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NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS2012

Assessing Confidence in Pliocene Sea Surface Temperatures to Evaluate Predictive Models

In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.33.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history.This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

simulation↗

DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗

NASA NTRS1994

Multicolor eclipse studies of UU Aquarii. 1: Observations and system parameters

A study of the eclipses in UU Aqr from multicolor high-speed photometry is presented. A revised ephemeris for the times of minimum and an upper limit for orbital period variations are obtained. We use measurements of contact phases in the eclipse light curve to derive the binary geometry and to estimate masses and relevant dimensions. We find a mass ratio of q = 0.30 +/- 0.07 and an inclination of i = 78 deg +/- 2 deg. The masses of the component stars are M(sub 1) = 0.67 +/- 0.14 solar mass and M(sub 2) = 0.20 +/- 0.07 solar mass. Our photometric model predicts K(sub 1) = 84 +/- 26 km/s, which is approximately 30% smaller than the velocity amplitude obtained from the emission lines. From the white dwarf fluxes we estimate T(sub wd) approximately = 34,000 K and a distance of d = 270 +/- 50 pc if the inner disk is opaque. UU Aqr has long term brightness variations of approximately = 0.3 m on timescales of approximately 4 yr. The system was in a 'high' state in 1989 and 1990 and in a 'low' state in 1988 and 1992. The high state results from an increase in the brightness of the outer and cooler parts of the disk, mainly due to the appearance of a bright spot at disk rim. Based on the smooth and gradual eclipse shape and on the absence of a prominent hump in the light curve we suggest that UU Aqr is a high mass-transfer nova-like system with a relatively bright and optically thick accretion disk. We find no perceptible eclipse in the H-alpha emission line. The fluxes at mid-eclipse can be fitted by a compostion of a late-type spectrum plus an optically thin hydrogen emission-line spectrum. These evidences suggest that the emission lines are formed in an extended region only partially occulted during eclipse.

Baptista, R.↗

NASA NTRS1999

An Assessment of the Impact of the 1997-98 El Nino on the Asian-Australian Monsoon

Using state-of-the-art satellite-gauge monthly rainfall estimate and optimally interpolated sea surface temperature (SST) data, we have assessed the 1997-98 AA-monsoon anomalies in terms of three basic causal factors: basin-scale SST, regional coupling, and internal variability. Singular Value Decomposition analyses of rainfall and SST are carried out globally over the entire tropics and regionally over the AA-monsoon domain. Contributions to monsoon rainfall predictability by various factors are evaluated from cumulative anomaly correlation with dominant regional SVD modes. Results reveal a dominant, large-scale monsoon-El Nino coupled mode with well-defined centers of action in the near-equatorial monsoon regions during the boreal summer and winter respectively. The observed 1997-98 AA-monsoon anomalies are found to be very complex with approximately 34% of the anomalies of the Asian (boreal) summer monsoon and 74% of the Australia (austral) monsoon attributable to basin-scale SST influence associated with El Nino. Regional coupled processes contribute an additional 19% and 10%, leaving about 47% and 16% due to internal dynamics for the boreal and austral monsoon respectively. For the boreal summer monsoon, it is noted that the highest monsoon predictability is not necessary associated with major El Nino events (e.g. 1997, 1982) but rather in non-El Nino years (e.g. 1980, 1988) when contributions from the regional coupled modes far exceed those from the basin-scale SST. The results suggest that in order to improve monsoon seasonal-to-interannual predictability, there is a need to exploit not only monsoon-El Nino relationship, but also intrinsic monsoon regional coupled processes.

Lau, K.-M.↗

NASA NTRS2002

Investigation of Space Based Solid State Coherent Lidar

This report describes the work performed over the period of October 1, 1997 through March 31, 2001. Under this contract, UAH/CAO participated in defining and designing the SPAce Readiness Coherent Lidar Experiment (SPARCLE) mission, and developed the instrument's optical subsystem. This work was performed in collaborative fashion with NASA/MSFC engineers at both UAH/CAO and NASA/MSFC facilities. Earlier work by the UAH/CAO had produced a preliminary top-level system design for the Shuttle lidar instrument meeting the proposed mission performance requirements and the Space Shuttle Hitchhiker canister volume constraints. The UAH/CAO system design efforts had concentrated on the optical and mechanical designs of the instrument. The instrument electronics were also addressed, and the major electronic components and their interfaces defined. The instrument design concept was mainly based on the state of the transmitter and local oscillator laser development at NASA Langley Research Center and Jet Propulsion Laboratory, and utilized several lidar-related technologies that were either developed or evaluated by the NASA/MSFC and UAH/CAO scientists. UAH/CAO has developed a comprehensive coherent lidar numerical model capable of analyzing the performance of different instrument and mission concepts. This model uses the instrument configuration, atmospheric conditions and current velocity estimation theory to provide prediction of instrument performance during different phases of operation. This model can also optimize the design parameters of the instrument.

Amzajerdian, Farzin↗

NASA NTRS2009

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Reichle, Rolf↗

NASA NTRS1977

Use of sensitivity analysis to predict pilot performance as a function of different displays

A technique for objectively evaluating different displays by sensitivity analysis is described. First, the mathematical model used to analyze static displays is developed. The technique is based on formulating functional relationships between the state variables and the variables observable in the display. The matrix of the partial derivatives of the display variables with respect to the state variables, together with the observer's acuity function, is used to calculate expected errors in the state vector estimation. The technique is expanded by the use of Kalman filtering to process a time series of observation vectors. This provides a tool for analyzing displays of dynamic processes by means of a dynamic display evaluation computer program. Results are reported using this program to simulate an Instrument Landing System approach.

Mout, M. L.↗

DOE OSTI2023

A general predictive methodology for fuel-mixture properties up to supercritical conditions

A predictive thermodynamic model is utilized for the calculation of fuel properties of oxymethylene dimethyl ethers (OME 3–4 ), surrogates for gasoline, diesel and aviation fuel, as well as alcohol blends with gasoline and diesel. The alcohols used for these blends are methanol, ethanol, propanol, butanol and pentanol; their mixing ratio ranges from 10 to 50% by volume. The model is based on the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EoS) and Vapor Liquid Equilibrium (VLE) calculations at constant temperature, density and composition. The model includes the association term, with the assumption of two association sites (2B scheme), to enable the modeling of alcohols. The pure-component parameters are estimated based on the Group Contribution (GC) method of various sources, as well as a parametrization model specifically designed for the case of OME 3–4 . The results of the computational model for the density, vapor pressure and distillation curves at various conditions, including high-pressure, high-temperature (HPHT), are compared to experimental and computational data available in the literature. In the cases where no measurements are available for the surrogates, experimental data for the corresponding target fuel are used, taking into consideration the inherent deviation in properties between real and surrogate fuel. Overall, the results are in good agreement with the data from the literature, with the average deviation not exceeding 12% for temperature (Kelvin) on the distillation curves, 10% for density and 46% for vapor pressure and the general trend being captured successfully. The use of different pure component parameter estimation techniques can further improve the prediction quality in the cases of OME3–4 and the aviation fuel surrogate, especially for the vapor pressure, leading to an average deviation lower than 18%. These results demonstrate the predictive capabilities of the model, which extend to a wide range of fuel types and pressure/temperature conditions. Through this investigation, the present work aims to establish the limits of applicability of this thermodynamic property prediction methodology.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2020

Cycling phosphorus on the Archean Earth: Part II. Phosphorus limitation on primary production in Archean ecosystems

Several lines of evidence point to low rates of net primary production (NPP) in Archean oceans. However, whether Archean NPP was limited by electron donors or nutrients, particularly phosphorus (P), and how these factors might have changed over a billion years of recorded Archean history, remains contentious. One major challenge is to understand quantitatively the biogeochemical cycling of P on the early Earth. In Part I of this series (Hao et al., 2020), we estimated the weathering flux of P to the oceans as a function of temporally increasing continental emergence and elevation through Archean time. In Part II, we conduct thermodynamic and kinetic simulations to understand key processes of P cycling within the Archean ocean, including seafloor weathering, recycling of organic P, the solubility and precipitation of secondary phosphate minerals, and the burial diagenesis of P precipitates. Our calculations suggest low solubilities of apatite minerals in Archean seawater, primarily due to nearly neutral pH and high levels of Ca. This low solubility, in turn, implies a negligible contribution of apatite dissolution to P bioavailability in Archean seawater. We also simulate the solubility limits of common secondary P-bearing minerals, showing that vivianite would have been the least soluble P mineral in ferruginous Archean seawater (0.1–0.3 µM), even at moderate supersaturation states (Ω = 100 or 1000). If vivianite precipitation was kinetically favorable by microbial activities and mineral adsorption, the sinking flux of P as vivianite in Archean seawater could have reached the modern sinking flux, implying that vivianite precipitation was a potentially major sink for P in Archean oceans. During burial diagenesis, however, vivianite in porewater would have become less stable than Ca-phosphates of lower solubility. At elevated temperatures (>100 °C) associated with burial diagenesis and low-grade metamorphism, vivianite is predicted to react irreversibly with calcite to form apatite. Optimistic assumptions about the recycling efficiency of P on the Archean Earth lead us to estimate that by the end of the eon the total flux of P (continental weathering + recycling) could have supported NPP at levels up to 7% of the modern. The total flux of P would have been much lower on the early and middle Archean Earth, whereas fluxes of electron donors could have been higher, suggesting very low productivity and P-limitation of marine ecosystems during much of the eon. Comparing our estimates of NPP as limited by P supply with the estimate by Ward et al. (2019), in which NPP was limited by electron donors and metabolic efficiency, there could have been a transition between P-limited productivity in the early to middle Archean to electron donor-limitation closer to the eon’s end (assuming no oxygenic photosynthesis). Once oxygenic photosynthesis reached ecological significance, probably near the end of the Archean, our estimated flux of P would allow rapid oxidation of atmosphere.

Vivianite↗

DOE OSTI2026

Intrinsic kinetics of water-inhibited ultra-lean methane oxidation over PtPd-Mg/ θ -Al 2 O 3 catalyst

Here, this study develops intrinsic methane oxidation kinetics for ultra-lean methane conditions in the presence of water over a highly active and stable PtPd–Mg/θ-Al 2 O 3 catalyst. Comprehensive laboratory experiments were conducted over a wide range of methane concentrations (150–1200 ppm CH 4 ), water contents (1–5% H 2 O), and industrially relevant space velocities (80,000 ≤ GHSV ≤ 160,000 h -1 ). These systematic experiments informed a two-dimensional, axisymmetric, multiscale reactor model that was used to develop and validate methane oxidation kinetics under practically relevant conditions, including non-isothermal operation and high conversion regimes. Combined experimental and modeling results revealed significant intraparticle diffusion resistance and transport-induced reaction exotherm at elevated temperatures, which limited catalyst utilization despite high intrinsic activity. These transport effects were explicitly incorporated into the reactor model, enabling accurate estimation of intrinsic kinetic parameters without reliance on conventional effectiveness-factor corrections. The resulting kinetic model successfully captured both kinetically controlled and mass-transfer-limited regimes and reliably predicted CH 4 conversion across broad ranges of temperature, methane concentration, and water content

Heat and mass transfer limitations↗

DOE OSTI2024

Importance of Electron Correlation on the Geometry and Electronic Structure of [2Fe–2S] Systems: A Benchmark Study of the [Fe 2 S 2 (SCH 3 ) 4 ] 2–,3–,4– , [Fe 2 S 2 (SCys) 4 ] 2– , [Fe 2 S 2 (S- p -tol) 4 ] 2– , and [Fe 2 S 2 (S- o -xyl) 4 ] 2– Complexes

Iron–sulfur clusters are crucial for biological electron transport and catalysis. Obtaining accurate geometries, energetics, manifolds of their excited electronic states, and reduction energies is important to understand their role in these processes. Here, using a [2Fe–2S] model complex with Fe II and Fe III oxidation states, which leads to different charges, i.e., [Fe 2 S 2 (SMe) 4 ] 2–,3–,4– , we benchmarked a variety of computational methodologies ranging from density functional theory (DFT) to post-Hartree–Fock methods, including complete active space self-consistent field (CASSCF), multireference configuration interaction, the second-order N-electron valence state perturbation theory (NEVPT2), and the linearized integrand approximation of adiabatic connection (AC0) approaches. Additionally, we studied three experimentally well-characterized complexes, [Fe 2 S 2 (SCys) 4 ] 2– , [Fe 2 S 2 (S-o-tol) 4 ] 2– , and [Fe 2 S 2 (S-o-xyl) 4 ] 2– , via DFT methods. We conclude that the dynamic electron correlation is important for accurately predicting the geometry of these complexes. Broken symmetry (BS) DFT correctly predicts experimental geometries of low-spin multiplicity, while CASSCF does not. However, BS-DFT significantly overestimates the difference between the low- and high-spin electronic states for a given oxidation state. At the same time, CASSCF underestimates it but provides relative energies closer to the reference NEVPT2 results. Finally, AC0 provides energetics of NEVPT2 quality with the additional advantage of being able to use large CASSCF sizes. NEVPT2 gives the best estimates of the Fe III /Fe III → Fe II /Fe III (4.27 eV) and Fe II /F III → Fe II /F II (7.72 eV) reduction energies. The results provide insight into the electronic structure of these complexes and assist in the understanding of their physical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Cross-scale covariance for material property prediction

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~10 8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10 2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

36 MATERIALS SCIENCE↗

DOE OSTI2024

High-temperature 205 Tl decay clarifies 205 Pb dating in early Solar System

Radioactive nuclei with lifetimes on the order of millions of years can reveal the formation history of the Sun and active nucleosynthesis occurring at the time and place of its birth. Among such nuclei whose decay signatures are found in the oldest meteorites, 205 Pb is a powerful example, as it is produced exclusively by slow neutron captures (the s process), with most being synthesized in asymptotic giant branch (AGB) stars. However, making accurate abundance predictions for 205 Pb has so far been impossible because the weak decay rates of 205 Pb and 205 Tl are very uncertain at stellar temperatures. To constrain these decay rates, we measured for the first time the bound-state β - decay of fully ionized 205 Tl 81+ , an exotic decay mode that only occurs in highly charged ions. The measured half-life is 4.7 times longer than the previous theoretical estimate and our 10% experimental uncertainty has eliminated the main nuclear-physics limitation. With new, experimentally backed decay rates, we used AGB stellar models to calculate 205 Pb yields. Propagating those yields with basic galactic chemical evolution (GCE) and comparing with the 205 Pb/ 204 Pb ratio from meteorites, we determined the isolation time of solar material inside its parent molecular cloud. We find positive isolation times that are consistent with the other s-process short-lived radioactive nuclei found in the early Solar System. Our results reaffirm the site of the Sun’s birth as a long-lived, giant molecular cloud and support the use of the 205 Pb– 205 Tl decay system as a chronometer in the early Solar System.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗

DOE OSTI2021

Rossi-alpha Analysis of CURIE Experiment [Slides]

Critical assembly measurement and operations are crucial to the development of benchmark data. Knowledge of the prompt neutron lifetime informs on the state of the system’s neutron spectrum. Rossi-alpha analysis can give us time-dependent information on the system. Teflon was selected, via genetic algorithm, as a moderator to target the URR in uranium for CURIE. Rossi-alpha measurements of the assembly's approach to critical using He-3 detectors estimated the prompt neutron decay constant (α) at delayed critical. α values of several subcritical measurements were then used to predict the α value at delayed critical.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Biases in preconstruction estimates of wind plant annual energy production

Estimating the energy yield of a wind plant during the preconstruction phase is a historically difficult task, even with industry improvements in these estimations. We build on prior research comparing the realized energy production of wind plants and their estimated annual energy production P50 values (median energy production), using owner-provided energy production and losses. We produced similar results to prior studies but with a slightly increasing bias of overestimating median energy production (a bias between realized and estimated energy production of −7.4 % to −6.6 %, depending on the scenario, as opposed to −6.7 % to −5.5 % from earlier studies). In addition to assessing annual energy production P50 bias, we compared both the 1-year and the long-term annual energy production P90 and uncertainty energy yield assessment estimates to the observed long-term-corrected energy production. We found that neither the energy yield assessment uncertainty nor the P90 is conservative enough compared to the observed distribution of prediction errors, suggesting significant room for improvement in the energy yield assessment process.

17 WIND ENERGY↗