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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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112 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence O - Raw Data

Sequence O: Sin AOA, Parked (P) This sequence was designed to quantify the blade 3-D unsteady aerodynamic response in the absence of rotational influences by varying blade pitch angle. This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged from 18.9 m/s to 39.3 m/s, and the yaw angle was 0° throughout the sequence. The rotor was parked with the instrumented blade fixed at 0° azimuth, and the rotor lock was installed (see Appendix A). Blade and probe pressure measurements were collected. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to -99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. Sinusoidal angle-of-attack histories were designed to emulate 2-D S809 dynamic data obtained at the OSU wind tunnel (Reuss Ramsey et al. 1995) as well as 1P yawed flow angle-of-attack variations the turbine could be expected to encounter in routine operation. The reduced frequency (K), mean angle of attack (αm), and oscillation amplitude (αω) were computed for each of the five primary span locations. The file lengths varied according to the time required to complete 40 oscillations at the specified frequency. The file name convention used the sequence designation O, followed by two digits for the span location (30, 47, 80, or 95), followed by two digits representing the condition specified in the test matrix, followed by the repetition digit. Due to time constraints, the test conditions corresponding to the 63% span location were eliminated. Test points with a static pitch angle of 3° were collected throughout the sequence to ascertain instrumentation fidelity.

17 WIND ENERGY↗
DOE OSTI2022

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence N - Raw Data

Sequence N: Sin AOA, Rotating (P) This sequence was designed to quantify the blade 3-D unsteady aerodynamic response in the presence of rotational influences by varying blade pitch angle. Test sequence N used an upwind, rigid turbine with a 0° cone angle. The wind speed was 15 m/s, and the yaw angle was 0° throughout the sequence. The rotor rotated at 72 RPM. Blade and probe pressure measurements were collected. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. Sinusoidal angle-of-attack histories were designed to emulate 2-D S809 dynamic data obtained at the Ohio State University (OSU) wind tunnel (Reuss Ramsey et al. 1995) and the 1P yawed flow angle-of-attack variations the turbine could be expected to encounter in routine operation. The reduced frequency (K), mean angle of attack (αm), and oscillation amplitude (αω) were computed for each of the five primary span locations. The file lengths varied according to the time required to complete 40 oscillations at the specified frequency. The file name convention used the sequence designation N, followed by two digits for the span location (30, 47, 63, 80, or 95), followed by two digits representing the condition specified in the test matrix, followed by the repetition digit. Due to time constraints, most of the test conditions corresponding to the 63% span location were eliminated. Test points with a static pitch angle of 3° were collected throughout the sequence to ascertain instrumentation fidelity.

17 WIND ENERGY↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗