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NASA NTRS2016

A Comparison of Metamodeling Techniques via Numerical Experiments

This paper presents a comparative analysis of a few metamodeling techniques using numerical experiments for the single input-single output case. These experiments enable comparing the models' predictions with the phenomenon they are aiming to describe as more data is made available. These techniques include (i) prediction intervals associated with a least squares parameter estimate, (ii) Bayesian credible intervals, (iii) Gaussian process models, and (iv) interval predictor models. Aspects being compared are computational complexity, accuracy (i.e., the degree to which the resulting prediction conforms to the actual Data Generating Mechanism), reliability (i.e., the probability that new observations will fall inside the predicted interval), sensitivity to outliers, extrapolation properties, ease of use, and asymptotic behavior. The numerical experiments describe typical application scenarios that challenge the underlying assumptions supporting most metamodeling techniques.

Crespo, Luis G.↗

DOE OSTI2024

Multicriteria screening evaluation of geothermal resources on mine lands for direct use heating

Abstract Direct use of geothermal energy is the oldest and most versatile form of utilizing geothermal energy. In the last decade, this utilization has significantly increased, especially with the installation of geothermal (ground-source) heat pumps. Many current and inactive mine land sites across the U.S. could be redeveloped with clean energy technologies such as direct use geothermal, which would revitalize former mining communities, help with reducing greenhouse gas emissions, and accelerate the transition to a clean energy economy. We present a multicriteria screening framework to evaluate various aspects of direct-use geothermal projects on mine lands. The criteria are divided into three categories: (1) technical potential, (2) demand and benefits, and (3) regulatory and permitting. We demonstrate the framework using publicly available data on a national scale (continental U.S.). Then, using an example of abandoned coal mines in Illinois and focusing on resource potential, we illustrate how this evaluation can be applied at the state or more local scales when a region’s characteristics drive spatial variability estimates. The strength of this approach is the ability to combine seemingly disparate parameters and inputs from numerous sources. The framework is very flexible—additional criteria can be easily incorporated and weights modified if input data support them. Vice versa, the framework can also help identify additional data needed for evaluating those criteria. The multicriteria screening evaluation methodology provides a framework for identifying potential candidates for detailed site evaluation and characterization.

15 GEOTHERMAL ENERGY↗

NASA NTRS1992

Estimation of bare soil evaporation using multifrequency airborne SAR

It is shown that for homogeneous areas soil moisture can be derived from synthetic aperture radar (SAR) measurements, so that the use of microwave remote sensing can given realistic estimates of energy fluxes if coupled to a simple two-layer model repesenting the soil. The model simulates volumetric water content (Wg) using classical meterological data, provided that some of the soil thermal and hydraulic properties are known. Only four parameters are necessary: mean water content, thermal conductivity and diffusitivity, and soil resistance to evaporation. They may be derived if a minimal number of measured values of Wg and surface layer temperature (Tg) are available together with independent measurements of energy flux to compare with the estimated values. The estimated evaporation is shown to be realistic and in good agreement with drying stage theory in which the transfer of water in the soil is in vapor form.

Soares, Joao V.↗

NASA NTRS1994

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Litt, Jonathan↗

DOE OSTI2021

Parameter inference from event ensembles and the top-quark mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array

A great challenge for 21 cm intensity mapping experiments is the strong foreground radiation which is orders of magnitude brighter than the 21 cm signal. Removal of the foreground takes advantage of the fact that its frequency spectrum is smooth while the redshifted 21 cm signal spectrum is stochastic. However, a complication is the non-smoothness of the instrument response. This paper describes the electromagnetic simulation of the Tianlai cylinder array, a pathfinder for 21 cm intensity mapping experiments. Due to the vast scales involved, a direct simulation requires a large amount of computing resources. We have made the simulation practical by using a combination of methods: first simulate a single feed, then an array of feed units, finally with the feed array and a cylindrical reflector together, obtain the response for a single cylinder. We studied its radiation pattern, bandpass response and the effects of mutual coupling between feed units, and compared the results with observation. Many features seen in the measurement result are reproduced well in the simulation, especially the oscillatory features which are associated with the standing waves on the reflector. The mutual coupling between feed units is quantified with S-parameters, which decrease as the distance between the two feeds increases. Based on the simulated S-parameters, we estimate the correlated noise which has been seen in the visibility data, and the results show very good agreement with the data in both magnitude and frequency structures. These results provide useful insights on the problem of 21 cm signal extraction for real instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Environmental factors have a greater influence on photosynthetic capacity in C 4 plants than biochemical subtypes or growth forms

Our understanding of how photosynthetic capacity varies among C 4 species and across growth and measurement conditions remains limited. We collated 1696 CO 2 response curves of net CO 2 assimilation rate (A/C i curves) from C 4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (V pmaxA ) and CO 2 -saturated net photosynthetic rate (A max ), two key parameters describing photosynthetic capacity. We examined how V pmaxA and A max vary with species-specific traits, growth and measurement conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1978

Observations of long-period Cepheids between l = 294 and 331 deg

Photoelectric and spectroscopic observations of long-period Cepheids, as well as 21-cm profiles in the directions of these young stars, are presented for the Crux-Centaurus-Circinus-Norma section of the Milky Way. The UBV data have been used to revise periods and light-curve parameters and ultimately to calculate the distances of these stars. From the spectroscopic information velocity estimates have been made. Finally, the stellar velocities have been compared with H I gas motions detected along the same line of sight. These results are briefly discussed with a view to understanding the spiral structure of the region.

Grayzeck, E. J.↗

NASA NTRS1984

Simulation of the earth's monthly average regional radiation balance derived from satellite measurements

Computer simulations of satellite-derived earth radiation parameters are used to determine the source and size of errors arising from measuring parameters over 1 month on a 2.5 deg x 2.5 deg longitude-latitude grid. November 1979 GOES data are used as a source of radiation parameter fields within each region. A mathematical model is given for the data-processing algorithms that are used to produce daily, monthly, and monthly hourly estimates of shortwave, longwave and net radiant exitance. Resulting retrieval errors are found to be due to insufficient knowledge of the temporal distribution of measurements. These errors, in combination with similar sampling errors produce biases in monthly net radiant exitance which are complex, regionally-dependent functions of the local time of the measurements. The 40 region standard errors of estimate for three satellites ranged from 2 to 3 W per sq. m., while single satellite errors ranged from 5 to 19 W per sq. m. A three-satellite system is therefore recommended to reduce biases.

Brooks, D. R.↗

NASA NTRS1990

Stochastic estimation of tropospheric path delays in global positioning system geodetic measurements

Water vapor radiometric and surface meteorological measurements taken during three GPS geodetic experiments are used to calculate process noise levels for random walk and first-order Gauss-Markov temporal models of tropospheric path delays. Entire wet and combined wet and dry zenith delays at each network site are estimated simultaneously with the geodetic parameters without prior calibration. The path delays and corresponding baseline estimates are compared to those obtained with calibrated data and stochastic residual delays. In this manner, the marginal utility of a priori tropospheric calibration is assessed given the ability to estimate the path delays directly using only the GPS data. Estimation of total zenith path delays with appropriate random walk or Gauss-Markov models yields baseline repeatabilities of a few parts in 100 million. Results suggest that GPS data alone have sufficient strength to resolve centimeter-level zenith path delay fluctuations over periods of a few minutes.

Tralli, David M.↗

NASA NTRS1992

Automated Characterization Of Vibrations Of A Structure

Automated method of characterizing dynamical properties of large flexible structure yields estimates of modal parameters used by robust control system to stabilize structure and minimize undesired motions. Based on extraction of desired modal and control-design data from responses of structure to known vibrational excitations. Applicable to terrestrial structures where vibrations are important - aircraft, buildings, bridges, cranes, and drill strings.

Bayard, David S.↗

NASA NTRS1992

An improved Venus gravity field from Doppler tracking of the Pioneer Venus Orbiter and Magellan spacecraft

A total of 365,000 Pioneer Venus Orbiter (PVO) S-band Doppler data points were fit in arcs ranging from one to four revolutions in length by estimating the spacecraft position and velocity, atmospheric density, and a solar pressure parameter at each arc epoch. The resultant converged PVO orbits were used to produce 194 information arrays for the Venus spherical harmonic coefficients complete to degree and order 21. A similar procedure was used to produce a second set of 72 information arrays for a 21 x 21 gravitational field from a limited, but global, sampling of Magellan S- and X-band data from cycles 1 and 2. The PVO and Magellan information arrays were combined and solved to obtain the 21 x 21 spherical harmonic expansion designated JPL-Venus Gravity Model 6A (VGM6A). The VGM6A harmonic field field produced significant improvements in the Doppler residual statistics obtained from field validation fits to both PVO and Magellan tracking data in comparison with alternative representations of Venus gravity.

Mcnamee, J. B.↗

NASA NTRS1997

Extraction of Lateral-Directional Stability and Control Derivatives for the Basic F-18 Aircraft at High Angles of Attack

The results of parameter identification to determine the lateral-directional stability and control derivatives of an F-18 research aircraft in its basic hardware and software configuration are presented. The derivatives are estimated from dynamic flight data using a specialized identification program developed at NASA Dryden Flight Research Center. The formulation uses the linearized aircraft equations of motions in their continuous/discrete form and a maximum likelihood estimator that accounts for both state and measurement noise. State noise is used to model the uncommanded forcing function caused by unsteady aerodynamics, such as separated and vortical flows, over the aircraft. The derivatives are plotted as functions of angle of attack between 3 deg and 47 deg and compared with wind-tunnel predictions. The quality of the derivative estimates obtained by parameter identification is somewhat degraded because the maneuvers were flown with the aircraft's control augmentation system engaged, which introduced relatively high correlations between the control variables and response variables as a result of control motions from the feedback control system.

Iliff, Kenneth W.↗

NASA NTRS2002

Fine Sun Sensor Field of View Calibration

The fine Sun sensor (FSS) used on many spacecraft consists of two independent single-axis sensors, nominally mounted perpendicularly, that detect Sun angle across a typical field of view of +/- 32 degrees. The nonlinear function that maps the measured counts into an observed angle is called the transfer function. The FSS transfer function provided by the manufacturer consists of nine parameters for each of the two sensitive axes. An improved transfer function has been previously reported that achieves a significant accuracy improvement across the entire field of view. This new function expands the parameter set to 12 coefficients per axis and includes cross terms combining counts from both axes. To make best use of the FSS for spacecraft attitude determination, it must be calibrated after launch. We are interested in simplifying the postlaunch calibration procedure for estimating improvements to the 24 parameters in the transfer function. This paper discusses how to recombine the terms of the transfer function to reduce their redundancy without decreasing its accuracy and then presents an attitude dependent procedure for estimating the parameters. The end result is a calibration algorithm that is easier to use and does not sacrifice accuracy. Results of calibration using on-orbit data are presented.

Sedlak, Joseph E.↗

NASA NTRS2003

Fuzzification of Electromagnetic Interference Patterns Onboard Commercial Airliners Due to Wireless Technology

The use of portable wireless technology has increased dramatically over the past few years. Over the years however, numerous reports have cited portable electronic devices (PEDs) as a possible cause of electromagnetic interference (EMI) to aircraft navigation and communication radio systems. PEDs may act as transmitters and their signals may be detected by the various radio receiver antennas installed on the aircraft. Measurement of the radiated field coupling between passenger cabin locations and aircraft communication and navigation receivers, via their antennas is defined herein as interference path loss (IPL). Personnel from NASA Langley Research Center, Eagles Wings Inc., and United Airlines performed extensive IPL measurements on several Boeing 737 airplanes. In previous work, the IPL data collected was graphically plotted and presented using MATLAB. This paper provides an introductory result of modeling EMI patterns using Fuzzy Logic, using the graphical analysis of the IPL data summarized. The application of fuzzy logic seeks to provide a means of estimating IPL at various locations within an airplane passenger cabin using simple modeling parameters. Fuzzy logic methods may provide a means to assess IPL characteristics of aircraft that have not been subject to expensive measurement or modeling processes and may also be useful for estimating the merit of aircraft design changes intended to minimize the potential for EMI.

Jafri, Madiha↗

NASA NTRS2012

Accelerated Aging in Electrolytic Capacitors for Prognostics

The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics-based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian-based implementation of model-based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave-one-out validation methodology is used to assess the validity of the methodology under the small sample size constrain. The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on-line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.

Celaya, Jose R.↗