Grants and Contracts Details
Turbines are widely used in the energy and transportation sectors, accounting for 15.7 quadrillion BTUs of annual energy consumption. According the U.S. Energy Information Administration, this figure represents 16.1% of the total energy consumed in the U.S., which is 43% larger than all renewable energy within the current energy portfolio . As a result, there is significant interest in realizing even minute improvement in the efficiency of turbines, including platforms for power generation as well as jet engines for transportation. OEM’s continue to invest significant resources to improve performance with new metallic, ceramic and composite materials. These novel materials, such as advanced nickel-based superalloys and ceramic matrix composites, are capable of operating at increasingly higher temperatures, which allows for more efficient turbine operation. However, while there have been tremendous advances in the Materials Science of turbine materials, the Manufacturing Science necessary to efficiently process them has been lacking. Proposed New Technology, Target Performance and Potential Impact We propose to leverage transformative advances in reinforcement learning, by using computationally-efficient physics-based process modeling to enable continuous acquisition of digital manufacturing expertise, in order to enable at least 10% reduction in the total life cycle embodied energy of advanced turbine components. Our AI-Enabled Manufacturing approach will enable more energy-efficient manufacturing and more effective use of difficult-to-process turbine materials. Furthermore, the approach will improve functional performance characteristics such as fatigue life and creep resistance through tailored mechanical treatments on existing manufacturing equipment. By offering more efficient and reliable processing strategies for industry, we expect more efficient turbine designs to become technically feasible and cost-effective to manufacture. While current raw material shortages and insufficient digital manufacturing tools are forcing turbine manufacturers to empirically adopt conservative (i.e. low productivity) and inefficient (i.e., high energy consumption) processing strategies to limit manufacturing scrap, our work will help identify more aggressive, yet reliable, strategies for processing scarce high temperature alloys.
|Effective start/end date||5/15/20 → 5/14/22|
- Department of Energy: $497,685.00
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