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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 1,387 records · Page 77

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NASA NTRS1988

Circulation from a joint gravity field solution determination of the general ocean

With the development of satellite altimetry, it is possible to infer the geostrophic velocity of the surface ocean currents, if the geoid and the position of the satellite are known accurately. Errors in current geoid models and orbit computations, both due primarily to errors in the earth's gravity field model, have limited the use of altimeter data for this purpose. The objective of this investigation is to demonstrate that altimeter data can be used in a joint solution to simultaneously estimate the quasi-stationary sea surface topography, zeta, and the model for the gravity field. Satellite tracking data from twelve satellites were used along with Seasat altimeter data for the solution. The estimated model of zeta compares well at long wavelengths with the hydrographic model of zeta. Covariance analysis indicates that the geoid is separable from zeta up to degree 9, at which point geoid error is comparable to the signal of zeta.

Tapley, B. D.↗

NASA NTRS1987

Application of TOMS data to weather analysis models

Recent studies of the errors of analysis reflected by numerical weather analysis models are reviewed. Despite the improvements in data coverage and data ingestion methods in the past decade, it is found that significant errors of analysis persist. A case study comparison of Total Ozone Mapping Spectrometer (TOMS) data and aircraft data with the NMC analysis over the North Atlantic is used to illustrate the size of local errors encountered. The possibility of using TOMS images to locate meteorologically significant features such as troughs and ridges near the tropopause is discussed.

Nastrom, Gregory D.↗

NASA NTRS1992

Reliability prediction for a class of highly reliable digital systems

Three theorems which show that the reliability of a popular class of systems can be computed using small and simple models are presented. This class consists of systems that are assemblages of subsystems where each subsystem is a majority-voting threeplex plus spares or majority-voting fourplex plus spares. The theorems are error bounds for model reduction and simplification. The error bounds are given in terms of readily available system parameters. The three theorems have been applied to a system that has been used as an example that generates extremely large reliability models; the system considered is one version of AIPS (Advanced Information Processing System) for IAPSA (Integrated Airframe Propulsion System Architecture).

White, Allan L.↗

DOE OSTI2023

The battle of clean and dirty qubits in the era of partial error correction

When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work, we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the “clean and dirty” setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in the concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The battle of clean and dirty qubits in the era of partial error correction

When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the "clean and dirty" setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Cost-constrained adaptive simulations of transient spray combustion in a gas turbine combustor

Predictive high-fidelity simulations of turbulent spray combustion must capture the combined effects of complex chemistry, multiphase evaporating flow and spray-flame interactions to achieve physical accuracy. Finite-rate chemistry (FRC) combined with a realistic chemical mechanism is a combustion model well-suited for this purpose, but has a high computational cost due to the large number and stiffness of transported chemical species. In contrast, flamelet-based models achieve lower cost by transporting a small number of quantities of reduced stiffness, but assumptions regarding local flame topology, boundary conditions and inter-phase coupling limit their physical accuracy. Recently, the Pareto-efficient combustion (PEC) framework was developed to dynamically assign combustion models based on local cost and accuracy metrics in gas-phase reacting flows. In this work, we extend this PEC framework to spray combustion through the rigorous analysis of the multiphase coupling terms in the governing equations. The derivation shows that spray evaporation causes errors in the prediction of species mass fractions for flamelet-based models due to the sensitivity of the local thermo-chemical state to changes in composition caused by fuel vaporization across combustion regimes present in practical spray combustion devices. Sub-model assignment is formulated as a multiple-choice knapsack problem, where computational cost is directly controlled through the fraction of the domain assigned to the FRC sub-model. The extended PEC formulation is applied to the simulation of a realistic rich-quench-lean gas turbine combustor at steady-state conditions, as well as transient operation resulting in lean blow-out (LBO). Analysis of transient simulations during LBO demonstrates the extended PEC formulation’s capacity to dynamically adapt to changing conditions within the combustor. Transient combustor dynamics are shown to approach convergence with limited increases in computational cost, while retaining substantial computational cost reduction compared to monolithic FRC simulations. Through PEC simulations with increasing fractions of the domain assigned to FRC, monolithic flamelet simulations are shown to over-predict flame stability during LBO. Finally, the extended PEC formulation is thus shown to overcome deficiencies of monolithic models by controlling modeling error for multiphase combustion modeling.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1975

Sensitivity analysis of short-arc station coordinate determinations from range data

The accurate determination of the geocentric coordinates of a tracking station is essential for most geodetic and geophysical satellite applications. Since most of these satellites are close to the earth, the geopotential model is a dominant source of error which significantly influences station coordinate determinations. Other sources, such as GM error and drag, also influence the accuracy of the station coordinate determination. One technique for reducing the effect of these errors is to use short-arcs consisting of a few passes of the satellite over the tracking station. This paper analyzes the sensitivity of short-arc station coordinate estimates to various errors in the physical model, to the number of observations, and to the station-satellite geometry using simulated as well as real data.

Schutz, B. E.↗

NASA NTRS1976

Sensitivity analysis of short-arc station coordinate determinations from range data

Schutz, B. E.↗

NASA NTRS1978

Model for redundant-sensor signal errors

Report describes application of order statistics to analysis of failure modes in redundant systems. Report provides detailed mathematical discussion of approach, accompanied by graphs and equations. Techniques are relevant to process that demand high reliability, such as manufacture of dangerous chemicals. It also includes discussion of quad-redundant system.

Sowada, D. J.↗

NASA NTRS1984

Mathematical models for cochannel interference in FH/MFSK multiple-access systems

FH/MFSK has been proposed for a multiuser spread spectrum digital communication system to combat both self-jamming and intentional jamming. An independence assumption at the energy detector outputs is used to evaluate such a system for a digitized voice mobile radio system (Yue, 1981). This paper presents a correlated model and compares its performance to an independence model under the symbol error probability criterion. A Gaussian process model is also developed for comparison. It is found that the conventional Gaussian approximation is inadequate in predicting the number of users that can be accommodated by an FH/MFSK system using the conventional receiver. Furthermore, for the cases evaluated, the independence model provides an excellent approximation to the correlated model. Additionally, a conditional Chernoff bound is presented for the more general case of frequency-hopping systems with multiple hops per symbol.

Yan, T.-Y.↗

NASA NTRS1992

Survey Data for Geomagnetic Field Modelling

The survey data discussed here are based on observations made relatively recently at points on land. A special subset of land survey data consists of those made at specially designated sites known as repeat stations. This class of data will be discussed in another part of this document (Barton, 1991b), so only the briefest of references will be made to repeat stations here. This discussion of 'ordinary' land survey data begins with a description of the spatial and temporal distributions of available survey data based on observations made since 1900. (The reason for this rather arbitrary choice of cut-off date is that this was the value used in the production of the computer file of magnetic survey data (land, sea, air, satellite, rocket) that is the primary source of data for geomagnetic main-field modeling). This is followed by a description of the various types of error to which these survey data are, or may be, subject and a discussion of the likely effects of such errors on field models produced from the data. Finally, there is a short section on the availability of geomagnetic survey data, which also describes how the data files are maintained.

Barraclough, D. R.↗

NASA NTRS2014

Analytical Assessment of Simultaneous Parallel Approach Feasibility from Total System Error

In a simultaneous paired approach to closely-spaced parallel runways, a pair of aircraft flies in close proximity on parallel approach paths. The aircraft pair must maintain a longitudinal separation within a range that avoids wake encounters and, if one of the aircraft blunders, avoids collision. Wake avoidance defines the rear gate of the longitudinal separation. The lead aircraft generates a wake vortex that, with the aid of crosswinds, can travel laterally onto the path of the trail aircraft. As runway separation decreases, the wake has less distance to traverse to reach the path of the trail aircraft. The total system error of each aircraft further reduces this distance. The total system error is often modeled as a probability distribution function. Therefore, Monte-Carlo simulations are a favored tool for assessing a "safe" rear-gate. However, safety for paired approaches typically requires that a catastrophic wake encounter be a rare one-in-a-billion event during normal operation. Using a Monte-Carlo simulation to assert this event rarity with confidence requires a massive number of runs. Such large runs do not lend themselves to rapid turn-around during the early stages of investigation when the goal is to eliminate the infeasible regions of the solution space and to perform trades among the independent variables in the operational concept. One can employ statistical analysis using simplified models more efficiently to narrow the solution space and identify promising trades for more in-depth investigation using Monte-Carlo simulations. These simple, analytical models not only have to address the uncertainty of the total system error but also the uncertainty in navigation sources used to alert an abort of the procedure. This paper presents a method for integrating total system error, procedure abort rates, avionics failures, and surveillance errors into a statistical analysis that identifies the likely feasible runway separations for simultaneous paired approaches.

Madden, Michael M.↗

NASA NTRS2006

Constraining Numerical Geodynamo Modeling with Surface Observations

Numerical dynamo solutions have traditionally been generated entirely by a set of self-consistent differential equations that govern the spatial-temporal variation of the magnetic field, velocity field and other fields related to dynamo processes. In particular, those solutions are obtained with parameters very different from those appropriate for the Earth s core. Geophysical application of the numerical results therefore depends on correct understanding of the differences (errors) between the model outputs and the true states (truth) in the outer core. Part of the truth can be observed at the surface in the form of poloidal magnetic field. To understand these differences, or errors, we generate new initial model state (analysis) by assimilating sequentially the model outputs with the surface geomagnetic observations using an optimal interpolation scheme. The time evolution of the core state is then controlled by our MoSST core dynamics model. The final outputs (forecasts) are then compared with the surface observations as a means to test the success of the assimilation. We use the surface geomagnetic data back to year 1900 for our studies, with 5-year forecast and 20-year analysis periods. We intend to use the result; to understand time variation of the errors with the assimilation sequences, and the impact of the assimilation on other unobservable quantities, such as the toroidal field and the fluid velocity in the core.

Kuang, Weijia↗

DOE OSTI2022

Forecasting distributed energy resources adoption for power systems

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity - the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗

DOE OSTI2026

Early calendar life and health prediction of silicon batteries via machine learning with uncertainty quantification

Lithium-ion batteries with silicon anodes promise high energy density but are limited by calendar lifetime. Reducing the long iteration time to obtain experimental results requires predicting calendar lifetime early in a cell's life. In this study, we demonstrate that lightweight machine learning models with feature engineering can provide calendar lifetime estimates from early electrochemical signals. After 1 month of electrochemical aging, the best models achieve 10% error in calendar-life prediction and can separate "bad" from "good" lifetime cells with a mean F1 score of 0.857. As battery systems exhibit inherent variability, four methods for uncertainty quantification are compared, and confidence intervals are demonstrated with an uncertainty of +-3.6 months in lifetime prediction. A feature importance analysis indicates that early patterns in voltage decay are the strongest indicators of calendar lifetime. Finally, this modeling approach has high error when generalizing to new electrode chemistries or testing conditions but with appropriately low confidence.

25 ENERGY STORAGE↗

DOE OSTI2020

Calibration, measurement, and characterization of soil moisture dynamics in a central Amazonian tropical forest

Soil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (K a ) to volumetric water content (θ v ). In this study, our objectives were to develop a field-based calibration of TDR sensors in an old-growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2 -m-deep vertical soil profile. Depth-specific TDR calibration using local soils in a controlled laboratory setting yielded a novel K a –θ v third-degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2-m pit. The widely used K a –θ v relationship (Topp model) underestimated the site-specific θ v by 22–42%, indicating significant error in the model when applied to these well-structured, clay-rich tropical forest soils. The calibrated wet- and dry-season θ v data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1984

The p-wave upper mantle structure beneath an active spreading centre - The Gulf of California

Over 1400 seismograms of earthquakes in Mexico are analyzed and data sets for the travel time, apparent phase velocity, and relative amplitude information are utilized to produce a tightly constrained, detailed model for depths to 900 km beneath an active oceanic ridge region, the Gulf of California. The data are combined by first inverting the travel times, perturbing that model to fit the p-delta data, and then performing trial and error synthetic seismogram modelling to fit the short-period waveforms. The final model satisfies all three data sets. The ridge model is similar to existing upper mantle models for shield, tectonic-continental, and arc-trench regimes below 400 km, but differs significantly in the upper 350 km. Ridge model velocities are very low in this depth range; the model 'catches up' with the others with a very large velocity gradient from 225 to 390 km.

Walck, M. C.↗