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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
NASA NTRS1991

A sequential approach to multivariable stability robustness analysis

In sequential loop closure, the importance of evaluating the stability and stability robustness at the intermediate loop closures is well known, yet how the stability and stability robustness evaluated at the intermediate steps contribute to the stability and stability robustness of the overall feedback system must be developed. An analysis of the complete feedback system reveals the multivariable Nyquist contributions from the intermediate loop closures. It is also shown that the results greatly simplify if frequency separation exists between the intermediate loops. The analysis is presented with a two-step loop closure procedure using 'inner' and 'outer' loops which can be generalized to multi-step situations. The control of the longitudinal dynamics of an aircraft is addressed to further clarify and demonstrate the results.

Newman, Brett↗
DOE OSTI2015

Efficient and robust analysis of complex scattering data under noise in microwave resonators

Superconducting microwave resonators are reliable circuits widely used for detection and as test devices for material research. A reliable determination of their external and internal quality factors is crucial for many modern applications, which either require fast measurements or operate in the single photon regime with small signal to noise ratios. Here, we use the circle fit technique with diameter correction and provide a step by step guide for implementing an algorithm for robust fitting and calibration of complex resonator scattering data in the presence of noise. The speedup and robustness of the analysis are achieved by employing an algebraic rather than an iterative fit technique for the resonance circle.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SU-F-BRD-04: Robustness Analysis of Proton Breast Treatments Using An Alpha-Stable Distribution Parameterization

Purpose: Currently, planning systems allow robustness calculations to be performed, but a generalized assessment methodology is not yet available. We introduce and evaluate a methodology to quantify the robustness of a plan on an individual patient basis. Methods: We introduce the notion of characterizing a treatment instance (i.e. one single fraction delivery) by describing the dose distribution within an organ as an alpha-stable distribution. The parameters of the distribution (shape(α), scale(γ), position(δ), and symmetry(β)), will vary continuously (in a mathematical sense) as the distributions change with the different positions. The rate of change of the parameters provides a measure of the robustness of the treatment. The methodology is tested in a planning study of 25 patients with known residual errors at each fraction. Each patient was planned using Eclipse with an IBA-proton beam model. The residual error space for every patient was sampled 30 times, yielding 31 treatment plans for each patient and dose distributions in 5 organs. The parameters’ change rate as a function of Euclidean distance from the original plan was analyzed. Results: More than 1,000 dose distributions were analyzed. For 4 of the 25 patients the change in scale rate (γ) was considerably higher than the lowest change rate, indicating a lack of robustness. The sign of the shape change rate (α) also seemed indicative but the experiment lacked the power to prove significance. Conclusion: There are indications that this robustness measure is a valuable tool to allow a more patient individualized approach to the determination of margins. In a further study we will also evaluate this robustness measure using photon treatments, and evaluate the impact of using breath hold techniques, and the a Monte Carlo based dose deposition calculation. A principle component analysis is also planned.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Real-Time Stability Margin Measurements for X-38 Robustness Analysis

A method has been developed for real-time stability margin measurement calculations. The method relies on a tailored-forced excitation targeted to a specific frequency range. Computation of the frequency response is matched to the specific frequencies contained in the excitation. A recursive Fourier transformation is used to make the method compatible with real-time calculation. The method was incorporated into the X-38 nonlinear simulation and applied to an X-38 robustness test. X-38 stability margins were calculated for different variations in aerodynamic and mass properties over the vehicle flight trajectory. The new method showed results comparable to more traditional stability analysis techniques, and at the same time, this new method provided coverage that is more complete and increased efficiency.

Bosworth, John T.↗
DOE OSTI2016

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1990

Multivariable flight control synthesis and literal robustness analysis for an aeroelastic vehicle

An integrated flight/aeroelastic control law is developed analytically for a hypothetical large supersonic transport aircraft in which the first aeroelastic mode frequency of the fuselage (6 rad/sec) is near the short-period mode (2 rad/sec). The approach employed is based on a linear-quadratic-regulator (LQR) formulation (yielding model-following state-feedback gains), followed by asymptotic loop-transfer recovery of LQR robustness (to produce an output-feedback control law). The derivation is outlined, and numerical results comparing the performance and multivariate stability robustness of the present controller with those of a classical controller are presented in graphs. The two controllers are shown to have similar characteristics, even with respect to the sources of limitations on robustness.

Schmidt, David K.↗
NASA NTRS2013

Tuning and Robustness Analysis for the Orion Absolute Navigation System

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Holt, Greg N.↗
NASA NTRS2013

Tuning and Robustness Analysis for the Orion Absolute Navigation System

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test ight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modi cations since its inception, and continues as a work-in-progress. This paper seeks to show the e orts made to-date in tuning the lter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. The results generally show Monte Carlo error performance bounded by the lter uncertainty for all phases of ight. Some future items of investigation are presented related to suspected anomalies in the trajectory truth reference le.

Holt, Greg N.↗
DOE OSTI2025

A machine learning framework for accurate and robust analysis of radiation detector pulses

The microscopic properties of atomic nuclei are used to study various scientific questions. They are essential for understanding the fundamental forces of nature and the chemical evolution of the universe. Detecting decay radiation from radioactive nuclei makes it possible to probe these fundamental nuclear properties. Detector waveform traces may contain additional information about the radiation. Generally, advanced signal processing techniques are needed to extract this additional information, often involving fitting the waveform with model response functions using non-linear least-squares optimization with second-order gradient methods. While this is a powerful technique, it is also computationally expensive, leading to slow processing time, which scales with the volume of data. To address this problem, we have developed a machine learning (ML) approach that infers the characteristics of traces from a model detector response function. In particular, we are interested in classifying whether a single recorded trace consists of one or two pulse constituents and estimating the pulse parameters. Furthermore, our proposed ML method can precisely extract the pulses’ parameters, such as energy and timing information, and accurately classify the pulse multiplicity of a trace. Unlike non-learning-based approaches, our ML approach uses neural networks that are significantly faster at inference, as they do not require any optimization during this stage.

Curve fitting↗
DOE OSTI2021

Robust analysis of space-, time-, and energy-resolved soft x-ray measurements of magnetically confined fusion plasmas (invited)

A novel compact multi-energy soft x-ray (ME-SXR) diagnostic based on the PILATUS3 100K x-ray detector has been developed in collaboration between the Princeton Plasma Physics Laboratory and the University of Wisconsin-Madison and tested on the Madison Symmetric Torus (MST) reversed-field pinch. This solid-state photon-counting detector consists of a two-dimensional array of ~100 000 pixels for which the lower photon absorption cutoff energy can be independently set, allowing it to be configured for a unique combination of simultaneous spatial, spectral, and temporal resolution of ~1 cm, 100 eV, and 500 Hz, respectively. The diagnostic is highly versatile and can be readily adapted to diverse plasma operating conditions and scientific needs without any required downtime. New results from improved-confinement and quasi-single helicity plasmas in the MST demonstrate how the detector can be applied to study multiple aspects of the evolution of magnetically confined fusion-grade plasmas. These include observing the evolution of thermal emissivity, characterizing the energy of mid-Z excitation lines, extracting the T e profile, and observing the evolution of non-thermal populations. A technique for integrating the ME-SXR diagnostic into an integrated data analysis framework based on Bayesian inference is also presented. As a result, this allows ME-SXR measurements to be combined with data for complementary diagnostics in order to simultaneously infer Te and nZ from all available information.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational and clinical use. More software tools are under development for automatic analysis of other 4D imaging modalities. This research is in part supported by NIH (U54CA137788/132378) and City Seed FY13(Grant No. 93348-14 01). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Precise Point Positioning for the Efficient and Robust Analysis of GPS Data From Large Networks

Networks of dozens to hundreds of permanently operating precision Global Positioning System (GPS) receivers are emerging at spatial scales that range from 10(exp 0) to 10(exp 3) km. To keep the computational burden associated with the analysis of such data economically feasible, one approach is to first determine precise GPS satellite positions and clock corrections from a globally distributed network of GPS receivers. Then, data from the local network are analyzed by estimating receiver specific parameters with receiver-specific data; satellite parameters are held fixed at their values determined in the global solution. This "precise point positioning" allows analysis of data from hundreds to thousands of sites every day with 40 Mflop computers, with results comparable in quality to the simultaneous analysis of all data. The reference frames for the global and network solutions can be free of distortion imposed by erroneous fiducial constraints on any sites.

Zumberge, J. F.↗