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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 1,765 records · Page 98
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NASA NTRS1991

An analytically linearized helicopter model with improved modeling accuracy

An analytically linearized model for helicopter flight response including rotor blade dynamics and dynamic inflow, that was recently developed, was studied with the objective of increasing the understanding, the ease of use, and the accuracy of the model. The mathematical model is described along with a description of the UH-60A Black Hawk helicopter and flight test used to validate the model. To aid in utilization of the model for sensitivity analysis, a new, faster, and more efficient implementation of the model was developed. It is shown that several errors in the mathematical modeling of the system caused a reduction in accuracy. These errors in rotor force resolution, trim force and moment calculation, and rotor inertia terms were corrected along with improvements to the programming style and documentation. Use of a trim input file to drive the model is examined. Trim file errors in blade twist, control input phase angle, coning and lag angles, main and tail rotor pitch, and uniform induced velocity, were corrected. Finally, through direct comparison of the original and corrected model responses to flight test data, the effect of the corrections on overall model output is shown.

Jensen, Patrick T.↗
DOE OSTI2018

Decay curve analysis for data error quantification in time-domain induced polarization imaging

In recent years, the time-domain induced polarization (TDIP) imaging technique has emerged as a suitable method for the characterization and the monitoring of hydrogeologic and biogeochemical processes. However, one of the major challenges refers to the resolution of the electrical images. Hence, various studies have stressed the importance of data processing, error characterization, and the deployment of adequate inversion schemes. A widely accepted method to assess data error in electrical imaging relies on the analysis of the discrepancy between normal and reciprocal measurements. Nevertheless, the collection of reciprocals doubles the acquisition time and is only viable for a limited subset of commonly used electrode configurations (e.g., dipole-dipole [DD]). To overcome these limitations, we have developed a new methodology to quantify the data error in TDIP imaging, which is entirely based on the analysis of the recorded IP decay curve and does not require recollection of data (e.g., reciprocals). The first two steps of the methodology assess the general characteristics of the decay curves and the spatial consistency of the measurements for the detection and removal of outliers. In the third and fourth steps, we quantify the deviation of the measured decay curves from a smooth model for the estimation of random error of the total chargeability and transfer resistance measurement. The error models and imaging results obtained from this methodology - in the following referred to as "decay curve analysis" - are compared with those obtained following a conventional normal-reciprocal analysis revealing consistent results. As a result, we determine the applicability of our methodology with real field data collected at the floodplain scale (approximately 12 ha) using multiple gradient and DD configurations.

58 GEOSCIENCES↗
DOE OSTI2023

Optimal PMU design based on sampling model and sensitivity analysis

The precise measurements of the synchrophasor and frequency from phasor measurement units (PMUs) are widely used in power grid applications. With the improvement of the technique, applications always require stable dynamic performance and higher accuracy for the synchrophasor and frequency measurements, which is challenging for PMU development. To evaluate the contribution of PMU hardware to measurement accuracy, this paper proposes a general-purpose sampling model to analyze the measurement error. In the proposed sampling model, a strict mathematical derivation is derived, where its error is purely determined by the parameters of the PMU hardware. The sensitivity analysis is carried out by three methods, including mathematical analysis, computer simulation, and variance-based sensitivity analysis. Through the sensitivity analysis, this paper establishes the systematic formulation and the inclusion of synchrophasor, frequency, and ROCOF. Experimental results based on the real-world testbench involving distribution-level PMUs match the mathematical analysis conclusion, which verifies the correctness of the general-purpose sampling model. Furthermore, a strategy for the optimal PMU design is proposed, which could guide PMU design in the future.

42 ENGINEERING↗
DOE OSTI2024

Baryon acoustic oscillation theory and modelling systematics for the DESI 2024 results

This paper provides a comprehensive overview of how fitting of baryon acoustic oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument’s (DESI) 2024 results using its DR1 data set, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic (⁠α iso ⁠) and anisotropic (⁠α ap ) BAO distance scales, and how these can be effectively removed with an appropriate choice of reconstruction algorithm. We then demonstrate how theory leads to a clear choice for how to model the BAO and develop, implement, and validate a new model for the remaining smooth-broad-band (i.e. without BAO) component of the galaxy clustering. Finally, we explore the impact of all remaining modelling choices on the BAO constraints from DESI using a suite of high-precision simulations, arriving at a set of best practices for DESI BAO fits, and an associated theory and modelling systematic error. Overall, our results demonstrate the remarkable robustness of the BAO to all our modelling choices and motivate a combined theory and modelling systematic error contribution to the post-reconstruction DESI BAO measurements of no more than 0.1 per cent (0.2 per cent) for its isotropic (anisotropic) distance measurements. We expect the theory and best practices laid out to here to be applicable to other BAO experiments in the era of DESI and beyond.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1998

The Error Distribution of BATSE GRB Location

We develop empirical probability models for BATSE GRB location errors by a Bayesian analysis of the separations between BATSE GRB locations and locations obtained with the InterPlanetary Network (IPN). Models are compared and their parameters estimated using 394 GRBs with single IPN annuli and 20 GRBs with intersecting IPN annuli. Most of the analysis is for the 4B (rev) BATSE catalog; earlier catalogs are also analyzed. The simplest model that provides a good representation of the error distribution has 78% of the locations in a 'core' term with a systematic error of 1.85 degrees and the remainder in an extended tail with a systematic error of 5.36 degrees, implying a 68% confidence region for bursts with negligible statistical errors of 2.3 degrees. There is some evidence for a more complicated model in which the error distribution depends on the BATSE datatype that was used to obtain the location. Bright bursts are typically located using the CONT datatype, and according to the more complicated model, the 68% confidence region for CONT-located bursts with negligible statistical errors is 2.0 degrees.

Briggs, Michael S.↗
NASA NTRS1985

An analysis of the effects of coincident errors on multi-version software

The present study is concerned with the application of a model which provides a basis for analytical investigations of redundant software. The model links certain basic quantities with the experimental process of testing independently designed software components. The model is employed to provide insight into the effects of coincident errors on an N-version software structure in which the structure fails if at least a majority of its components fail. Attention is given to coincident errors, the intensity distribution for coincident errors, the coincident errors model, and a condition for redundancy to improve reliability.

Eckhardt, D. E., Jr.↗
NASA NTRS1999

Optimized System Identification

In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.

Juang, Jer-Nan↗
NASA NTRS2012

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Diallo, Ousmane H.↗
DOE OSTI2014

A flexible method to calculate the distributions of discretization errors in operator-split codes with stochastic noise in problem data

This report investigates a technique to calculate the distributions of discretization errors for a model of advection-diffusion-reaction with stochastic noise in problem data. The focus is on operator-split discretization methods. The error is decomposed into components due to the splitting and due to the discretization within each component. We present a method to estimate the distributions of the total discretization error for a quantity of interest as well as the aforementioned error components. These computations require information about the propagation of uncertainty within and between code modules, for which purpose we use a flexible code framework for uncertainty quantification. Computational examples illustrate that in certain cases the distribution of numerical error can have a significant impact on the code output. Using information about the components of the numerical error, decisions can be made regarding discretization parameters to efficiently reduce the impact of numerical error on the distribution of model outputs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

DISCOVERY OF A HIGHLY UNEQUAL-MASS BINARY T DWARF WITH KECK LASER GUIDE STAR ADAPTIVE OPTICS: A COEVALITY TEST OF SUBSTELLAR THEORETICAL MODELS AND EFFECTIVE TEMPERATURES

Highly unequal-mass ratio binaries are rare among field brown dwarfs, with the mass ratio distribution of the known census described by q {sup (4.9{+-}0.7)}. However, such systems enable a unique test of the joint accuracy of evolutionary and atmospheric models, under the constraint of coevality for the individual components (the 'isochrone test'). We carry out this test using two of the most extreme field substellar binaries currently known, the T1 + T6 {epsilon} Ind Bab binary and a newly discovered 0.''14 T2.0 + T7.5 binary, 2MASS J12095613-1004008AB, identified with Keck laser guide star adaptive optics. The latter is the most extreme tight binary resolved to date (q {approx} 0.5). Based on the locations of the binary components on the Hertzsprung-Russell (H-R) diagram, current models successfully indicate that these two systems are coeval, with internal age differences of log(age) = -0.8 {+-} 1.3(-1.0{sup +1.2}{sub -1.3}) dex and 0.5{sup +0.4}{sub -0.3}(0.3{sup +0.3}{sub -0.4}) dex for 2MASS J1209-1004AB and {epsilon} Ind Bab, respectively, as inferred from the Lyon (Tucson) models. However, the total mass of {epsilon} Ind Bab derived from the H-R diagram ({approx} 80 M{sub Jup} using the Lyon models) is strongly discrepant with the reported dynamical mass. This problem, which is independent of the assumed age of the {epsilon} Ind Bab system, can be explained by a {approx} 50-100 K systematic error in the model atmosphere fitting, indicating slightly warmer temperatures for both components; bringing the mass determinations from the H-R diagram and the visual orbit into consistency leads to an inferred age of {approx} 6 Gyr for {epsilon} Ind Bab, older than previously assumed. Overall, the two T dwarf binaries studied here, along with recent results from T dwarfs in age and mass benchmark systems, yield evidence for small ({approx}100 K) errors in the evolutionary models and/or model atmospheres, but not significantly larger. Future parallax, resolved spectroscopy, and dynamical mass measurements for 2MASS J1209-1004AB will enable a more stringent application of the isochrone test. Finally, the binary nature of this object reduces its utility as the primary T3 near-IR spectral typing standard; we suggest SDSS J1206+2813 as a replacement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km 2 . Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1999

Calculation and Error Analysis of a Digital Elevation Model of Hofsjokull, Iceland from SAR Interferometry

Two ascending European Space Agency (ESA) Earth Resources Satellites (ERS)-1/-2 tandem-mode, synthetic aperture radar (SAR) pairs are used to calculate the surface elevation of Hofsjokull, an ice cap in central Iceland. The motion component of the interferometric phase is calculated using the 30 arc-second resolution USGS GTOPO30 global digital elevation product and one of the ERS tandem pairs. The topography is then derived by subtracting the motion component from the other tandem pair. In order to assess the accuracy of the resultant digital elevation model (DEM), a geodetic airborne laser-altimetry swath is compared with the elevations derived from the interferometry. The DEM is also compared with elevations derived from a digitized topographic map of the ice cap from the University of Iceland Science Institute. Results show that low temporal correlation is a significant problem for the application of interferometry to small, low-elevation ice caps, even over a one-day repeat interval, and especially at the higher elevations. Results also show that an uncompensated error in the phase, ramping from northwest to southeast, present after tying the DEM to ground-control points, has resulted in a systematic error across the DEM.

Barton, Jonathan S.↗
DOE OSTI2014

Mathematical modelling for the drying method and smoothing drying rate using cubic spline for seaweed Kappaphycus Striatum variety Durian in a solar dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m{sup 2} and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R{sup 2}), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1990

The effects of training on errors of perceived direction in perspective displays

An experiment was conducted to determine the effects of training on the characteristic direction errors that are observed when subjects estimate exocentric directions on perspective displays. Changes in five subjects' perceptual errors were measured during a training procedure designed to eliminate the error. The training was provided by displaying to each subject both the sign and the direction of his judgment error. The feedback provided by the error display was found to decrease but not eliminate the error. A lookup table model of the source of the error was developed in which the judgement errors were attributed to overestimates of both the pitch and the yaw of the viewing direction used to produce the perspective projection. The model predicts the quantitative characteristics of the data somewhat better than previous models did. A mechanism is proposed for the observed learning, and further tests of the model are suggested.

Tharp, Gregory K.↗
NASA NTRS1992

Tropical intraseasonal oscillation and its prediction by the NMC operational model

Results are presented of an investigation of the tropical intraseasonal oscillation (ISO) and its impact on the extended-range forecast in the NMC operational model during Phase II (14 December 1986-31 March 1987) of the Dynamical Extended Range Forecast. Based on principal component analysis of the velocity potential and streamfunction, evidence was found of tropical-extratropical interaction associated with the ISO. The NMC model possess significant forecast skills for the principal streamfunction and velocity potential modes up to the first ten days. Results of the error growth analysis suggest that the principal modes of velocity potential have large errors comparable to the model random errors. By comparison, the initial errors in the streamfunction are much smaller. The error growth for both tropical and extratropical modes are found to be significantly suppressed during periods of strong ISO relative to periods of weak ISO. The increase in extratropical forecast skill is likely due to (1) the model's ability to better capture ISO signals in the tropics and (2) the increased coupling between the tropics and extratropics during periods of strong ISO.

Lau, K.-M.↗
DOE OSTI2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

17 WIND ENERGY↗
DOE OSTI2019

Constitutive Modeling for Predicting High-Temperature Flow Behavior in Aluminum 5083+10 Wt Pct SiC{sub p} Composite

A constitutive model capable of predicting material flow behavior with high precision is essential for optimizing secondary processing parameters through simulation techniques. In this study, the hot deformation behavior of aluminum 5083+10 wt pct SiC particulate composite was predicted using constitutive equations based on the modified Johnson–Cook (JC), modified Zerilli–Armstrong (ZA), and strain-compensated Arrhenius models. The models were established on the basis of the true stress–strain values obtained from an isothermal hot compression test conducted on the INSTRON 8801 universal tensile testing machine under a temperature range of 473 K to 773 K and strain rate of 0.01 to 10 s{sup −1}. The prediction ability of the modified models was compared by calculating the correlation coefficient (R), average absolute relative error, and relative error. All the models could precisely predict the hot flow behavior of the composite. The modified ZA model had the highest accuracy. The JC model required the least number of material constants and had the lowest calculation time, followed by the modified ZA and strain-compensated Arrhenius models.

36 MATERIALS SCIENCE↗