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Shashank Tamaskar

from Columbus, IN
Age ~39
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Related to:
Shravan Tamaskar
Connected to:
Aparna U Tamaskar, 65Terry L Tamaska, 69Gregory C Tamason, 73Diane C Tamasovich, 71Lee Tamassia, 52Claudia Tamay, 34Maria Tamay, 40Nancy F Tamay, 34Victor Tamay, 57Abraham A Tamayo, 32

Shashank Tamaskar Phones & Addresses

  • Columbus, IN
  • Davis, CA
  • West Lafayette, IN
  • W Lafayette, IN

Resumes

Resumes

Shashank Tamaskar Photo 1

R And D Research Scientist And Project Manager

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Location:
Columbus, IN
Industry:
Research
Work:
Purdue University since May 2010
Graduate Research Assistant
Cummins Inc. May 2012 - Aug 2012
Controls and Diagnostics - Advanced R&T
Education:
Purdue University 2011 - 2014
Doctor of Philosophy (PhD), Aerospace, Aeronautical and Astronautical Engineering Purdue University 2009 - 2011
Master's degree, Aerospace, Aeronautical and Astronautical Engineering Indian Institute of Technology, Bombay 2005 - 2009
Bachelor of Technology (BTech), Aerospace, Aeronautical and Astronautical Engineering
Skills:
Matlab
Simulations
Simulink
Aerodynamics
Ansys
Engineering
Latex
Numerical Analysis
Experimentation
Finite Element Analysis
Aircraft Design
Fluid Mechanics
Systems Engineering
Labview
Aerospace
Heat Transfer
Thermodynamics
Interests:
Indian Institute of Technology
Bombay
Languages:
Hindi
Marathi
English
Shashank Tamaskar Photo 2

Shashank Tamaskar

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Publications

Us Patents

Deep Reinforcement Learning For Air Handling Control

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US Patent:
20200063676, Feb 27, 2020
Filed:
Aug 21, 2018
Appl. No.:
16/106311
Inventors:
- Columbus IN, US
Vikas Narang - Columbus IN, US
Govindarajan Kothandaraman - Columbus IN, US
Shashank Tamaskar - West Lafayette IN, US
International Classification:
F02D 41/00
G06N 3/08
G06F 15/18
F02D 41/18
F02D 41/28
Abstract:
An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.

Deep Reinforcement Learning For Air Handling And Fuel System Referencing

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US Patent:
20200063681, Feb 27, 2020
Filed:
Aug 21, 2018
Appl. No.:
16/106300
Inventors:
- Columbus IN, US
Vikas Narang - Columbus IN, US
Govindarajan Kothandaraman - Columbus IN, US
Shashank Tamaskar - West Lafayette IN, US
International Classification:
F02D 41/14
F02D 41/00
G06N 3/08
G06F 15/18
F02D 41/18
F02D 41/28
Abstract:
An engine system includes an air handling and fuel system whose states are managed by a reference managing unit. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The reference managing unit includes a controller which controls the air handling and fuel system of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines air handling references and fuel system references by using the policy function after receiving the current state as an input, and outputs the air handling references and fuel system references to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling and fuel system of the engine in response to receiving the air handling references and the fuel system references.
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Shashank Tamaskar from Columbus, IN, age ~39 Get Report