Thermal Sciences
Machine Learning

Manojkumar Lokanathan

Manojkumar Lokanathan

Research Scientist in Thermal Fluids Sciences

C-Crete Technologies (Bay Area)


I am a research scientist at C-Crete Technologies with a doctorate from the University of Texas at Austin (Bahadur Research Group), with 9 years of experience in analytical, numerical and experimental heat transfer and thermal management, microfluidics, emulsion chemistry, surface sciences and machine learning. I have published more than 10 peer-reviewed & award-winning articles in multiple internationally renowned scientific journals and conferences.

My experience is highly interdisciplinary with focuses on thermal and lifetime management of power electronics, nanocomposites, nanofluids, electrowetting, dielectrophoresis, electrocoalescence, droplet generation, machine learning and more.

See all skills & expertise

  • Thermal management
  • Electronics cooling
  • Microfluidics
  • Surface sciences
  • Machine learning
  • PhD in Mechanical Engineering, 2022

    University of Texas at Austin

  • MSc in Mechanical Engineering, 2016

    Purdue University

  • BSc in Mechanical Engineering, 2013

    Purdue University


C-Crete Technologies
Research Scientist
C-Crete Technologies
Apr 2022 – Present California (Bay Area)
  • Leading a $1.5 million ARPA-E project to develop novel insulating systems for energy infrastructure
  • Synthesizing and testing nanofluids for next-generation power transformers
       o In accordance with ASTM and IEC standards
  • Designing high pressure and temperature experimental setups over $150,000
  • Designing and running transformer thermal ageing tests according to IEEE standards
  • Working with vendors and manufacturers on experimental components
  • Scaling up nanofluid production by 1000x (lab to commercialization)
University of Texas at Austin
Entrepreneurial Lead (NSF I-Corps)
University of Texas at Austin
Oct 2021 – Mar 2022 Texas
  • Customer discovery for ultra-fast hydrate formation
University of Texas at Austin
Graduate Research Assistant
University of Texas at Austin
Jan 2017 – Apr 2022 Texas


  • Achieved high degree of wettability alteration of water and oil droplets via surface engineering, surfactants & electrowetting (EW)

  • Computationally modeled droplet actuation under dielectrophoresis (DEP)
       o Model predicted experimental data with high accuracy (> 95%) based on electrohydrodynamic physics

  • Developed a multifunctional EW microfluidic device with high capability in droplet coalescence & generation
       o Attained coalesce efficiency greater than 95%
       o Device generated 100s of micron-sized droplets per second
       o Device effectiveness was mapped with a phase diagram with physics-based interpretability

  • Developed a deep learning (DL) model to predict the effectiveness of microfluidic devices, which could reduce the costs of evaluating potential designs
       o Multi-output DL regression model yielded high prediction accuracy
       o Using Shapley Additive exPlanations, the DL model retained a high degree of physics-based interpretability

      Thermal Management

  • Carried out holistic multifunctional assessments of composite polymeric encapsulants for power electronics (PE) modules
  • Assessed current limitations (mechanical, thermal, electrical) of nanocomposites on PE modules
  • Ran ANSYS thermal parametric simulations of PE module through UT Austin’s supercomputer
  • Performed machine learning analysis of thermal simulation data to study effect of nanocomposite encapsulants
       o Best composite encapsulants reduces maximum junction temperatures by 7.4 C (steady state) and 8.9 C (transient)
  • Performed IR imaging tests on PE modules with liquid-cooled heatsink
Pattern Bioscience
Research & Development Intern
Pattern Bioscience
Oct 2020 – Mar 2021 Texas
  • Researched surface & emulsion chemistry effects on droplet distribution in microchannel cells
  • Improved droplet distribution by understanding curing intensity and thermal effects
  • Built a holistic, data analytics approach in quantifying the effects of surfactants on droplet emulsion stability
Purdue University
Graduate Research Assistant
Purdue University
Jan 2015 – May 2016 Indiana
  • Analytically modelled flow transition criteria for vertical downward two-phase flow
  • Model is crucial to predict loss of coolant accident (LOCA) scenarios in high pressure nuclear power plants
  • Achieved 20% higher accuracy with new model as compared to literature
Magnetation Inc.
Reliability Engineer
Magnetation Inc.
Mar 2014 – Nov 2014 Indiana
  • Carried out calibration of electrical and automation hardware for a mining plant start-up
  • Performed safety checks and mechanical cold commissioning of mining plant


Peer Review

  • 9 conference papers (ASTFE, ITherm, SHTC, InterPACK)
  • 1 Journal paper (ACS Nano)


  • 2020-Present: ASME K8 (Heat Transfer Division) Committee Chair
  • 2019-2020: Diversity, Equity & Inclusion Committee Chair
  • 2019-2020: Graduate Engineering Council President
  • 2018-2019: Graduate Engineering Council Financial Director
  • 2017-2018: Graduate Engineering Council Activities Director
  • 2018-2019: Introduce a Girl to Engineering Day/Explore UT


  • 2018-Present: Heat Transfer
  • 2017: Thermodynamics


  • 2021: ITherm Best Paper Runner-Up Award
  • 2020: Philip C. & Linda L. Lewis Foundation Graduation Fellowship
  • 2019: Professional Development Award
  • 2018: Professional Development Award


  • 14421 Catalina St, San Leandro, CA 94577