Investigation of "Smart Parts" with Embedded Sensors for Energy System Applications


''Smart Parts'' with embedded sensors, (a) sensing<br/>capabilities showcase, (b) case study 1, ''smart''<br/>tube, (c) case study 2, ''smart'' pre-mixer.
''Smart Parts'' with embedded sensors, (a) sensing
capabilities showcase, (b) case study 1, ''smart''
tube, (c) case study 2, ''smart'' pre-mixer.
University of Texas at El Paso
Website:  University of Texas at El Paso
Award Number:  FE0012321
Project Duration:  10/01/2013 – 09/30/2017
Total Award Value:  $1,150,820
DOE Share:  $913,287
Performer Share:  $237,532
Technology Area:  Plant Optimization Technologies
Key Technology:  Sensors & Controls
Location:  El Paso, Texas

Project Description

This research project aims to optimize advanced 3-D manufacturing processes for embedded sensors in energy system components, characterizing the performance and properties of these smart parts, and assessing the feasibility of applying these parts in harsh energy system environments. Specific project objectives are to (1) fabricate energy system related components with embedded sensors, (2) evaluate the mechanical properties and sensing functionalities of the smart parts with embedded piezoceramic sensors, and (3) assess in-situ sensing capability of energy system parts. This research effort will not only contribute to designing and fabricating parts, but also to determining the smart part's durability, repeatability, and stability by testing it in realistic energy environments.

Project Benefits

This project will aid in designing and fabricating energy system "smart parts" and determining their durability, repeatability, and stability through testing in realistic energy environments. This research will aid in the development of advanced technologies to reduce the cost and increase the efficiency of power-generation facilities with carbon capture capabilities.

Contact Information

Federal Project Manager 
Maria Reidpath:
Technology Manager 
Briggs White:
Principal Investigator 
Yirong Lin:

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