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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Counter cross-flow evaporator geometries for supercritical organic Rankine cycles

With recent advancements in advanced manufacturing techniques, it is now possible to fabricate complex geometries that take advantage of well known principles of heat transfer. Therefore, unconventional configurations to enhance effectiveness beyond conventional designs can now be considered for practical application. Thermal performance and overall cost of a new design of heat exchangers in counter cross-flow configurations are studied using a simplified but accurate computational method. The new heat exchanger design was introduced and studied previously for a cross-flow configuration by Sabau et al. (2016, 2018). This new design concept uses multi-scale configurations with successive plenums for the working fluid. At the smallest scale the tubes are sized to be equal to the hydraulic entrance length of the inside fluid, in accord with constructal design. With this being said, results indicate that compared to the earlier cross-flow configuration, the counter cross-flow arrangement improves the thermal performance of the heat exchanger by as much as 17% and lowers the total cost by as much as 14%.

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DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

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