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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Passive Acoustic Detection of Wind Turbine In-Flow Conditions for Active Control and Optimization

Wind is a significant source of energy; however, the human capability to produce electrical energy still has many hurdles to overcome. One of these is the unpredictability of the winds in the atmospheric boundary layer (ABL). The ABL is highly turbulent in both stable and unstable conditions (based on the vertical temperature profile) and the resulting fluctuations can have a dramatic impact on wind turbine operation. Any method by which these fluctuations could be observed, estimated, or predicted could provide a benefit to the wind energy industry as a whole. Based on the fundamental coupling of velocity fluctuations to pressure fluctuations in the nearly incompressible flow in the ABL, This work hypothesizes that a ground-based array of infrasonic pressure transducers could be employed to estimate the vertical wind profile over a height relevant for wind turbines. To analyze this hypothesis, experiments and field deployments were conducted. Wind tunnel experiments were performed for a thick turbulent boundary layer over a neutral or heated surface. Surface pressure and velocity probe measurements were acquired simultaneously. Two field deployments yielded surface pressure data from a 49 element array. The second deployment at the Reese Technology Center in Lubbock, TX, also included data from a smaller aperture, 96-element array and a 200-meter tall meteorological tower. Analysis of the data successfully demonstrated the ability to estimate the vertical velocity profile using coherence data from the pressure array. Also, dynamical systems analysis methods were successful in identifying and tracking a gust type event. In addition to the passive acoustic profiling method, this program also investigated a rapid response Doppler SODAR system, the optimization of wind turbine blades for enhanced power with reduced aeroacoustic noise production, and the implementation of a wireless health monitoring system for the wind turbine blades. Each of these other objectives was met successfully. The use of phase unwrapping applied to SODAR data was found to yield reasonable results for per-pulse measurements. A health monitoring system design analysis was able to demonstrate the ability to use a very small number of sensors to monitor blade health based on the blade's overall structural modes. Most notable was the development of a multi-objective optimization methodology that successfully yielded an aerodynamic blade design that produces greater power output with reduced aerodynamic loading noise. This optimization method could be significant for future design work.

17 WIND ENERGY↗
DOE OSTI2011

Implementing Best Practices for Data Quality Assessment of the National Renewable Energy Laboratory's Solar Resource and Meteorological Assessment Project

Effective solar radiation measurements for research and economic analyses require a strict protocol for maintenance, calibration, and documentation to minimize station down-time and data corruption. The National Renewable Energy Laboratory's Concentrating Solar Power: Best Practices Handbook for the Collection and Use of Solar Resource Data (1) includes guidelines for operating a solar measure-ment station. This paper describes a suite of automated and semi-automated routines based on the best practices hand-book as developed for the National Renewable Energy La-boratory Solar Resource and Meteorological Assessment Project. These routines allow efficient inspection and data flagging to alert operators of conditions that require imme-diate attention. Although the handbook is targeted for con-centrating solar power applications, the quality-assessment procedures described are generic and should benefit many solar measurement applications. The routines use data in one-minute measurement resolution, as suggested by the handbook, but they could be modified for other time scales.

14 SOLAR ENERGY↗
DOE OSTI2020

BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO 2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d -1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Marine and Hydrokinetic Energy Metocean Data-use, Sources, and Instrumentation

Marine and Hydrokinetic Energy Metocean Data-use, Sources, and Instrumentation presentation from Water Power Technologies Office Peer Review, FY14-FY16. This project aims to accelerate deployment of marine and hydrokinetic (MHK) technology by establishing: 1) relevant existing and evolving standards and guidelines, 2) meteorological and oceanic (metocean) data use 3) data sources, and 4) instrumentation guidance for siting, design, and operation of MHK devices along the U.S coastline.

13 HYDRO ENERGY↗
DOE OSTI2020

Modeling the Effects of Global Change on Ecosystem Processes in a Tropical Rainforest

Ongoing land-use change and climate change in wet tropical forests can potentially drive shifts in tree species composition, representing a change in individual species within a functional group, tropical evergreen trees. The impacts on the global carbon cycle are potentially large, but unclear. We explored the differential effects of species within this functional group, in comparison with the effects of climate change, using the Century model as a research tool. Simulating effects of individual tree species on biome-level biogeochemical cycles constituted a novel application for Century. Background and Objectives: A unique, long-term, replicated field experiment containing five evergreen tree species in monodominant stands under similar environmental conditions in a Costa Rican wet forest provided data for model evaluation. Our objectives were to gain insights about this forest’s biogeochemical cycles and effects of tree species within this functional group, in comparison with climate change. Materials and Methods: We calibrated Century, using long-term meteorological, soil, and plant data from the field-based experiment. In modeling experiments, we evaluated effects on forest biogeochemistry of eight plant traits that were both observed and modeled. Climate-change simulation experiments represented two climate-change aspects observed in this region. Results: Model calibration revealed that unmodeled soil processes would be required to sustain observed P budgets. In species-traits experiments, three separate plant traits (leaf death rate, leaf C:N, and allocation to fine roots) resulted in modeled biomass C stock changes of >50%, compared with a maximum 21% change in the climate-change experiments. Conclusions: Modeled ecosystem properties and processes in Century were sensitive to changes in plant traits and nutrient limitations to productivity. Realistic model output was attainable for some species, but unusual plant traits thwarted predictions for one species. Including more plant traits and soil processes could increase realism, but less-complex models provide an accessible means for exploring plant-soil-atmosphere interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.

60 APPLIED LIFE SCIENCES↗