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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 NTRSDate not supplied

Estimation of Permanent Magnet Synchronous Motor Parameters using a Linear Unknown Input Interval Observer

This paper develops and evaluate a technique to estimate the speed and unknown load torque of a permanent magnet synchronous motor (PMSM). An interval unknown input observer (UIO) for linear time-invariant (LTI) systems is designed and utilized to estimate the PMSM parameters. Interval UIOs are advantageous as they allow for the estimation of both states and unknown inputs, while accounting for varying uncertainty sources, such as model and measurement uncertainties inherent in dynamic systems and processes. First, the nonlinear PMSM model is linearized to transform it to a suitable form for application of the linear interval UIO. Then, the interval UIO is applied to jointly estimate the motor speed and the unknown load torque disturbance. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated for the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
DOE OSTI2020

Robust Adaptive Decentralized Dynamic State Estimation with Unknown Control Inputs using Field PMU Measurements

This paper proposes a robust adaptive decentralized dynamic state estimation method for power system with unknown inputs of the highly detailed synchronous machine model. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive model. The latter is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. Thanks to the consideration of implicit cross-correlations between the states and the unknown inputs, only generator terminal voltage and current phasors are needed. Test results on the US WECC system using the field PMU measurements show that the proposed method is able to track both the system dynamic states and unknown controller inputs. These information could significantly benefit the validation and calibration of generator controller parameters.

Zhao, Junbo↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Systems and methods for electronic communication with a device using an unknown communications protocol

Various technologies for communicating with systems that communicate using an unknown communications protocol are described herein. A transceiver intercepts a plurality of communications exchanged between two or more transceivers on a communications network. Pattern-recognition algorithms are executed over the plurality of communications, and features of an unknown communications protocol that governs the communications between the two or more transceivers are inferred based upon output of the pattern-recognition algorithms. A communication is formatted based upon the inferred features of the unknown communications protocol, and the communication transmitted to one or more systems on the communications network by way of the transceiver. The communication at least partially conforms to the unknown communications protocol, and so may be interpreted by systems on the network.

Patel, Silpan M.↗
DOE OSTI2019

Unscented Kalman Filter-based Unbiased Minimum-Variance Estimation for Nonlinear Systems with Unknown Inputs

This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linerization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

Unscented Kalman filter, unbiased minimumvariance ↗
DOE OSTI2017

Non-destructive identification of unknown minor phases in polycrystalline bulk alloys using three-dimensional X-ray diffraction

Minor phases make considerable contributions to the mechanical and physical properties of metals and alloys. Unfortunately, it is difficult to identify unknown minor phases in a bulk polycrystalline material using conventional metallographic methods. Here, a non-destructive method based on three-dimensional X-ray diffraction (3DXRD) is developed to solve this problem. Simulation results demonstrate that this method is simultaneously able to identify minor phase grains and reveal their positions, orientations and sizes within bulk alloys. According to systematic simulations, the 3DXRD method is practicable for an extensive sample set, including polycrystalline alloys with hexagonal, orthorhombic and cubic minor phases. Experiments were also conducted to confirm the simulation results. The results for a bulk sample of aluminum alloy AA6061 show that the crystal grains of an unexpected γ-Fe (austenite) phase can be identified, three-dimensionally and nondestructively. Therefore, we conclude that the 3DXRD method is a powerful tool for the identification of unknown minor phases in bulk alloys belonging to a variety of crystal systems. This method also has the potential to be used for in situ observations of the effects of minor phases on the crystallographic behaviors of alloys. - Highlights: •A method based on 3DXRD is developed for identification of unknown minor phase. •Grain position, orientation and size, is simultaneously acquired. •A systematic simulation demonstrated the applicability of the proposed method. •Experimental results on a AA6061 sample confirmed the practicability of the method.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Characterizing unknown systematics in large scale structure surveys

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2 ), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana . The NMR/MS 2 approach is well suited for discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Exploration of unknown mechanical assemblies through manipulation

If robots must function in unstructured environments, they must also possess the ability to acquire information and construct appropriate models of the unknown environment. This paper addresses the automatic generation of kinematic models of unknown objects with movable parts in the environment. If the relative motion between moving parts must be observed and characterized, vision alone cannot suffice. An approach in which manipulation is used with vision for sensing is better suited to the task of determining kinematic properties. In this paper, algorithms for constructing models of unknown mechanical assemblies and characterizing the relative motion are developed. Results of a simulation are described to demonstrate the role of manipulation in such an endeavor.

Kumar, Vijay R.↗
NASA NTRS2008

Direct Solve of Electrically Large Integral Equations for Problem Sizes to 1M Unknowns

Matrix methods for solving integral equations via direct solve LU factorization are presently limited to weeks to months of very expensive supercomputer time for problems sizes of several hundred thousand unknowns. This report presents matrix LU factor solutions for electromagnetic scattering problems for problem sizes to one million unknowns with thousands of right hand sides that run in mere days on PC level hardware. This EM solution is accomplished by utilizing the numerical low rank nature of spatially blocked unknowns using the Adaptive Cross Approximation for compressing the rank deficient blocks of the system Z matrix, the L and U factors, the right hand side forcing function and the final current solution. This compressed matrix solution is applied to a frequency domain EM solution of Maxwell's equations using standard Method of Moments approach. Compressed matrix storage and operations count leads to orders of magnitude reduction in memory and run time.

Shaeffer, John↗
NASA NTRS2022

Optimization Approach for Wind Tunnel Fan Blade Strain Gage Correlation with Test Fixture Unknowns

NASA has a significant maintenance challenge with the aging wind tunnel infrastructure across the Agency. Some wind tunnel fan blades, such as those in the National Transonic Facility at NASA Langley Research Center, have been in use for far longer than their planned 10-year lifetime. No detailed analytical models of the fan blades from their initial fabrication were created. To support the fabrication of either new, nominally identical fan blades or newly designed fan blades, accurate analytical models of the existing fan blades are necessary to demonstrate an understanding of the capabilities of the existing fan blades. Additionally, the existing documentation of the testing of the existing fan blades has gaps. In this report, a process for improving test-analysis correlation for these wind tunnel fan blades is discussed. This process involved a detailed study of unknown parameters in test fixture parameters from the old and incomplete test documentation. To improve correlation, these unknown parameters were used as design variables in an optimization study to minimize the error between test data and computed structural responses. Legacy strain gage data from tests conducted in 1981 were compared with results from a finite element model created in 2019. The use of test data from 1981 was necessary because none of the existing fan blades could be spared for use in destructive structural testing. While this process was demonstrated for wind tunnel fan blades, this process could be utilized for evaluating unknowns in test fixtures for other structural test configurations.

wind tunnel↗
DOE OSTI2023

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Recent advances in modeling large-scale, complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. Here in this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and a low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.

MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Intracellular delivery of poly(I:C) induces apoptosis of fibroblast-like synoviocytes via an unknown dsRNA sensor

Fibroblast-like synoviocytes (FLS) express functional membranous and cytoplasmic sensors for double-stranded (ds)RNA. Notably, FLS undergo apoptosis upon transfection with the synthetic dsRNA analog poly(I:C). We here studied the mechanism of intracellular poly(I:C) recognition and subsequent cell death in FLS. FLS responded similarly to poly(I:C) or 3pRNA transfection; however, only intracellular delivery of poly(I:C) induced significant cell death, accompanied by upregulation of pro-apoptotic proteins Puma and Noxa, caspase 3 cleavage, and nuclear segregation. Knockdown of the DExD/H-box helicase MDA5 did not affect the response to intracellular poly(I:C); in contrast, knockdown of RIG-I abrogated the response to 3pRNA. Knockdown of the downstream adaptor proteins IPS, STING, and TRIF or inhibition of TBK1 did not affect the response to intracellular poly(I:C), while knockdown of IFNAR blocked intracellular poly(I:C)-mediated signaling and cell death. We conclude that a so far unknown intracellular sensor recognizes linear dsRNA and induces apoptosis in FLS. - Highlights: • Intracellular poly(I:C) and 3pRNA evoke immune responses in FLS. • Only intracellular delivery of poly(I:C) induces FLS apoptosis. • FLS do not require MDA5 for their response to intracellular poly(I:C). • FLS respond to intracellular poly(I:C) independent of IPS and STING. • An unknown intracellular sensor recognizes linear dsRNA in FLS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Structural and Functional Characterization of YdjI, an Aldolase of Unknown Specificity in Escherichia coli K12

The ydj gene cluster is found in 80% of sequenced Escherichia coli genomes and other closely related species in the human microbiome. On the basis of the annotations of the enzymes located in this cluster, it is expected that together they catalyze the catabolism of an unknown carbohydrate. The focus of this investigation is on YdjI, which is in the ydj gene cluster of E. coli K-12. It is predicted to be a class II aldolase of unknown function. Here we describe a structural and functional characterization of this enzyme. YdjI catalyzes the hydrogen/deuterium exchange of the pro- S hydrogen at C3 of dihydroxyacetone phosphate (DHAP). In the presence of DHAP, YdjI catalyzes an aldol condensation with a variety of aldo sugars. YdjI shows a strong preference for higher-order (seven-, eight-, and nine-carbon) monosaccharides with specific hydroxyl stereochemistries and a negatively charged terminus (carboxylate or phosphate). The best substrate is l -arabinuronic acid with an apparent k cat of 3.0 s –1 . The product, l - glycero - l - galacto -octuluronate-1-phosphate, has a k cat / K m value of 2.1 × 10 3 M –1 s –1 in the retro-aldol reaction with YdjI. This is the first recorded synthesis of l - glycero - l - galacto -octuluronate-1-phosphate and six similar carbohydrates. The crystal structure of YdjI, determined to a nominal resolution of 1.75 Å (Protein Data Bank entry 6OFU ), reveals unusual positions for two arginine residues located near the active site. Furthermore, computational docking was utilized to distinguish preferable binding orientations for l - glycero - l - galacto -octuluronate-1-phosphate. These results indicate a possible alternative binding orientation for l - glycero - l - galacto -octuluronate-1-phosphate compared to that observed in other class II aldolases, which utilize shorter carbohydrate molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Linking in silico MS/MS spectra with chemistry data to improve identification of unknowns

Confident identification of unknown chemicals in high resolution mass spectrometry (HRMS) screening studies requires cohesive workflows and complementary data, tools, and software. Chemistry databases, screening libraries, and chemical metadata have become fixtures in identification workflows. To increase confidence in compound identifications, the use of structural fragmentation data collected via tandem mass spectrometry (MS/MS or MS2) is vital. However, the availability of empirically collected MS/MS data for identification of unknowns is limited. Researchers have therefore turned to in silico generation of MS/MS data for use in HRMS-based screening studies. This paper describes the generation en masse of predicted MS/MS spectra for the entirety of the US EPA’s DSSTox database using competitive fragmentation modelling and a freely available open source tool, CFM-ID. The generated dataset comprises predicted MS/MS spectra for ~700,000 structures, and mappings between predicted spectra, structures, associated substances, and chemical metadata. Together, these resources facilitate improved compound identifications in HRMS screening studies. These data are accessible via an SQL database, a comma-separated export file (.csv), and EPA’s CompTox Chemicals Dashboard.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Crystal structure of domain of unknown function 507 (DUF507) reveals a new protein fold

The crystal structure of the domain of unknown function family 507 protein from Aquifex aeolicus is reported (AaDUF507, UniProt O67633, 183 residues). The structure was determined in two space groups (C222 1 and P3 2 21) at 1.9 Å resolution. The phase problem was solved by molecular replacement using an AlphaFold model as the search model. AaDUF507 is a Y-shaped α-helical protein consisting of an anti-parallel 4-helix bundle base and two helical arms that extend 30-Å from the base. The two crystal structures differ by a 25° rigid body rotation of the C-terminal arm. The tertiary structure exhibits pseudo-twofold symmetry. The structural symmetry mirrors internal sequence similarity: residues 11–57 and 102–148 are 30% identical and 53% similar with an E-value of 0.002. In one of the structures, electron density for an unknown ligand, consistent with nicotinamide or similar molecule, may indicate a functional site. Docking calculations suggest potential ligand binding hot spots in the region between the helical arms. Structure-based query of the Protein Data Bank revealed no other protein with a similar tertiary structure, leading us to propose that AaDUF507 represents a new protein fold.

36 MATERIALS SCIENCE↗