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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

Here, soil respiration (R s ), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SU-E-T-365: Estimation of Neutron Ambient Dose Equivalents for Radioprotection Exposed Workers in Radiotherapy Facilities Based On Characterization Patient Risk Estimation

Purpose: Previous measurements with Bonner spheres{sup 1} showed that normalized neutron spectra are equal for the majority of the existing linacs{sup 2}. This information, in addition to thermal neutron fluences obtained in the characterization procedure{sup 3}3, would allow to estimate neutron doses accidentally received by exposed workers, without the need of an extra experimental measurement. Methods: Monte Carlo (MC) simulations demonstrated that the thermal neutron fluence distribution inside the bunker is quite uniform, as a consequence of multiple scatter in the walls{sup 4}. Although inverse square law is approximately valid for the fast component, a more precise calculation could be obtained with a generic fast fluence distribution map around the linac, from MC simulations{sup 4}. Thus, measurements of thermal neutron fluences performed during the characterization procedure{sup 3}, together with a generic unitary spectra{sup 2}, would allow to estimate the total neutron fluences and H*(10) at any point{sup 5}. As an example, we compared estimations with Bonner sphere measurements{sup 1}, for two points in five facilities: 3 Siemens (15–23 MV), Elekta (15 MV) and Varian (15 MV). Results: Thermal neutron fluences obtained from characterization, are within (0.2–1.6×10{sup 6}) cm−{sup 2}•Gy{sup −1} for the five studied facilities. This implies ambient equivalent doses ranging from (0.27–2.01) mSv/Gy 50 cm far from the isocenter and (0.03–0.26) mSv/Gy at detector location with an average deviation of ±12.1% respect to Bonner measurements. Conclusion: The good results obtained demonstrate that neutron fluence and H*(10) can be estimated based on: (a) characterization procedure established for patient risk estimation in each facility, (b) generic unitary neutron spectrum and (c) generic MC map distribution of the fast component. [1] Radiat. Meas (2010) 45: 1391 – 1397; [2] Phys. Med. Biol (2012) 5 7:6167–6191; [3] Med. Phys (2015) 42:276 - 281. [4] IFMBE (2012) 39: 1245–1248. [5] ICRU Report 57 (1998)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Estimating Vadose Zone Flow Properties at the 100 K-East Soil Flushing Site Using ERT Monitoring Data: 2023 Interim Report - 100 KE Soil Property Estimation

In situ soil flushing is being using at the Hanford 100 K-East (100 KE) area to transport mobile chromium contamination in the vadose zone to the water table, where it can be collected and treat through pump and treat operations. The efficacy of soil flushing is directly related to the volume of clean water that infiltrates through contaminated soils. In practice, it is infeasible to comprehensively monitor which regions of the vadose zone are being infiltrated through direct sampling of pore water. Consequently, there can be significant uncertainty about which regions of the subsurface have been treated, especially if hydrogeologic conditions are favorable for the development of unstable flows and preferred flow pathways through the vadose zone (Jarvis, Koestel, and Larsbo 2016). Current approaches for quantitative monitoring of soil flushing performance rely on contaminant concentration measurements collected from extractions wells. There is no quantitative information on the volume of flush water delivered to targeted regions of the vadose zone at the Hanford Site, leading to significant uncertainty regarding source term removal and long-term impacts to groundwater. If the subsurface hydrogeologic properties at the 100 KE Area were adequately known, qualitative metrics of soil flushing performance could be simulated, thereby negating expenses required to obtain quantitative performance information through borehole drilling/sampling. However, estimating in situ hydrogeologic properties has long proven elusive, due primarily to a lack of sufficient information to constrain heterogeneous property estimates to a useful degree of certainty. Estimating vadose zone hydrogeologic properties is particularly challenging due the dependence of hydraulic conductivity on saturation. This report describes progress toward a first-of-its-kind demonstration using surface time-lapse 3D electrical resistivity tomography (ERT) monitoring data to estimate the hydrogeologic properties that control flush water transport at the 100 KE soil flushing site. The ultimate objective is (1) to verify sufficient information exists in the ERT monitoring data to adequately resolve vadose zone hydraulic properties, and (2) generate a “digital twin” (i.e., a numerical simulator) that can be used to simulate the amount of flush water that has been delivered to each targeted region of the vadose zone, and thereby assess the efficacy of flush water delivery. Resulting performance estimates can be used in leu of comprehensive borehole drilling and direct sampling (or wellbore logging) that would otherwise be required to obtain the same information.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1975

J-Adaptive estimation with estimated noise statistics

The J-Adaptive estimator described by Jazwinski and Hipkins (1972) is extended to include the simultaneous estimation of the statistics of the unmodeled system accelerations. With the aid of simulations it is demonstrated that the J-Adaptive estimator with estimated noise statistics can automatically estimate satellite orbits to an accuracy comparable with the data noise levels, when excellent, continuous tracking coverage is available. Such tracking coverage will be available from satellite-to-satellite tracking.

Jazwinski, A. H.↗
NASA NTRS2007

Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation

A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.

Rakoczy, John↗
NASA NTRS2008

Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation

A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.

Rakoczy, John M.↗
NASA NTRS2009

REFAME: Rain Estimation Using Forward Adjusted-Advection of Microwave Estimates

Sensors flying on satellites provide the only practical means of estimating the precipitation that falls over the entire globe, particularly across the vast unpopulated expanses of Earth s oceans. The sensors that observe the Earth using microwave frequencies provide the best data, but currently these are mounted only on satellites flying in "low Earth orbit". Such satellites constantly move across the Earth s surface, providing snapshots of any given location every 12-36 hours. The entire constellation of low-orbit satellites numbers less than a dozen, and their orbits are not coordinated, so a location will frequently go two or more hours between snapshots. "Geosynchronous Earth orbit" (GEO) satellites continuously observe the same region of the globe, allowing them to provide very frequent pictures. For example, the "satellite movies" shown on television come from GEO satellites. However, the sensors available on GEO satellites cannot match the skill of the low-orbit microwave sensors in estimating precipitation. It is perhaps obvious that scientists should try to combine these very different kinds of data, taking advantage of the strengths of each, but this simple concept has proved to be a huge challenge. The scheme in this paper is "Lagrangian", meaning we follow the storm systems, rather than being tied to a fixed grid of boxes on the Earth s surface. Whenever a microwave snapshot occurs, we gladly use the resulting precipitation estimate. Then at all the times between the microwave snapshots we force the storm system to make a smooth transition from one snapshot s values to the next. We know that a lot more changes occur between the snapshots, but this smooth transition the best we can do with the microwave data alone. The key new contribution in this paper is that we also look at the relative variations in the GEO estimates during these in-between times and force the estimated changes in the precipitation to have similar variations. Preliminary testing shows that this approach has enough promise that it should be developed and studied.

Behrangi, Ali↗
NASA NTRS2018

Dynamic Modeling using Output-Error Parameter Estimation based on Frequency Responses Estimated with Multisine Inputs

A method is developed for estimating model parameters, such as nondimensional stability and control derivatives, by fitting transfer function or state-space models to empirical frequency response data using the output-error approach. The frequency response data were computed using Fourier transforms of measured input and output data. The control surfaces were excited with periodic multisine inputs which facilitated time-ecient estimation of multiple-input multiple-output frequency responses. The method was applied to lateral data from a nonlinear flight dynamics simulation of the F-16 aircraft, and to longitudinal data from multiple repeated flight test maneuvers of the NASA T-2 subscale aircraft. Results using simulation data showed the frequency response method compared well to other standard methods for parameter estimation. In addition to including all the available inputs, outputs, and harmonic frequencies in the estimation, relatively small subsets of the measured data could also be used to focus on identifying specific parts of the model. Results from flight test data showed that parameter estimates and uncertainties determined from repeated maneuvers were accurate and in statistical agreement with each other.

Grauer, Jared A.↗
DOE OSTI2017

Monte Carlo Estimates of Eigenvalue Sensitivity to System Dimensions using Kernel Density Estimators

In the last decade there has been an increasing interest in calculating sensitivity coefficients in order to quantify the impact of uncertainty in material properties and system parameters on quantities of interest. Extensive work has been done developing Monte Carlo methods to estimate eigenvalue sensitivity to material properties. While deterministic methods exist for determining the eigenvalue sensitivity to system dimensions, the use of Monte Carlo methods to efficiently calculate eigenvalue sensitivity coefficients to system dimensions is still an open topic. Current Monte Carlo methods for calculating eigenvalue sensitivity coefficients to system dimensions are inefficient. Direct Monte Carlo perturbations require two additional simulations of high statistical precision in order to determine the eigenvalue sensitivity to the location of a single boundary. If the sensitivity to the location of multiple surfaces are desired, two additional simulations must be run for each surface. Kiedrowski and Favorite extended continuous energy Monte Carlo methods for sensitivities of nuclear cross sections to calculate sensitivities to interface boundaries or system dimensions to create the only current alternative to direct Monte Carlo calculations. However this method relies on the simulation of additional random walks launched at each surface crossing event. The present method extends upon Kiedrowski's work and eliminates the inefficient portions of the calculation through the use of Kernel Density Estimators (KDEs). The new method efficiently estimates eigenvalue sensitivities to the uniform expansion or contraction of material interface boundaries or system dimensions, negligibly impacting the runtime performance of the Monte Carlo code. The new estimator is tested on bare and reflected critical configurations in spherical geometry and is compared to estimates obtained from direct Monte Carlo perturbations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics

Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of $$C^{\infty }$$ C ∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Full-scale 3-D finite element modeling of a two-loop pressurized water reactor for heat transfer, thermal–mechanical cyclic stress analysis, and environmental fatigue life estimation

This paper discusses a system-level finite element model of a two-loop pressurized water reactor (PWR). Based on this model, system-level heat transfer analysis and subsequent sequentially coupled thermal-mechanical stress analysis were performed for typical thermal-mechanical fatigue cycles. The in-air fatigue lives of example components, such as the hot and cold legs, were estimated on the basis of stress analysis results, ASME in-air fatigue life estimation criteria, and fatigue design curves. Furthermore, environmental correction factors and associated PWR environment fatigue lives for the hot and cold legs were estimated by using estimated stress and strain histories and the approach described in US-NRC report: NUREG-6909.

Full-scale 3-D finite element model, Pressurized w↗
DOE OSTI2018

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR) for learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS- SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can be properly distinguished, thus a robust modeling result can be achieved. Finally, a novel multi- objective evaluation index on modeling performance is developed by comprehensively considering the root mean square error of modeling and the correlation coefficient on trend fitting, based on which the NSGA-II algorithm is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently, which indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

blast cavities, Control↗
DOE OSTI2016

Estimation of economic impacts of cellulosic biofuel production: a comparative analysis of three biofuel pathways: Economic impacts of biofuel production

The development of a cellulosic biofuel industry utilizing domestic biomass resources is expected to create opportunities for economic growth resulting from the construction and operation of new biorefineries. We applied an economic input-output model to estimate potential economic impacts, particularly gross job growth, resulting from the construction and operation of biorefineries using three different technology pathways: 1) cellulosic ethanol via biochemical conversion in Iowa, 2) renewable diesel blendstock via biological conversion in Georgia, and 3) renewable diesel and gasoline blendstock via fast pyrolysis in Mississippi. Combining direct, indirect, and induced effects, capital investment associated with the construction of a biorefinery processing 2,000 dry metric tons of biomass per day (DMT/day) could yield between 5,960 and 8,470 full-time equivalent (FTE) jobs during the construction period. Fast pyrolysis biorefineries produce the most jobs on a project level thanks to the highest capital requirement among the three pathways. Normalized for one million dollars of capital investment, the fast pyrolysis biorefineries are estimated to yield slighter more jobs (12.1 jobs) than the renewable diesel (11.8 jobs) and the cellulosic ethanol (11.6 jobs) biorefineries. While operating biorefineries is not labor-intensive, the annual operation of a 2,000 DMT/day biorefinery could support between 720 and 970 jobs when the direct, indirect, and induced effects are considered. The major factor, which results in the variations among the three pathways, is the type of biomass feedstock used for biofuels. The agriculture/forest, services, and trade industries are the primary sectors that will benefit from the ongoing operation of biorefineries.

Biofuels↗