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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Discriminating Quantum States with Quantum Machine Learning

Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algebra subroutines (BLAS), a fundamental element in most ML algorithms. By making use of BLAS operations, we propose, implement and analyze a quantum k-means (qk-means) algorithm with a low time complexity of O(NKlog(D)I/C) to apply it to the fundamental problem of discriminating quantum states at readout. Discriminating quantum states allows the identification of quantum states |0⟩ and |1⟩ from low-level in-phase and quadrature signal (IQ) data, and can be done using custom ML models. In order to reduce dependency on a classical computer, we use the qk-means to perform state discrimination on the IBMQ Bogota device and managed to find assignment fidelities of up to 98.7% that were only marginally lower than that of the k-means algorithm. We also performed a cross-talk benchmark on the quantum device by applying both algorithms to perform state discrimination on a combination of quantum states and using Pearson Correlation coefficients and assignment fidelities of discrimination results to conclude on the presence of cross-talk on qubits. Evidence shows cross-talk in the (1, 2) and (2, 3) neighboring qubit couples for the analyzed device.

Quiroga, David↗
DOE OSTI2021

pnnl/neuromancer

Dynamics-based deep learning methods to modernize current scientific computing methods. Neuromancer is currently capable of solving inverse problems for a system of ordinary differential equations. The functionality includes system identification and constrained optimal control of unknown or partially known ODEs.

Skomski, Elliott↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
NASA NTRS1971

A program for identification of linear systems

A program has been written for the identification of parameters in certain linear systems. These systems appear in biomedical problems, particularly in compartmental models of pharmacokinetics. The method presented here assumes that some of the state variables are regularly modified by jump conditions. This simulates administration of drugs following some prescribed drug regime. Parameters are identified by a least-square fit of the linear differential system to a set of experimental observations. The method is especially suited when the interval of observation of the system is very long.

Buell, J.↗
NASA NTRS1988

The IUE data base: Homogenizing the IUE object nomenclature

The IUE project started to homogenize the object nomenclature in the IUE data base. Due to the absence of an official IAU nomenclature hierarchy and in view of the increasing confusion in IUE (and, in general, astronomical) object identifications, the IUE project adopted its own nomenclature hierarchy. The scheme and problems encountered in establishing it are described.

Barylak, Michael↗
NASA NTRS1992

Control design for the SERC experimental testbeds

Viewgraphs on control design for the Space Engineering Research Center experimental testbeds are presented. Topics covered include: SISO control design and results; sensor and actuator location; model identification; control design; experimental results; preliminary LAC experimental results; active vibration isolation problem statement; base flexibility coupling into isolation feedback loop; cantilever beam testbed; and closed loop results.

Jacques, Robert↗
NASA NTRS1994

Parameter identification for X-31A at high angles of attack

The U.S./German experimental aircraft X-31A was designed and constructed to demonstrate enhanced fighter maneuverability. Post-stall maneuvering is enabled by applying new technologies such as high angle of attack aerodynamics and flight control system integrated thrust vectoring. Two demonstrator aircraft have been built by the main contractors, Rockwell International and Deutsche Aerospace (formerly MBB). Flight testing started in October 1990 and before the end of 1992 both aircraft had accomplished a significant number of flights covering the entire AoA regime from about -5 to 70 deg. Throughout the envelope expansion, DLR Institute of Flight Mechanics conducted parameter identification (PID) to determine the aerodynamic parameters of the aircraft from flight test data and to compare the results to the predictions from the aerodynamic dataset (ADS). The application of system identification to high AoA / post-stall flight data raises some major problems, which are discussed in this paper. Results from both longitudinal and lateral-directional motion will be presented.

S Weiss↗
NASA NTRS1998

Smart Optical RAM for Fast Information Management and Analysis

Statement of Problem Instruments for high speed and high capacity in-situ data identification, classification and storage capabilities are needed by NASA for the information management and analysis of extremely large volume of data sets in future space exploration, space habitation and utilization, in addition to the various missions to planet-earth programs. Parameters such as communication delays, limited resources, and inaccessibility of human manipulation require more intelligent, compact, low power, and light weight information management and data storage techniques. New and innovative algorithms and architecture using photonics will enable us to meet these challenges. The technology has applications for other government and public agencies.

Liu, Hua-Kuang↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗
NASA NTRS2024

Looking to the Future: A Call to Action for Advanced GNC Algorithm Verification and Validation

Future space systems will rely on autonomous Guidance, Navigation, and Control (GNC) functions to efficiently manage safe and precise self-directed operations in uncertain complex environments. Fundamentally, the GNC system plays a key role in mission performance and safety because it computes the ideal trajectory (Guidance), determines the actual trajectory (Navigation), and executes the ideal trajectory (Control) of a vehicle’s position and attitude. Our current GNC systems are highly automated and already have a high degree of complexity. As missions become more ambitious, GNC systems for launch vehicles and space platforms (e.g., spacecraft, probes, and landers) will require higher levels of performance and autonomous operation than previously encountered, for example, this includes GNC for optimizing aerodynamic and/or propulsion performance during planetary entry. This GNC Verification and Validation (V&V) paper highlights concerns with what undoubtedly will be a trend towards increased complexity as fully autonomous GNC systems are developed for future space missions. Clearly, complex GNC systems pose challenges in the prelaunch V&V phase, which is a relatively expensive part of a mission’s life cycle. Essentially the V&V phase is focused on checking that the system effectively meets all the design and operational requirements for the mission. The authors of this paper (i.e., the Inter-Agency Working Group of GNC subject matter experts) focused on this fundamental question over the past few years: Will the GNC engineering community of practice be sufficiently prepared to perform the necessary V&V on evolving GNC architectures that are driven by very demanding requirements for autonomy, resiliency, reconfigurability, adaptability, and mission cost-benefit balance? It is the viewpoint of our Inter-Agency team that the GNC V&V approaches and processes needed to address the next generation of complex GNC systems, which likely will employ various forms of modern GNC technology, are not currently established to the level the community will need in the future. While researchers and practitioners have made some progress in developing new GNC V&V methods for modern GNC systems, a good deal of work remains to be done to codify such methods in a comprehensive and systematic manner. Thus, the Inter-Agency team’s partner organizations [the National Aeronautics and Space Administration (NASA), the European Space Agency (ESA), the National Centre for Space Studies (CNES), the German Aerospace Center (DLR), the French Aerospace Lab (ONERA), and ISAE-SUPAERO] have conducted preliminary investigations into advancing GNC V&V techniques, which resulted in the identification of the need for education, new V&V tools, and benchmark problems for the GNC community. The necessary proactive steps to be taken to meet the challenges and fill the gaps in GNC V&V are summarized in this paper. The first steps include identifying advanced analysis tools, developing a GNC V&V roadmap, and expanding education and training programs for GNC practitioners. This paper is a call to action and proposes a comprehensive set of recommended actions for all our stakeholders: space agencies, researchers, and industry.

Samir Bennani↗
DOE OSTI2022

Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data

In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗
NASA NTRS1989

Expansion and orthogonalization of measured modes for structure identification

The purpose was to investigate a new simultaneous expansion/orthogonalization method in comparison with two previously published expansion methods and a widely used orthogonalization technique. Each expansion method uses data from an analytical model of the structure to complete the estimate of the mode shape vectors. Berman and Nagy used Guyan expansion in their work with improving analytical models. In this method, modes are expanded one at a time, producing a set not orthogonal with respect to the mass matrix. Baruch and Bar Itzhack's optimal orthogonalization procedure was used to subsequently adjust the expanded modes. A second expansion technique was presented by O'Callahan, Avitabile, and Reimer and separately by Kammer. Again, modes are expanded individually and orthogonalized after expansion with the same optimal technique as above. Finally, a simultaneous expansion/orthogonalization method was developed from the orthogonal Procrustes problem of computational mathematics. In this method modes are optimally expanded as a set and orthogonal with respect to the mass matrix as a result. Two demonstation problems were selected for the comparison of the methods described. The first problem is an 8 degree of freedom spring-mass problem first presented by Kabe. Several conditions were examined for expansion method including the presence of errors in the measured data and in the analysis models. As a second demonstration problem, data from tests of laboratory scale model truss structures was expanded for system identification. Tests with a complete structure produced a correlated analysis model and the stiffness and mass matrices. Tests of various damaged configurations produced measured data for 6 modes at 14 dof locations.

Smith, Suzanne Weaver↗
DOE OSTI2020

Bus Clustering for Distribution Grid Topology Identification

Recovering the distribution grid topology is essential to perform several distribution system operator functions. Many algorithms that address the topology recovery problem have already been proposed in the literature. Most are based on a priori information regarding which buses are fed by which substation; however, this information might not be available because frequent grid reconfigurations change the distribution grid portion connected to each substation. This paper addresses the problem of assigning every substation the set of buses that it is feeding, given field data. First, the aforementioned task is cast as a nonconvex optimization problem. Second, a relaxed version of the optimization problem is solved via the alternating direction method of multipliers. Finally, the performance of our approach is validated through numerical simulations of realistic scenarios using a standard IEEE benchmark feeder.

bus clustering↗
NASA NTRS2012

Managing Small Spacecraft Projects: Less is Not Easier

Managing small, low cost missions (class C or D) is not necessarily easier than managing a full flagship mission. Yet, small missions are typically considered easier to manage and used as a training ground for developing the next generation of project managers. While limited resources can be a problem for small missions, in reality most of the issues inherent in managing small projects are not the direct result of limited resources. Instead, problems encountered by managers of small spacecraft missions often derive from 1) the perception that managing small projects is easier if something is easier it needs less rigor and formality in execution, 2) the perception that limited resources necessitate or validate omitting standard management practices, 3) less stringent or unclear guidelines or policies for small projects, and 4) stakeholder expectations that are not consistent with the size and nature of the project. For example, the size of a project is sometimes used to justify not building a full, detailed integrated master schedule. However, while a small schedule slip may not be a problem for a large mission, it can indicate a serious problem for a small mission with a short development phase, highlighting the importance of the schedule for early identification of potential issues. Likewise, stakeholders may accept a higher risk posture early in the definition of a low-cost mission, but as launch approaches this acceptance may change. This presentation discusses these common misconceptions about managing small, low cost missions, the problems that can result, and possible solutions.

Barley, Bryan↗
DOE OSTI2021

Directional Recoil Detection

Searches for dark matter–induced recoils have made impressive advances in the last few years. Yet the field is confronted by several outstanding problems. First, the inevitable background of solar neutrinos will soon inhibit the conclusive identification of many dark matter models. Second, and more fundamentally, current experiments have no practical way of confirming a detected signal's Galactic origin. The concept of directional detection addresses both of these issues while offering opportunities to study novel dark matter– and neutrino-related physics. The concept remains experimentally challenging, but gas time projection chambers are an increasingly attractive option and, when properly configured, would allow directional measurements of both nuclear and electron recoils. In this review, we reassess the required detector performance and survey relevant technologies. Fortuitously, the highly segmented detectors required to achieve good directionality also enable several fundamental and applied physics measurements. As a result, we comment on near-term challenges and how the field could be advanced.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

New directions in the search for dark matter

The identification of the nature of dark matter is one of the most important problems confronting particle physics. Current observational constraints permit the mass of the dark matter to range from 10^{-22} 10 − 22 eV - 10^{48} 10 48 GeV. Given the weak nature of these bounds and the ease with which dark matter models can be constructed, it is clear that the problem can only be solved experimentally. In these lectures, I discuss methods to experimentally probe a wide range of dark matter candidates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗