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Walker, David

Publications and source records attributed to Walker, David.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

SI-Traceable Calibration of Satellite Microwave Radiometers

Background (what): SI (International System of Units)-traceable Microwave Radiometer calibration; Motivation (why): NWP (Numerical Weather Prediction), FCDR (Fundamental Climate Data Record); Technology (how): NIST (National Inst. of Standards and Technology) blackbody target for ; Standards: Status & Future Plans. Development of a National Standard for Microwave Brightness Temperature (TB) at NIST.

MW Radiometer TB Calibration↗
DOE OSTI2019

Mn 2 CoReO 6 : a robust multisublattice antiferromagnetic perovskite with small A-site cations

Mn 2 CoReO 6 , the fourth known magnetic transition-metal-only double perovskite oxide (space group P2 1 /n) was synthesized here at high pressure and temperature (8 GPa, 1350 °C). Large structural distortions are induced by the small A-site Mn 2+ cations. Mn 2 CoReO 6 exhibits complex magnetic properties with a robust antiferromagnetic order (T N = 94 K) involving all cation sublattices.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Assimilation of Wave Imaging Radar Observations for Real-time Wave-by-Wave Forecasting

This project addressed Topic 3: “Wave Measurement Instrumentation for Feed Forward Controls” under the FOA number DE-FOA-0000971. The overall goal of the program was to develop a phase-resolving wave forecasting technique for application to the active control of Wave Energy Conversion (WEC) devices. We have developed an approach that couples a wave imaging marine radar with a phase-resolving linear wave model for real-time wave field reconstruction and forward propagation of the wave field in space and time. The scope of the project was to develop and assess the performance of this novel forecasting system. Specific project goals were as follows: Develop and verify a fast, GPU-based (Graphical Processing Unit) wave propagation model suitable for phase-resolved computation of nearshore wave transformation over variable bathymetry; Compare the accuracy and speed of performance of the wave model against a deep water model in their ability to predict wave field transformation in the intermediate water depths (50 to 70 m) typical of planned WEC sites; Develop and implement a variational assimilation algorithm that can ingest wave imaging radar observations and estimate the time-varying wave conditions offshore of the domain of interest such that the observed wave field is best reconstructed throughout the domain and then use this to produce model forecasts for a given WEC location; Collect wave-resolving marine radar data, along with relevant in situ wave data, at a suitable wave energy test site, apply the algorithm to the field data, assess performance, and identify any necessary improvements; and Develop a production cost estimate that addresses the affordability of the wave forecasting technology and include in the Final Report. The developed forecasting algorithm (“Wavecast”) was evaluated for both speed and accuracy against a substantial synthetic dataset. Early in the project, performance tests definitively demonstrated that the system was capable of forecasting in real-time, as the GPU-based wave model backbone was very computationally efficient. The data assimilation algorithm was developed on a polar grid domain in order to match the sampling characteristics of the observation system (wave imaging marine radar). For verification purposes, a substantial set of synthetic wave data (i.e. forward runs of the wave model) were generated to be used as ground truth for comparison to the reconstructions and forecasts produced by Wavecast. For these synthetic cases, Wavecast demonstrated very good accuracy, for example, typical forecast correlation coefficients were between 0.84-0.95 when compared to the input data. Dependencies on shadowing, observational noise, and forecast horizon were also identified. During the second year of the project, a short field deployment was conducted in order to assess forecast accuracy under field conditions. For this, a radar was installed on a fishing vessel and observations were collected at the South Energy Test Site (SETS) off the coast of Newport, OR. At the SETS site, simultaneous in situ wave observations were also available owing to an ongoing field project funded separately. Unfortunately, the position and heading information that was available for the fishing vessel were not of sufficient accuracy in order to validate the forecast in a phase-resolving sense. Instead, a spectral comparison was made between the Wavecast forecast and the data from the in situ wave buoy. Although the wave and wind conditions during the field test were complex, the comparison showed a promising reconstruction of the wave spectral shape, where both peaks in the bimodal spectrum were represented. However, the total reconstructed spectral energy (across all directions and frequencies) was limited to 44% of the observed spectrum. Overall, wave-by-wave forecasting using a data assimilation approach based on wave imaging radar observations and a physics-based wave model shows promise for short-term phase-resolved predictions. Two recommendations for future work are as follows: first, we would recommend additional focused field campaigns for algorithm validation. The field campaign should be long enough to capture a range of wave conditions relevant to the target application and WEC site. In addition, it will be crucial to make sure the vessel of choice has high accuracy position and heading instrumentation (this instrumentation is commercially available but not standard on commercial fishing vessels). The second recommendation is to expand the model physics in the wave model backbone to include some nonlinear effects. Specifically, the third-order correction to the wave speed due to amplitude dispersion would be the next step in order to more accurately represent the phase speeds of large amplitude waves.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Ba 3 (Cr 0.97(1) Te 0.03(1) ) 2 TeO 9 : in Search of Jahn–Teller Distorted Cr(II) Oxide

A novel 6H - type hexagonal perovskite Ba 3 (Cr 0.97(1) Te 0.03 (1 ) ) 2 TeO 9 was prepared at high pressure (6 GPa) and temperature ( 1 773 K). Both transmission electron microscopy and synchrotron powder x - ray diffraction data demonstrate that Ba 3 (Cr 0.97(1) Te 0.03(1) ) 2 TeO 9 crystallize s in P6 3 / mmc with face - shared (Cr 0.97(1) Te 0.03(1) )O 6 octahedral pairs interconnected with TeO 6 octahedra via corner - sharing. Structure analysis shows a mixed Cr 2+ /Cr 3+ valence state with ~ 10% Cr 2+ . The existence of Cr 2+ in Ba 3 (Cr 2+ 0.10(1) Cr 3+ 0.87(1) Te 6+ 0.03 ) 2 TeO 9 is further evidenced by x - ray absorption near edge spectr oscopy . Magnetic properties measurements show a paramagnetic response down to 4 K and a small glassy - state curvature at low temperature. In this work, the o ctahedral Cr 2+ O 6 component is stabilized in an oxide material for the first time ; the expected Jahn - Teller distortion of high - spin ( d 4 ) Cr 2+ is not found , which is attributed to the small proportion of Cr 2+ (~ 10%) and the face - sharing arrangement of CrO 6 octahedral pairs, that structu rally dis favor axial distortion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Size dependent compressibility of nano-ceria: Minimum near 33 nm

We report the crystallite-size-dependency of the compressibility of nanoceria under hydrostatic pressure for a wide variety of crystallite diameters and comment on the size-based trends indicating an extremum near 33 nm. Uniform nano-crystals of ceria were synthesized by basic precipitation from cerium (III) nitrate. Size-control was achieved by adjusting mixing time and, for larger particles, a subsequent annealing temperature. The nano-crystals were characterized by transmission electron microscopy and standard ambient x-ray diffraction (XRD). Compressibility, or its reciprocal, bulk modulus, was measured with high-pressure XRD at LBL-ALS, using helium, neon, or argon as the pressure-transmitting medium for all samples. As crystallite size decreased below 100 nm, the bulk modulus first increased, and then decreased, achieving a maximum near a crystallite diameter of 33 nm. We review earlier work and examine several possible explanations for the peaking of bulk modulus at an intermediate crystallite size.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2011

Receiver Gain Modulation Circuit

A receiver gain modulation circuit (RGMC) was developed that modulates the power gain of the output of a radiometer receiver with a test signal. As the radiometer receiver switches between calibration noise references, the test signal is mixed with the calibrated noise and thus produces an ensemble set of measurements from which ensemble statistical analysis can be used to extract statistical information about the test signal. The RGMC is an enabling technology of the ensemble detector. As a key component for achieving ensemble detection and analysis, the RGMC has broad aeronautical and space applications. The RGMC can be used to test and develop new calibration algorithms, for example, to detect gain anomalies, and/or correct for slow drifts that affect climate-quality measurements over an accelerated time scale. A generalized approach to analyzing radiometer system designs yields a mathematical treatment of noise reference measurements in calibration algorithms. By treating the measurements from the different noise references as ensemble samples of the receiver state, i.e. receiver gain, a quantitative description of the non-stationary properties of the underlying receiver fluctuations can be derived. Excellent agreement has been obtained between model calculations and radiometric measurements. The mathematical formulation is equivalent to modulating the gain of a stable receiver with an externally generated signal and is the basis for ensemble detection and analysis (EDA). The concept of generating ensemble data sets using an ensemble detector is similar to the ensemble data sets generated as part of ensemble empirical mode decomposition (EEMD) with exception of a key distinguishing factor. EEMD adds noise to the signal under study whereas EDA mixes the signal with calibrated noise. It is mixing with calibrated noise that permits the measurement of temporal-functional variability of uncertainty in the underlying process. The RGMC permits the evaluation of EDA by modulating the receiver gain using an external signal. Without the RGMC, samples of calibrated references from radiometers form an ensemble data set of the natural occurring fluctuations within a receiver. By driving the gain of an otherwise stable receiver with an external signal, the conceptual framework and generalization of the mathematics of EDA can be tested. A series of measurements was conducted to evaluate and characterize the performance of the RGMC. Test signals stepped the RGMC across its dynamic range of performance using a radiometer that sampled four noise references; analysis indicates that the RGMC successfully modulated the receiver gain with an external signal. Calibration algorithms applied to four noise references demonstrate the RGMC produced ensemble data sets of the external signal.

Jones, Hollis↗
NASA NTRS2011

Calibrated Noise Measurements with Induced Receiver Gain Fluctuations

The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory in science and engineering. These limitations were encountered when modeling the performance of radiometer calibration architectures and algorithms in the presence of non stationary receiver fluctuations. Analyses of measured signals have traditionally been limited to a single measurement series. Whereas in a radiometer that samples a set of noise references, the data collection can be treated as an ensemble set of measurements of the receiver state. Noise Assisted Data Analysis is a growing field of study with significant potential for aiding the understanding and modeling of non stationary processes. Typically, NADA entails adding noise to a signal to produce an ensemble set on which statistical analysis is performed. Alternatively as in radiometric measurements, mixing a signal with calibrated noise provides, through the calibration process, the means to detect deviations from the stationary assumption and thereby a measurement tool to characterize the signal's non stationary properties. Data sets comprised of calibrated noise measurements have been limited to those collected with naturally occurring fluctuations in the radiometer receiver. To examine the application of NADA using calibrated noise, a Receiver Gain Modulation Circuit (RGMC) was designed and built to modulate the gain of a radiometer receiver using an external signal. In 2010, an RGMC was installed and operated at the National Institute of Standards and Techniques (NIST) using their Noise Figure Radiometer (NFRad) and national standard noise references. The data collected is the first known set of calibrated noise measurements from a receiver with an externally modulated gain. As an initial step, sinusoidal and step-function signals were used to modulate the receiver gain, to evaluate the circuit characteristics and to study the performance of a variety of calibration algorithms. The receiver noise temperature and time-bandwidth product of the NFRad are calculated from the data. Statistical analysis using temporal-dependent calibration algorithms reveals that the natural occurring fluctuations in the receiver are stationary over long intervals (100s of seconds); however the receiver exhibits local non stationarity over the interval over which one set of reference measurements are collected. A variety of calibration algorithms have been applied to the data to assess algorithms' performance with the gain fluctuation signals. This presentation will describe the RGMC, experiment design and a comparative analysis of calibration algorithms.

Racette, Paul↗
NASA NTRS2009

ARTEMIS: Ares Real Time Environments for Modeling, Integration, and Simulation

This slide presentation reviews the use of ARTEMIS in the development and testing of the ARES launch vehicles. Ares Real Time Environment for Modeling, Simulation and Integration (ARTEMIS) is the real time simulation supporting Ares I hardware-in-the-loop (HWIL) testing. ARTEMIS accurately models all Ares/Orion/Ground subsystems which interact with Ares avionics components from pre-launch through orbit insertion The ARTEMIS System integration Lab, and the STIF architecture is reviewed. The functional components of ARTEMIS are outlined. An overview of the models and a block diagram is presented.

Hughes, Ryan↗
NASA NTRS2009

Real-Time Simulation of Ares I Launch Vehicle

The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory (SIL) at the Marshall Space Flight Center (MSFC). The primary purpose of the Ares SIL is to test the vehicle avionics hardware and software in a hardware-in-the-loop (HWIL) environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time software backbone to stimulate all required Ares components through high-fidelity simulation. ARTEMIS has been designed to take full advantage of the advances in underlying computational power now available to support HWIL testing. A modular real-time design relying on a fully distributed computing architecture has been achieved. Two fundamental requirements drove ARTEMIS to pursue the use of high-fidelity simulation models in a real-time environment. First, ARTEMIS must be used to test a man-rated integrated avionics hardware and software system, thus requiring a wide variety of nominal and off-nominal simulation capabilities to certify system robustness. The second driving requirement - derived from a nationwide review of current state-of-the-art HWIL facilities - was that preserving digital model fidelity significantly reduced overall vehicle lifecycle cost by reducing testing time for certification runs and increasing flight tempo through an expanded operational envelope. These two driving requirements necessitated the use of high-fidelity models throughout the ARTEMIS simulation. The nature of the Ares mission profile imposed a variety of additional requirements on the ARTEMIS simulation. The Ares I vehicle is composed of multiple elements, including the First Stage Solid Rocket Booster (SRB), the Upper Stage powered by the J- 2X engine, the Orion Crew Exploration Vehicle (CEV) which houses the crew, the Launch Abort System (LAS), and various secondary elements that separate from the vehicle. At launch, the integrated vehicle stack is composed of these stages, and throughout the mission, various elements separate from the integrated stack and tumble back towards the earth. ARTEMIS must be capable of simulating the integrated stack through the flight as well as propagating each individual element after separation. In addition, abort sequences can lead to other unique configurations of the integrated stack as the timing and sequence of the stage separations are altered.

Tobbe, Patrick↗
NASA NTRS2009

Real-Time Hardware-in-the-Loop Simulation of Ares I Launch Vehicle

The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory at the Marshall Space Flight Center. The primary purpose of the Ares System Integration Laboratory is to test the vehicle avionics hardware and software in a hardware - in-the-loop environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time simulation backbone to stimulate all required Ares components for verification testing. ARTE_VIIS provides high -fidelity dynamics, actuator, and sensor models to simulate an accurate flight trajectory in order to ensure realistic test conditions. ARTEMIS has been designed to take advantage of the advances in underlying computational power now available to support hardware-in-the-loop testing to achieve real-time simulation with unprecedented model fidelity. A modular realtime design relying on a fully distributed computing architecture has been implemented.

Tobbe, Patrick↗
NASA NTRS1993

Ureilite smelting

Ureilites containing homogeneous Fo76 olivine cores in intimate co-existence with graphite must have recrystallized at pressures of at least approximately 100 bars to suppress smelting of the fayalite component of the olivine to Fe metal. Smelting of olivine and pyroxene-saturated magmatic liquids produces orthopyroxene-without-olivine crystalline derivatives unlike those in ureilites. Thus the Mg# compositional variation within the ureilite suite, which is commonly attributed to partial smelting, cannot plausibly be produced by assemblages rich in liquid. In situ smelting of graphitic olivine + pigeonite crystal mushes can produce the correct crystal assemblage, but fails to provide a plausible account for the removal of metal from ureilites or for the correlation of Mg# with Delta O-17. Even if Mg# and Delta O-17 variations are established in the nebula, ureilite recrystallization with graphite must have occurred at pressures greater than the minima we have experimentally established, corresponding to parent objects not less than approximately 100 km in radius.

Walker, David↗
NASA NTRS1990

Operations influence on Space Station Freedom

The Space Station Freedom is the first experience for NASA and its international partners in building a spacecraft that will be operated and maintained over a multi-year lifetime and that will be permanently manned. To ensure it functions safely and effectively, the operations community has become involved much earlier than in previous U.S. programs. This involvement has taken the form of defining requirements and analyzing the configuration, systems, and elements in terms of operations suitability and supportability. This early participation has had many effects on the definition and design of the Freedom Station. Examples of operations participation include the development of various operations concepts, design reference missions, contingency operations scenarios, and assembly operations assessments.

Accola, Anne L.↗