial Board Member for Vinsa Publishing, Chennai.
• Review Board Member for International Journal of Emerging Research in Engineering, Science and Management.
• Review Member for Journal of Emerging Technologies & Innovative Research (ID: 116880).
• Editorial Board Member of Scienxt Journal of Experimental Applied Mechanics.