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Results for “ambient evaporative cooling”

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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21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reducing Sample Loss in Measurement of Heat of Vaporization of Ethanol/Gasoline Blends by Differential Scanning Calorimetry/Thermogravimetric Analysis

Higher gasoline Heat of Vaporization (HOV) can enable higher compression-ratio direct injection spark ignition engines by providing evaporative cooling that effectively increases fuel knock resistance. Methods to directly measure this fuel property in complex gasoline samples are not well developed. The objective of the present study was to further improve a Differential Scanning Calorimetry/Thermogravimetric Analyzer (DSC/TGA) method to measure the total and partial HOV of gasoline. Ten market gasoline samples were chosen to have a wide range of properties to assess the method's capability across the entire volatility range with an emphasis on understanding how well the method captures the initial 10 percent (%) of sample evaporation and how much sample is left unevaporated at the end of the experiment. Modifications to the sample preparation/introduction method as well as to the instrument itself were made to reduce initial sample losses which included measurements of the HOV at 10 degrees C and 5 degrees C (in a cold chamber) as well as under (uncontrolled) ambient conditions. Experimental results from the DSC/TGA were compared to calculated total HOV results based on Detailed Hydrocarbon Analysis (DHA). Results from the two methods agreed very well with the difference being 5% or less in almost every case. In addition, the repeatability of the method was investigated by analyzing samples in triplicate at the three temperatures investigated. One valuable conclusion from the study was that the lower temperatures of 10 degrees C and 5 degrees C enabled more reproducible measurements for both total and partial HOV. This improved precision may be caused by the fact that temperature control in the cold chamber was more reliable than the ambient laboratory temperature control. Additionally, cold chamber experiments, due to ergonomic limitations, did not allow for the use of a lid on the sample pan. The reproducibility of the evaporation rate was found to be highly dependent on the pan/lid fit which can vary significantly such that elimination of the lid improved measurement precision while operation at sub-ambient temperature slowed the evaporation rate. Results detailing sample preparation and instrument modifications as well as a detailed comparison of total and partial HOV results are presented.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS↗
DOE OSTI2025

Dual-season thermal energy storage-integrated heat pump system for load shifting: experiments, modeling and nationwide analysis

Here, this paper proposes and validates a dual-season thermal energy storage-integrated heat pump (TES-HP) system that shifts building electric load in both cooling and heating modes using a single phase-change TES. The design uses the TES as a heat sink for the condenser in summer and a heat source for the evaporator in winter, thereby reducing on-peak electric demand while ensuring compatible with existing air-distribution systems. A 14-kW TES-HP prototype with six operating modes and novel refrigerant charge management strategies was experimentally tested under varied conditions. Subsequently, data-driven polynomial performance curves were developed and validated against experiments, then coupled with a phase change material (PCM) model and a calibrated building model within a rule-based controller. Single-day case studies demonstrated effective on-peak demand reduction while maintaining thermal comfort. During cooling, hourly power was lowered by up to 1.5 kW; in heating, average hourly power decreased from over 3.5 kW to 1.8 kW, compared to an HP-only system, achieving 64.5 % load shifting, and the need for resistance heating was eliminated. Seasonal simulations showed typical on-peak electricity savings of 25–35 % in cooling and 40–65 % in heating, with the largest benefits on the hottest and coldest days. Extended response-surface analysis and nationwide mapping quantified load shifting as functions of ambient and TES temperatures, revealing a seasonal trade-off in TES phase-change temperature. These results demonstrate the TES-HP system as a practical and scalable solution for grid-interactive buildings that reduces on-peak demand and electricity use while maintaining thermal comfort and enhancing grid flexibility and reliability.

Grid-interactive buildings↗
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.

42 ENGINEERING↗