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Results for “Unbiased estimator”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

SSG-4 - An automated spring small grains proportion estimator

In connection with an implementation of the classification procedures employed in the Large Area Crop Inventory Experiment (LACIE), a human analyst had to provide labeled samples. The present investigation is concerned with an automated proportion estimation procedure which has been derived from the early field-labeling procedures used in LACIE. This procedure was developed for the U.S./Canada Spring Small Grains Pilot Experiment. It is demonstrated that the considered spatial/color-based proportion estimation procedure provides the agricultural remote-sensing community with the basic tools to develop unbiased and highly efficient procedures for obtaining crop area estimates at the end of the season.

Dennis, T. B.↗
DOE OSTI2021

Verification of Combined VR Techniques, Derivation of Future Time Equation, and Integration of LLNL Pulsed Sphere V&V Suite [Slides]

To determine whether the combination of forced-collision and DXTRAN variance-reduction (VR) techniques is unbiased, and to gain insight into the operation of these techniques, proof that first-moment estimates from Monte Carlo simulations employing both techniques are unbiased is developed. A general background on the forced-collision and DXTRAN VR techniques and their combination is given. Proof of an unbiased simulation is outlined by showing the equivalence of the history score moment equations of simulations with these techniques in use. A report with detailed proof of this equivalence is available upon request. The derivation of the future time equation using a similar approach, as well as a summary of the addition of the LLNL Pulsed Sphere experiments to the MCNP verification and validation suite, is also briefly discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers

Abstract Detection of gravitational waves (GWs) from neutron star-black hole (NSBH) standard sirens provides local measurements of the Hubble constant (H 0 ), regardless of the detection of an electromagnetic (EM) counterpart, given that matter effects can be exploited to break the redshift degeneracy of the GW waveforms. The distinctive merger morphology and the high-redshift detectability of tidally disrupted NSBH make them promising candidates for this method. Also, the detection prospects of an EM counterpart for these systems will be limited toz< 0.8 in the optical, in the era of future GW detectors. Using recent constraints on the equation of state of NSs from multi-messenger observations of NICER and LIGO/Virgo/KAGRA, we show the prospects of measuringH 0 solely from GW observation of NSBH systems, achievable by the Einstein telescope (ET) and Cosmic Explorer (CE) detectors. We first analyze individual events to quantify the effect of high-frequency (≥500 Hz) tidal distortions on the inference of NS tidal deformability parameter (Λ) and hence onH 0 . We find that disruptive mergers can constrain Λ up to  ( 60 % ) more precisely than nondisruptive ones. However, this precision is not sufficient to place stringent constraints on theH 0 from individual events. By performing Bayesian analysis on simulated NSBH data (up toN= 100 events, corresponding to a day of observation) in the ET+CE detectors, we find that NSBH systems enable unbiased 4%–13% precision on the estimate ofH 0 (68% credible interval). This is a similar measurement precision found in studies analyzing NSBH mergers with EM counterparts in the LVKC O5 era.

Astronomy & Astrophysics↗
NASA NTRS1999

Publication Bias ( The "File-Drawer Problem") in Scientific Inference

Publication bias arises whenever the probability that a study is published depends on the statistical significance of its results. This bias, often called the file-drawer effect since the unpublished results are imagined to be tucked away in researchers' file cabinets, is potentially a severe impediment to combining the statistical results of studies collected from the literature. With almost any reasonable quantitative model for publication bias, only a small number of studies lost in the file-drawer will produce a significant bias. This result contradicts the well known Fail Safe File Drawer (FSFD) method for setting limits on the potential harm of publication bias, widely used in social, medical and psychic research. This method incorrectly treats the file drawer as unbiased, and almost always miss-estimates the seriousness of publication bias. A large body of not only psychic research, but medical and social science studies, has mistakenly relied on this method to validate claimed discoveries. Statistical combination can be trusted only if it is known with certainty that all studies that have been carried out are included. Such certainty is virtually impossible to achieve in literature surveys.

Scargle, Jeffrey D.↗
NASA NTRS2012

An Integrated Approach for Gear Health Prognostics

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

He, David↗
DOE OSTI2020

Hedonic Price Estimates of Lake Water Quality: Valued Attribute, Instrumental Variables, and Ecological-Economic Benefits

We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators

We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high k modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Kumar, Sujay V.↗
NASA NTRS1979

Large Area Crop Inventory Experiment (LACIE). Development of procedure M for multicrop inventory, with tests of a spring-wheat configuration

The author has identified the following significant results. An outgrowth of research and development activities in support of LACIE was a multicrop area estimation procedure, Procedure M. This procedure was a flexible, modular system that could be operated within the LACIE framework. Its distinctive features were refined preprocessing (including spatially varying correction for atmospheric haze), definition of field like spatial features for labeling, spectral stratification, and unbiased selection of samples to label and crop area estimation without conventional maximum likelihood classification.

Horvath, R.↗
DOE OSTI2024

Large-Volume Injection and Assessment of Reference Standards for n -Alkane δD and δ 13 C Analysis via Gas Chromatography Isotope Ratio Mass Spectrometry

Compound-specific stable isotope analysis of hydrogen (δD) and carbon (δ 13 C) in organic compounds is a valuable tool in biogeochemical research. A key limitation of this method is the relatively large amount of sample required to achieve desirable precision. We developed a large-volume (20 μL) injection method that allows for high throughput analysis of less concentrated samples and tested it for δ 13 C and δD measurements of n-alkanes. We also conducted a comparison of reference standards and assessed several methods to normalize and correct n-alkane δD and δ13C measurements. The mean precision of the δD method based on 233 environmental n-alkane samples (two to three replications per sample) is 4.0‰ (1σ, estimated from the weighted mean of the pooled unbiased standard deviations) and 0.46‰ (1σ) for δ 13 C from 37 environmental samples (two to three replications per sample). The evaluation of reference standards shows that the use of n-alkane standards with large offsets in δD values in adjacent n-alkane chains can lead to biases in measurement correction. The large-volume injection method shows good reproducibility of δ 13 C and δD measurements of n-alkanes and reduces the required sample concentration by about 80%. We propose that for δD measurements, a reference standard set should be used in which each reference standard has a limited range of δD values and no adjacent n-alkane chains, to minimize memory effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗
NASA NTRS1972

On estimating the Venus spin vector

The improvement in spin vector and probe position estimates one may reasonably expect from the processing of such data is indicated. This was done by duplicating the ensemble calculations associated with a weighed least squares with a priori estimation technique applied to range rate data that were assumed to be unbiased and uncorrelated. The weighting matrix was assumed to be the inverse of the covariance matrix of the noise on the data. Attention is focused primarily on the spin vector estimation.

Argentiero, P. D.↗
DOE OSTI2021

Proof that Combining the Forced-collision and DXTRAN Monte Carlo Variance-reduction Techniques is Fair

This report provides mathematical proof that the MCNP DXTRAN (also known in other codes as forced-flight) and forced-collision variance-reduction techniques do not bias the expected value of Monte Carlo simulation estimates when combined. Proof is also provided that the techniques are unbiased when used independently. To prove that the techniques are unbiased, this report derives the first History Score Moment Equation (HSME) for non-multiplying media employing only forced collisions, only DXTRAN, and then both the variance-reduction techniques combined, as defined next. The HSMEs are found by forming the History Score Probability Density Functions (HSPDFs) and then taking the first score moment. Following its derivation, each HSME with a variance-reduction technique employed is reduced to the HSME for an analog simulation. Because the HSME represents the expected contribution to estimators in the simulation, reducing the HSME with variance reduction to the analog HSME shows that the simulation is unbiased despite the variance-reduction technique considered in the HSME. Analysis regarding higher-score moments of each technique is the subject of prior work and is not addressed herein. Throughout this work, the phase space p is defined to be the particle position x, direction-of-flight unit vector $\hat{Ω}$, energy E, and statistical weight w, $$p ≡ (x; \hat{Ω}; E; w).$$ A reduced phase-space excluding the statistical weight of the particle, $$r ≡ (x; \hat{Ω}; E),$$ is also used.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Robust detection, isolation and accommodation for sensor failures

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Emami-Naeini, A.↗
NASA NTRS1993

Evaluation of a nonlinear method for the enhancement of tonal signal detection

A method is presented for biasing spectral estimates to enhance detection of tonal signals against a background of broadband noise. In this method, a nonlinear average of an ensemble of individual spectral estimates is made where broadband noise energy is biased downward, pure tone energy is unbiased, and a mixture of the two is biased by an amount that depends on the ratio of tonal energy to broadband energy. The method is analyzed to provide estimates of the extent of tonal signal detection enhancement.

Garber, Donald P.↗
NASA NTRS1999

The Effects of LOX Post Biasing on SSME Injector Wall Compatibility

An experimental investigation has been carried out to examine the effects of LOX post biasing of a shear coaxial injector on the behavior of the spray near a chamber wall. The experimental work was performed with inert propellant simulants in a high-pressure chamber. Injector flow rates and chamber pressure were designed to match the Space Shuttle Main Engine (SSME) injector gas-to-liquid density and velocity ratio at the point of propellant injection. Measurements of liquid mass flux, gas phase velocity and droplet size were made using mechanical patternation and phase Doppler interferometry techniques. The measurements revealed that the liquid mass flux distribution shifts away from the wall with increasing LOX post bias away from the wall. The shift in the liquid flux distribution was much greater than that caused by the angling of the LOX post alone. Gas velocity near the wall simultaneously increased with increasing LOX post bias away from the wall. The increase in wall side gas velocity was due to the higher fraction of gas injected on the wall side of the injector as a result of the eccentricity at the injector exit. The net result is a decrease in mixture ratio near the wall. Estimates of heat transfer and engine performance relative to the unbiased case are presented.

Strakey, P. A.↗
NASA NTRS2016

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

SMOS↗
DOE OSTI2023

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools . In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

14 SOLAR ENERGY↗