Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “standard normal variate”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Unusual locations of Earth's bow shock on September 24 - 25, 1987: Mach number effects

International Sun Earth Explorer 1 (ISEE 1) and Interplanetary Monitoring Platform 8 (IMP 8) data are used to identify 19 crossings of Earth's bow shock during a 30-hour period following 0000 UT on September 24, 1987. Apparent standoff distances for the shock are calculated for each crossing using two methods and the spacecraft location; one method assumes the average shock shape, while the other assumes a ram pressure-dependent shock shape. The shock's apparent standoff distance, normally approximately 14 R(sub E), is shown to increase from near 10 R(sub E) initially to near 19 R(sub E) during an 8-hour period, followed by an excursion to near 35 R(sub E) (where two IMP 8 shock crossings occur) and an eventual return to values smaller than 19 R(sub E). The Alfven M(sub A) and fast magnetosonic M(sub ms). Mach numbers remain above 2 and the number density above 4/cu cm for almost the entire period. Ram pressure effects produce the initial near-Earth shock location, whereas expansions and contractions of the bow shock due to low Mach number effects account, qualitatively and semiquantitatively, for the timing and existence of almost all the remaining ISEE crossings and both IMP 8 crossings. Significant quantitative differences exist between the apparent standoff distances for the shock crossings and those predicted using the observed plasma parameters and the standard model based on Spreiter et al.'s (1966) gasdynamic equation. These differences can be explained in terms of either a different dependence of the standoff distance on Mach number at low M(sub A) and M(sub ms), or variations in shock shape with M(sub A) and M(sub ms) (becoming increasingly "puffed up" with decreasing M(sub A) and M(sub ms), as expected theoretically), or by a combination of both effects.

Cairns, Iver H.↗
NASA NTRS1997

Atmospheric Angular Momentum Fluctuations During 1979-1988 Simulated by Global Circulation Models

Changes in major global dynamical phenomena in the Earth's atmosphere are manifested in the time series of atmospheric angular momentum (AAM), as determined directly from meteorological observations and indirectly from geodetic observations of small fluctuations in the rotation of the solid Earth which are proportional to length of day. AAM fluctuations are intimately linked with energetic processes throughout the whole atmosphere and also with the stresses at the Earth's surface produced largely by turbulent momentum transport in the oceanic and continental boundary layers and by the action of normal pressure forces on orographic features. A stringent test of any numerical global circulation model (GCM) is therefore provided by a quantitative assessment of its ability to represent AAM fluctuations on all relevant timescales, ranging from months to several years. From monthly data provided by the Atmospheric Model Intercomparison Project (AMIP) of the World Climate Research Programme, we have investigated seasonal and interannual fluctuations and the decadal mean in the axial component of AAM in 23 AMIP GCMs over the period 1979-1988. The decadal means are generally well simulated, with the model median value (1.58 x 10(exp 26) kg sq m/s) being only 3.5% larger than the observed mean and with 10 of the models being within 5% of the observed. The seasonal cycle is well reproduced, with the median amplitude of the models seasonal standard deviations being only 2.4% larger than observed. Half the seasonal amplitudes lie within 15% of the observed, and the median correlation found between the observed and model seasonal cycles is 0.95. The dominant seasonal error is an underestimation of AAM during northern hemisphere winter associated with errors in the position of subtropical jets. Less robust are the modeled interannual variations, although the median correlation of 0.61 between model simulations and observed AAM is statistically significant. The two El Nino-Southem Oscillation events that occurred during the AMIP decade 1979-1988 have the expected positive AAM anomalies, although the AAM signature of the 1982-1983 event tends to be underestimated and that of the 1986-1987 event overestimated.

Hide, R.↗
NASA NTRS1997

Atmospheric Angular Momentum Fluctuations During 1979-1988 Simulated by Global Circulation Models

Changes in major global dynamical phenomena in the Earth's atmosphere are manifested in the time series of atmospheric angular momentum (AAM), as determined directly from meteorological observations and indirectly from geodetic observations of small fluctuations in the rotation of the solid Earth which are proportional to length of day. AAM fluctuations are intimately linked with energetic processes throughout the whole atmosphere and also with the stresses at the Earth's surface produced largely by turbulent momentum transport in the oceanic and continental boundary layers and by the action of normal pressure forces on orographic features. A stringent test of any numerical global circulation model (GCM) is therefore provided by a quantitative assessment of its ability to represent AAM fluctuations on all relevant timescales, ranging from months to several years. From monthly data provided by the Atmospheric Model Intercomparison Project (AMIP) of the World Climate Research Programme, we have investigated seasonal and interannual fluctuations and the decadal mean in the axial component of AAM in 23 AMIP GCMs over the period 1979-1 988. The decadal means are generally well simulated, with the model median value (1.58 x 10(exp 26) kg sq m/s) being only 3.5% larger than the observed mean and with 10 of the models being within 5% of the observed. The seasonal cycle is well reproduced, with the median amplitude of the models' seasonal standard deviations being only 2.4% larger than observed. Half the seasonal amplitudes lie within 15% of the observed, and the median correlation found between the observed and model seasonal cycles is 0.95. The dominant seasonal error is an under- estimation of AAM during northern hemisphere winter associated with errors in the position of subtropical jets. Less robust are the modeled interannual variations, although the median correlation of 0.61 between model simulations and observed AAM is statistically significant. The two El Nino-Southern Oscillation events that occurred during the AMIP decade 1979-1988 have the expected positive AAM anomalies, although the AAM signature of the 1982-1983 event tends to be underestimated and that of the 1986-1987 event overestimated.

Hide, R.↗
NASA NTRS1997

Determination of Stress Intensity Factor Distributions for "Interface" Cracks in Incompressible, Dissimilar Materials

The present study was undertaken in order to develop test methods and procedures for measuring the variation of the stress intensity factor through the thickness in bimaterial specimens containing cracks within and parallel to the bond line using the frozen stress photoelastic method. Since stress freezing materials are incompressible above critical temperature, and since thick plates are to be employed which tend to produce a state of plane strain near the crack tip, the interface near tip fracture equations reduce to the classic form for homogeneous materials. Moreover, zero thickness interfaces do not exist when materials are bonded together. It was decided early on that it would be important to insure a uniform straight and accurate crack tip region through the thickness of the body to reduce scatter in the SIF distribution through the thickness. It was also observed that rubberlike materials which were desired to be modeled exhibited significant tip blunting prior to crack extension and that some blunting of the tip would provide a more realistic model. It should be noted that, in normal stress freezing photoelastic work, it is considered good practice to avoid utilizing data near bond lines in photoelastic models due to the bond line stresses which inevitably develop when two parts are bonded together. Thus, the present study involves certain exploratory aspects in deviating from standard practice in stress freezing work. With the above ideas in mind, several different test methods were investigated and are described in the following sections and appendices. The geometry selected for the program was a thick, edge cracked specimen containing a bond line.

Smith, C. W.↗
NASA NTRS1997

Near-infrared spectroscopy for monitoring of tissue oxygenation of exercising skeletal muscle in a chronic compartment syndrome model

Variations in the levels of muscle hemoglobin and of myoglobin oxygen saturation can be detected non-invasively with near-infrared spectroscopy. This technique could be applied to the diagnosis of chronic compartment syndrome, in which invasive testing has shown increased intramuscular pressure associated with ischemia and pain during exercise. We simulated chronic compartment syndrome in ten healthy subjects (seven men and three women) by applying external compression, through a wide inflatable cuff, to increase the intramuscular pressure in the anterior compartment of the leg. The tissue oxygenation of the tibialis anterior muscle was measured with near-infrared spectroscopy during gradual inflation of the cuff to a pressure of forty millimeters of mercury (5.33 kilopascals) during fourteen minutes of cyclic isokinetic dorsiflexion and plantar flexion of the ankle. The subjects exercised with and without external compression. The data on tissue oxygenation for each subject then were normalized to a scale of 100 per cent (the baseline value, or the value at rest) to 0 per cent (the physiological minimum, or the level of oxygenation achieved by exercise to exhaustion during arterial occlusion of the lower extremity). With external compression, tissue oxygenation declined at a rate of 1.4 +/- 0.3 per cent per minute (mean and standard error) during exercise. After an initial decrease at the onset, tissue oxygenation did not decline during exercise without compression. The recovery of tissue oxygenation after exercise was twice as slow with compression (2.5 +/- 0.6 minutes) than it was without the use of compression (1.3 +/- 0.2 minutes).

NASA Discipline Musculoskeletal↗
NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Flux-Level Transit Injection Tests of TPS for Data Release 25

Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.

DR25↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
NASA NTRS2015

Estimation of Crop Gross Primary Production (GPP): Do Scaled (MODIS) Vegetation Indices Improve Performance? - 2

Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.

Vegetation Index↗
NASA NTRSDate not supplied

Additional Trials & Tribulations in Synthesizing a Sulfide Standard

Well-characterized sulfide reference materials that can serve as matrix-matched calibrants for in-situ trace element analyses via laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remain elusive. Here, we describe the creation an in-house sulfide standard at NASA JSC that will be used to measure siderophile and chalcophile trace elements in low pressure experimental products, specifically pentlandite ([FeNi]9S8) and pyrrhotite ([FeNi]1-xS), as well as natural sulfides in terrestrial and meteoritic mineral assemblages. Recent methods in creating a homogenous sulfide standard include pressed pellets, synthesized chips, or fused glass [e.g. 1-3]. Other studies, however, have had success in forming homogenous FeS standards via synthesization methods [e.g. 4-6]. We present a variation of the method described in [4], in which we create a pyrrhotite standard doped with a variety of trace elements (Zn, As, Se, Mo, Ru, Rh, Pd, Sn, Sb, Te, W, Os, Ir, Pt, Au) at ~10-40 ppm and Cu at ~200 ppm. Sulfide compositions were constructed using Fe and Ni metal and elemental sulfur powders. Trace elements were added to elemental sulfur from Atomic Absorption Spectroscopy (AAS) elemental standards as either nitrate or chloride solutions to prevent oxidation of the metal powders. The dried sulfur mixture was mixed with Fe and Ni powders and mechanically mixed before two aliquots were placed in separate SiO2 tubes. Each tube was held under vacuum for ~30 minutes, sealed under vacuum, and then heated at 800C for 48 hours. Like [4], the synthesis products were composed of pourous sulfide crystals. Major element analyses of both experimental aliquots, collected using an Electron Probe Microanalyser (EPMA), yield indistinguishable major element compositions (uncertainties in 2SE & 2RSE[%]), with an average of 57.90  0.09 (0.16 %), 4.95  0.03 (0.55 %), and 39.70  0.14 (0.34 %), for Fe, Ni, and S, respectively. Trace element data were measured using a Photon Machines 193nm laser ablation system coupled to a Thermo-Scientific Element-XR ICP-MS. Spot sizes were limited to 50 μm due to the porous nature of the sulfide target material. Trace element abundances, normalized to Fe as an internal standard, were also found to be homogenous between the two aliquots, with weighted mean 2RSE (%) values of <3.0 for all trace elements. Synthesized products were re-powdered and absolute concentrations measured via solution ICP-MS. Although the sulfide appears to be homogenous, sintering experiments will be performed to more closely match the standard density to natural sulfides and minimize differences in ablation behavior. Improved density also allows for higher sensitivity (i.e. more compact target material) and larger spot sizes or traverses, as void space is eliminated. Additional major and trace element analyses on the products of the sintering experiments will be undertaken.

Jacob B Setera↗
DOE OSTI2021

Performance Comparison of Circular and Spherical Error Probable Estimators

This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

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

Sequence Q: Dynamic Inflow (P) This sequence was designed to characterize the dynamic inflow variation using the five-hole probes that extend upwind of the leading edge of the blade. This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speeds ranged from 5 m/s to 15 m/s, and data were collected at a 0° yaw angle. 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 pretensioned to 40,000 N. The blade pitch angle was changed from the initial tip pitch angle to the final tip pitch angle at the maximum rate of 67°/s. This angle was maintained for the specified delay time duration. Then the blade was pitched back to the initial pitch angle and held for the specified hold time. This was repeated 20 times. The campaign at 15 m/s was aborted early due to excessive low-speed shaft torque loads. The file length varied depending on the time required to obtain 20 pitch cycles for the specified range of angles. The name convention was the standard format, except for the short campaigns collected to ascertain the functionality of the instrumentation. These points were collected at a 3° pitch angle and use the characters REF in the four digits that normally represent yaw angle.

17 WIND ENERGY↗
DOE OSTI2023

Sensitivity analysis of the effect of wind and wake characteristics on wind turbine loads in a small wind farm

Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, which are typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. Turbines experience altered inflow conditions when placed in the wake of upstream turbines, which can additionally influence the fatigue and ultimate loads. It is important to understand the impact of uncertainty on the resulting loads of both unwaked and waked turbines. The goal of this work is to assess which wind-inflow-related and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters are screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study uses the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine, simulated with OpenFAST and synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with the IEC exponential coherence model using the NREL tool TurbSim. The focus is on sensitivity to individual parameters, though interactions between parameters are considered, and how sensitivity differs between waked and unwaked turbines. The results of this work show that for both waked and unwaked turbines, ambient turbulence in the primary wind direction and shear are the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines are identified as yaw misalignment, streamwise integral length, and the exponent and streamwise components of the IEC coherence model. The tertiary parameters of importance differ between waked and unwaked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, air density, and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation, lateral and vertical wind integral lengths, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in the fatigue and ultimate load sensitivity of downstream turbines.

17 WIND ENERGY↗
NASA NTRS1959

Transonic Aerodynamic Characteristics of a 45 deg Swept Wing Fuselage Model with a Finned and Unfinned Body Pylon Mounted Beneath the Fuselage or Wing, Including Measurements of Body Loads

An investigation of a model of a standard size body in combination with a representative 45 deg swept-wing-fuselage model has been conducted in the Langley 8-foot transonic pressure tunnel over a Mach number range from 0.80 to 1.43. The body, with a fineness ratio of 8.5, was tested with and without fins, and was pylon-mounted beneath the fuselage or wing. Force measurements were obtained on the wing-fuselage model with and without the body, for an angle-of-attack range from -2 deg to approximately 12 deg and an angle-of-sideslip range from -8 deg to 8 deg. In addition, body loads were measured over the same angle-of-attack and angle-of-sideslip range. The Reynolds number for the investigation, based on the wing mean aerodynamic chord, varied from 1.85 x 10(exp 6) to 2.85 x 10(exp 6). The addition of the body beneath the fuselage or the wing increased the drag coefficient of the complete model over the Mach number range tested. On the basis of the drag increase per body, the under-fuselage position was the more favorable. Furthermore, the bodies tended to increase the lateral stability of the complete model. The variation of body loads with angle of attack for the unfinned bodies was generally small and linear over the Mach number range tested with the addition of fins causing large increases in the rates of change of normal-force coefficient and nose-down pitching-moment coefficient. The variation of body side-force coefficient with sideslip for the unfinned body beneath the fuselage was at least twice as large as the variation of this load for the unfinned body beneath the wing. The addition of fins to the body beneath either the fuselage or the wing approximately doubled the rate of change of body side-force coefficient with sideslip. Furthermore, the variation of body side-force coefficient with sideslip for the body beneath the wing was at least twice as large as the variation of this load with angle of attack.

Wornom, Dewey E.↗
NASA NTRS2019

The ILRS: Approaching 20 Years and Planning for the Future

The International Laser Ranging Service (ILRS) was established by the International Association of Geodesy (IAG) in 1998 to support programs in geodesy, geophysics, fundamental constants and lunar research, and to provide the International Earth Rotation Service with data products that are essential to the maintenance and improvement in the International Terrestrial Reference Frame (ITRF), the basis for metric measurements of changes in the Earth and Earth–Moon system. Other scientific products derived from laser ranging include precise geocentric positions and motions of ground stations, satellite orbits, components of Earth’s gravity field and their temporal variations, Earth Orientation Parameters, precise lunar ephemerides and information about the internal structure of the Moon. Laser ranging systems are already measuring the one-way distance to remote optical receivers in space and are performing very accurate time transfer between remote sites in the Earth and in Space. The ILRS works closely with the IAG’s Global Geodetic Observing System. The ILRS develops (1) the standards and specifications necessary for product consistency, and (2) the priorities and tracking strategies required to maximize network efficiency. The service collects, merges, analyzes, archives and distributes satellite and lunar laser ranging data to satisfy a variety of scientific, engineering, and operational needs and encourages the application of new technologies to enhance the quality, quantity, and cost effectiveness of its data products. The ILRS works with (1) new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity, and (2) science programs to optimize scientific data yield. Since its inception, the ILRS has grown to include forty laser ranging stations distributed around the world. The ILRS stations track more than ninety satellites from low Earth orbit (LEO) to the geosynchronous orbit altitude as well as retroreflector arrays on the surface of the Moon. Applications have been expanded to include time transfer, asynchronous ranging for targets at extended ranges, free space quantum telecommunications, and the tracking of space debris. Laser ranging technology is moving to lower energy, higher repetition rates (kHz), single-photon-sensitive detectors, shorter pulse widths, shorter normal point intervals for faster data acquisition, and increased pass interleaving, automated to autonomous operation with remote access, and embedded software for real-time updates and decision making. An example of pass interleaving is presented for the Yarragadee station (see Fig. 4); tracking of LEO satellites is often accommodated during break in LEO and GNSS passes. New satellites arrays provide more compact targets and work continues on the development of lighter less expensive arrays for satellites and the moon. The service now provides operational ITRF products including daily/ weekly station positions and daily resolution Earth orientation products; the flow of weekly combination of satellite orbit files for LAGEOS/Etalon-1 and -2 has recently been established. New products are under testing through a pilot project on systematic error monitoring currently underway. The article will give an overview of activities underway within the service, paths forward presently envisioned, and current issues and challenges.

Laser retroreflectors↗
NASA NTRS1981

Variation Principles and Applications in the Study of Cell Structure and Aging

In this report we have attempted to show that "some reality lies concealed in biological variation". This "reality" has its principles, laws, mechanisms, and rules, only a few of which we have sketched. A related idea we pursued was that important information may be lost in the process of ignoring frequency distributions of physiological variables (as is customary in experimental physiology and gerontology). We suggested that it may be advantageous to expand one's "statistical field of vision" beyond simple averages +/- standard deviations. Indeed, frequency distribution analysis may make visible some hidden information not evident from a simple qualitative analysis, particularly when the effect of some external factor or condition (e.g., aging, dietary chemicals) is being investigated. This was clearly illustrated by the application of distribution analysis in the study of variation in mouse liver cellular and fine structure, and may be true of fine structural studies in general. In living systems, structure and function interact in a dynamic way; they are "inseparable," unlike in technological systems or machines. Changes in fine structure therefore reflect changes in function. If such changes do not exceed a certain physiologic range, a quantitative analysis of structure will provide valuable information on quantitative changes in function that may not be possible or easy to measure directly. Because there is a large inherent variation in fine structure of cells in a given organ of an individual and among individuals, changes in fine structure can be analyzed only by studying frequency distribution curves of various structural characteristics (dimensions). Simple averages +/- S.D. do not in general reveal all information on the effect of a certain factor, because often this effect is not uniform; on the contrary, this will be apparent from distribution analysis because the form of the curves will be affected. We have also attempted to show in this chapter that similar general statistical principles and mechanisms may be operative in biological and technological systems. Despite the common belief that most biological and technological characteristics of interest have a symmetric bell-shaped (normal or Gaussian) distribution, we have shown that more often than not, distributions tend to be asymmetric and often resemble a so-called log-normal distribution. We saw that at least three general mechanisms may be operative, i.e., nonadditivity of influencing factors, competition among individuals for a common resource, and existence of an "optimum" value for a studied characteristic; more such mechanisms could exist.

Economos, Angelos C.↗
NASA NTRS2011

An Empirical State Error Covariance Matrix for Batch State Estimation

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).

Frisbee, Joseph H., Jr.↗
NASA NTRS2001

Sustained attention performance during sleep deprivation: evidence of state instability

Nathaniel Kleitman was the first to observe that sleep deprivation in humans did not eliminate the ability to perform neurobehavioral functions, but it did make it difficult to maintain stable performance for more than a few minutes. To investigate variability in performance as a function of sleep deprivation, n = 13 subjects were tested every 2 hours on a 10-minute, sustained-attention, psychomotor vigilance task (PVT) throughout 88 hours of total sleep deprivation (TSD condition), and compared to a control group of n = 15 subjects who were permitted a 2-hour nap every 12 hours (NAP condition) throughout the 88-hour period. PVT reaction time means and standard deviations increased markedly among subjects and within each individual subject in the TSD condition relative to the NAP condition. TSD subjects also had increasingly greater performance variability as a function of time on task after 18 hours of wakefulness. During sleep deprivation, variability in PVT performance reflected a combination of normal timely responses, errors of omission (i.e., lapses), and errors of commission (i.e., responding when no stimulus was present). Errors of omission and errors of commission were highly intercorrelated across deprivation in the TSD condition (r = 0.85, p = 0.0001), suggesting that performance instability is more likely to include compensatory effort than a lack of motivation. The marked increases in PVT performance variability as sleep loss continued supports the "state instability" hypothesis, which posits that performance during sleep deprivation is increasingly variable due to the influence of sleep initiating mechanisms on the endogenous capacity to maintain attention and alertness, thereby creating an unstable state that fluctuates within seconds and that cannot be characterized as either fully awake or asleep.

Non-NASA Center↗