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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Design and Measurement of a Low-Noise 64-Channels Front-End Readout ASIC for CdZnTe Detectors

Cadmium zinc telluride (CdZnTe) detectors, as one of the principal detectors for the next-generation X-ray and γ-ray imagers, have high energy resolution and supporting electrode patterning in the radiation environment at room-temperature. In the present, a number of internationally renowned research institutions and universities are actively using these detector systems to carry out researches of energy spectrum analysis, medical imaging, materials characterization, high-energy physics, nuclear plant monitoring, and astrophysics. As the most important part of the readout system for the CdZnTe detector, the front-end readout application specific integrated circuit (ASIC) would have an important impact on the performances of the whole detector system. In order to ensure the small signal to noise ratio (SNR) and sufficient range of the output signal, it is necessary to design a front-end readout ASIC with very low noise and very high dynamic range. In addition, radiation hardness should be considered when the detectors are utilized in the space applications and high energy physics experiments. In this paper, we present measurements and performances of a novel multi-channel radiation-hardness low-noise front-end readout ASIC for CdZnTe detectors. The readout circuits in each channel consist of charge sensitive amplifier, leakage current compensation circuit (LCC), CR-RC shaper, S-K filter, inverse proportional amplifier, peak detect and hold circuit (PDH), discriminator and trigger logic, time sequence control circuit and driving buffer. All of 64 readout channels' outputs enter corresponding inputs of a 64 channel multiplexer. The output of the mux goes directly out of the chip via the output buffer. The 64-channel readout ASIC is implemented using the TSMC 0.35 μm mixed-signal CMOS technology. The die size of the prototype chip is 2.7 mm x 8 mm. At room temperature, the equivalent noise level of a typical channel reaches 66 e{sup -} (rms) at zero farad for a power consumption of 8 mW per channel. The linearity error is lower than 1% and the overall gain of the readout channel is 165 V/pC. The crosstalk between the channels is less than 3%. By connecting the readout ASIC to a CdZnTe detector, we obtained a γ-ray spectrum, the energy resolution is 5.1% at the 59.5-keV line of {sup 241}Am source. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Final Technical Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)

QUEST is a SciDAC Institute comprising Sandia National Laboratories, Los Alamos National Laboratory, University of Southern California, Massachusetts Institute of Technology, University of Texas at Austin, and Duke University. The mission of QUEST is to: (1) develop a broad class of uncertainty quantification (UQ) methods/tools, and (2) provide UQ expertise and software to other SciDAC projects, thereby enabling/guiding their UQ activities. The Duke effort focused on the development of algorithms and utility software for non-intrusive sparse UQ representations, and on participation in the organization of annual workshops and tutorials to disseminate UQ tools to the community, and to gather input in order to adapt approaches to the needs of SciDAC customers. In particular, fundamental developments were made in (a) multiscale stochastic preconditioners, (b) gradient-based approaches to inverse problems, (c) adaptive pseudo-spectral approximations, (d) stochastic limit cycles, and (e) sensitivity analysis tools for noisy systems. In addition, large-scale demonstrations were performed, namely in the context of ocean general circulation models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Insensitive control technology development

THe investigation of two insensitive controller synthesis techniques was reported. The finite dimensional inverse approach produces a time varying insensitive controller and/or parameter identifier by constructing inverse functions derived from a finite number of input output pair relationships. The MD/IM concept relies on the information matrix theory that was developed in the estimation and identification field. The MD/IM synthesis technique is based on the hypothesis that minimizing the information matrix will reduce system identifiability and consequently system sensitivity to uncertain parameters. The controllers designed with both techniques were evaluated on a realistic C-5A aircraft flight control problem. Results indicate that the FDI controller is more suited to trajectory type problems because of its time varying nature. The MD/IM controller performed as well as the top-rated controllers of the initial effort and has direct application to aircraft flight control problems.

Harvey, C. A.↗
NASA NTRS2018

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Petty, Alek A.↗
NASA NTRS2019

Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Folberth, Christian↗
DOE OSTI2014

A microwave resonator for limiting depth sensitivity for electron paramagnetic resonance spectroscopy of surfaces

A microwave Surface Resonator Array (SRA) structure is described for use in Electron Paramagnetic Resonance (EPR) spectroscopy. The SRA has a series of anti-parallel transmission line modes that provides a region of sensitivity equal to the cross-sectional area times its depth sensitivity, which is approximately half the distance between the transmission line centers. It is shown that the quarter-wave twin-lead transmission line can be a useful element for design of microwave resonators at frequencies as high as 10 GHz. The SRA geometry is presented as a novel resonator for use in surface spectroscopy where the region of interest is either surrounded by lossy material, or the spectroscopist wishes to minimize signal from surrounding materials. One such application is in vivo spectroscopy of human finger-nails at X-band (9.5 GHz) to measure ionizing radiation dosages. In order to reduce losses associated with tissues beneath the nail that yield no EPR signal, the SRA structure is designed to limit depth sensitivity to the thickness of the fingernail. Another application, due to the resonator geometry and limited depth penetration, is surface spectroscopy in coating or material science. To test this application, a spectrum of 1.44 μM of Mg{sup 2+} doped polystyrene 1.1 mm thick on an aluminum surface is obtained. Modeling, design, and simulations were performed using Wolfram Mathematica (Champaign, IL; v. 9.0) and Ansys High Frequency Structure Simulator (HFSS; Canonsburg, PA; v. 15.0). A micro-strip coupling circuit is designed to suppress unwanted modes and provide a balanced impedance transformation to a 50 Ω coaxial input. Agreement between simulated and experimental results is shown.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

20 Channel Quench Detector

A multi-channel quench detector is useful for troubleshooting of prototype HTC superconducting magnets with current lead connections and imbedded splices. There are commercially available quench detectors, but not with as many channels, or with the particular functionality of this unit. This unit was designed specifically for cold testing of a superconducting sextupole prototype, which is wound with many turns of HTC ribbon. Associated with the current lead connections and the imbedded splices are 18 voltage taps, thus 17 quench detection channels are required to monitor the condition of the magnet. Of the 20 differential input channels in this quench detection unit, 19 are configured in a ladder network. The 20th channel is isolated and can be connected across any pair of voltage taps. This quench detector is designed to be used to monitor the condition of a series-connected superconducting magnet powered by a unipolar supply, and therefore the polarity and sequence of the voltage taps must be observed. There are 20 toggle switches and 20 red LEDs on the front panel of the unit which are associated with the 20 quench detection channels. There are also 2 green LEDs to indicate the state of the +5 V and +25 V internal supplies. Channel input impedance is ~10 kΩ. The input lines are diode protected against negative voltage transients and can tolerate up to +25 V, which is greater than the compliance of supplies typically used to power superconducting magnets. The front panel switches are used to enable or disable individual channels. If a channel is not used it is disabled. There are 20 pots on the quench detector PCB. They provide adjustment for the sensitivity of each of the 20 channels. There is an onboard reference to facilitate channel sensitivity calibration. The detection sensitivity range is from ~27 mV to ~700 mV. Typically all channels are set for the same sensitivity. The response time is ~20 μs, but the time constant can be increased if desired by adding a single capacitor to each channel. When the magnet under test is cooled to the superconducting state and the supply current to the windings is slowly ramped up to the set point, if there is a voltage rise across a pair of voltage taps which exceeds the quench detection limit of a given channel, then the front panel LED for that channel illuminates and all other channels are disabled within ~25 ns. Thus only the first quench is detected for the channels which are enabled. A pushbutton switch on the front panel is used to reset a quench detection. A 25 pin D-subminiature connector on the back of the unit is used for the channel inputs. TTL level ‘quench’ and ‘quench-not’ signals, and DPDT relay contacts, are provided through a 9 pin D-subminiature connector on the back to indicate a detected quench condition, which could be used to disable the power supply and engage an energy absorber.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗
DOE OSTI2021

Urca Cooling in Neutron Star Crusts and Oceans: Effects of Nuclear Excitations

We report the excited-state structure of atomic nuclei can modify nuclear processes in stellar environments. In this Letter, we study the influence of nuclear excitations on Urca cooling (repeated back-and-forth β decay and electron capture in a pair of nuclear isotopes) in the crust and ocean of neutron stars. We provide for the first time an expression for Urca process neutrino luminosity which accounts for a thermal Boltzmann distribution of excited states in both members of an Urca pair. We use our new formula with state-of-the-art nuclear structure inputs to compute neutrino luminosities of candidate Urca cooling pairs. Our nuclear inputs consist of the latest experimental data supplemented with calculations using the projected shell model. We show that, in contrast to previous results that only consider the ground states of both nuclei in the pair, our calculated neutrino luminosities for different Urca pairs vary sensitively with the environment temperature and can be radically different from those obtained in the one-transition approximation. We find that nuclear excitations can lead to an enhancement in total Urca neutrino luminosities in the accreted neutron star crust by about 5 times as compared with the previous Urca results, which is expected to cause significant observational effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

SU-E-I-30: Image Analysis in Ultrasonography for Diagnosis of Sjoegren's Syndrome Using Dual-Tree Complex Wavelet Transform

Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosed with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of MCNP, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) confirm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results analysis methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exemplified through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) confirm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exemplified through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) con firm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exempli fied through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Hybrid Hydraulic-Electric Architecture (HHEA) for Mobile Machines (Final Report)

Traditionally, off-road mobile machines such as excavators and wheel loaders are primarily powered by hydraulics, and throttling valves are used to control their work circuits. In recent years, two general trends are towards more energy efficient systems and electrification. With electrification, both efficiency and control performance can be improved by the elimination of throttling losses and the use of high-bandwidth inverter control. Electrification is generally accomplished with Electro-hydraulic actuators (EHA) but they are limited to lower powered systems due to the high cost of electric machines capable of high power or high torque. This project proposes a new system architecture for off-road vehicles - Hybrid Hydraulic Electric Architecture (HHEA) to improve efficiency and control performance without requiring large electric machines. The widely applicable architecture combines hydraulic power and electric power in such a way that the majority of power is provided hydraulically while electric drives are used to modulate this power. In particular, HHEA utilizes multiple common pressure rails to transmit the majority of power and small electric machines to modulate the power. The energy-saving potential of the the HHEA has been validated for the work circuits of a variety of mobile machines, from small 5-ton excavators to medium sized 20-ton excavators and wheel loaders, and representative duty cycles to reduce energy input by 50-80% compared to the commercial state-of-art load-sensing systems. In addition, the corner power requirements of the electrical machines can be downsized by 85% compared to the EHA approach. Various tradeoff studies have also been conducted, including sensitivities to individual components performances, controllers, accumulator sizes, and variations of the system architecture etc. A control strategy has been developed to maintain or exceed the motion control precision compared to current systems. The motion control strategy consists of a nominal controller, based on a passivity-based backstepping design, and a transition controller, based on least-norn feedforward design. The nominal controller is used in between common pressure rail switches whereas the transition controller compensates for any disturbance that common pressure rail switchings inflict on the system. The control strategy has been experimentally validated on both a medium pressure (200bar) hardware-in-the-loop (HIL) testbed and a high pressure (300+bar) HIL testbed. An efficient and power-dense integrated electric-hydraulic machine consisting of an axial flux electric machine and a radial hydrostatic piston hydraulic machine has been designed, constructed and tested. The machine has an active material power density of 6.1kW/kg, a rated speed of 12500 RPM, and a design efficiency of 85%. This is among the highest power density electric machines using conventional materials. While the integrated machine was designed for modulating the hydraulic power within the HHEA, it can also be used in other applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Validation of SAS4A/SASSYS-1 for Steady-State Single-Phase Natural Circulation

The development of numerical software for engineering applications requires the validation of the code against experimental benchmark datasets. To support the validation of SAS4A/SASSYS-1 in simulating the single-phase natural circulation within nuclear reactor systems, high-precision steady-state experiments are performed to capture single-phase natural circulation phenomena on an existing scaled facility with comprehensive instrumentation. Forced convection tests are designed precisely capturing the facility’s critical thermal-hydraulic parameters required for one-dimensional modeling, and a comprehensive single-phase natural circulation dataset is obtained with well-documented experimental uncertainty and facility description. Analysis and discussion based on the finalized dataset confirm the dataset’s ability in capturing dominant phenomena and address important physical interpretation of parameters. The dataset reported in this project provides a valuable benchmark resource for the validation of system analysis codes under single-phase natural circulation inside nuclear reactors. Validation of SAS4A/SASSYS-1 is then performed against the obtained benchmark dataset to confirm the capability of its physics model in simulating the steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators to quantify and restrict numerical errors from discretization. Numerical uncertainty originating from finite maximum pseudo-transient time is also quantified and restricted. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, directly quantifying the simulation output in a Monte Carlo approach. The composition of the output uncertainty is further determined by the estimators of Sobol’ indices through a variance-based sensitivity analysis. With uncertainty quantified for each individual condition, detailed comparison between the simulation results and experimental data is performed covering the whole dataset, which shows consistent agreement for all important quantities. The validation activity reported in this project demonstrates that SAS4A/SASSYS-1 can predict the primary parameters with satisfactorily accuracy under steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Multi-Fidelity Active Subspaces for Wind Farm Uncertainty Quantification

Wind plants operate in stochastic environments characterized by complex turbulent flow dynamics and high-dimensional random variables. A key step in uncertainty quantification studies is sensitivity analysis and dimension reduction that can facilitate the development of surrogate models to be used for forward and inverse propagation or optimization under uncertainty. Prior work has shown active subspaces are an effective tool for identifying important directions in the space of stochastic inputs; however, they have only been applied to single-fidelity wind plant models. In this study, we investigate the efficacy of a multi-fidelity active subspace method for analyzing the uncertainty in wind plant power output. The multi-fidelity active subspace estimator offers the promise of increased accuracy in identifying active subspaces as compared to a single-fidelity estimator for the same computational cost, or a reduction in cost for the same accuracy. This makes the study of uncertainty in larger wind plants and with higher fidelity physics tractable. The multi-fidelity active subspace method is applied to gridded and existing wind plant layouts with single and multiple inflow conditions and its performance for surrogate modeling and uncertainty propagation is compared against a single-fidelity active subspace method. This multi-fidelity approach yields substantial computational speedups of 2x - 3.4x across the test cases along with acceptable accuracy in surrogate modeling and computing statistical moments.

active subspace↗
NASA NTRS1980

Microwave remote sensing of soil moisture content over bare and vegetated fields

The author has identified the following significant results. Ground truth of soil moisture content, and ambient air and soil temperatures were acquired concurrently with measurements of soil moisture in bare fields and fields covered with grass, corn, and soybeans obtained with 1.4 GHz and 5 GHz radiometers mounted on a truck. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over the bare fields were compared with those of radiative transfer model calculations using as inputs the acquired soil moisture and temperatures data with appropriate values of dielectric constants for soil-water mixtures. A good agreement was found between the calculated and measured results over 10 deg to 70 deg incident angles. The presence of vegetation reduced the sensitivity of soil moisture sensing. At 1.4 GHz the sensitivity reduction ranged from about 20% for 10 cm tall grassland cover to over 50 to 60% for the dense soybean field. At 5 GHz corresponding reduction in sensitivity ranged from approximately 70% to approximately 90%.

Wang, J. R.↗
NASA NTRS1980

Solar energy system economic evaluation for IBM System 3, Glendo, Wyoming

This analysis was based on the technical and economic models in f-chart design procedures with inputs based on the characteristics of the parameters of present worth of system cost over a projected twenty year life: life cycle savings, year of positive savings, and year of payback for the optimized solar energy system at each of the analysis sites. The sensitivity of the economic evaluation to uncertainties in constituent system and economic variables was also investigated.

Source record↗