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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗

NASA NTRS1985

A Nonlinear Dual-Adaptive Control Strategy for Identification and Control of Flexible Structures

A technique is presented for obtaining a control law to regulate the modal dynamics and identify the modal parameters of a flexible structure. The method is based on using a min-max performance index to derive a control law which may be considered to be a best compromise between optimum one step control and identification inputs. Features of the approach are demonstrated by a computer simulation of the controlled modal response of a flexible beam.

Thau, F. E.↗

NASA NTRS1975

Optimization of duct acoustic liners of finite length

Lining design procedures generally ignore the effects of discontinuous impedance changes on reflection and modal redistribution of incident energy. The present study involves identification of the mechanisms of optimal suppression by segmented liners, including impedance discontinuity effects. The mathematical model involves rectangular ducts in the absence of mean flow. Segmented acoustic fields are represented by duct eigenfunction expansions, and mode matching is used to ensure continuity of the total field. Extensive parameter studies were used to identify attenuation mechanisms and to define preliminary liner configurations. These are used as starting values in conjugate-gradient optimization studies. Optimal segmented liners are presented and it is shown that, if the sound source is sufficiently well-defined, conventional infinite duct optimum attenuation rates can be improved upon by taking advantage of wall impedance discontinuities. Finally, follow-on work which will involve experimental verification and extension to the case with mean flow in the duct is described.

Beckemeyer, R. J.↗

NASA NTRS1991

Structural damage identification using mathematical optimization techniques

An identification procedure is proposed to identify damage characteristics (location and size of the damage) from dynamic measurements. This procedure was based on minimization of the mean-square measure of difference between measurement data (natural frequencies and mode shapes) and the corresponding predictions obtained from the computational model. The procedure is tested for simulated damage in the form of stiffness changes in a simple fixed free spring mass system and symmetric cracks in a simply supported Bernoulli Euler beam. It is shown that when all the mode information is used in the identification procedure it is possible to uniquely determine the damage properties. Without knowing the complete set of modal information, a restricted region in the initial data space has been found for realistic and convergent solution from the identification process.

Shen, Mo-How Herman↗

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 NTRS1983

Experimental modal analysis

Experimental modal analysis is extremely important with regard to verification of analytical models, identification of vibration and acoustic problems, and structural modification and sensitivity analysis. With the expanding access of the testing environment to computational power, the complexity of existing approaches, as well as the development of new approaches, to the estimation of modal parameters has grown tremendously. Currently, the state of the art in experimental modal analysis involves methods that can be grouped in four categories: forced normal mode excitation method, frequency response function method, damped complex exponential function method, and mathematical input-output model methods. The theoretical basis of each of these general approaches, with appropriate references, is reviewed briefly.

Allemang, R. J.↗

NASA NTRS1988

Model reduction in the simulation of interconnected flexible bodies

Given the control system specifications for a system of interconnected rigid and flexible bodies, methods now exist for determining the system modes that do not interact 'strongly' with the controller. Once these important system modes are known, there still remains the problem of determining the modes of individual bodies that should be retained, since, in the final analysis, it is the modal information at the component level that must be fed into any multibody simulation code. Systematic identification of these component modes is achieved through a two-phase matrix diagonalization process starting with judiciously chosen submatrices of the system modal matrix.

Eke, Fidelis O.↗

DOE OSTI2022

Identification and demonstration of roGFP2 as an environmental sensor for cryogenic correlative light and electron microscopy

Cryogenic correlative light and electron microscopy (cryo-CLEM) seeks to leverage orthogonal information present in two powerful imaging modalities. While recent advances in cryogenic electron microscopy (cryo-EM) allow for the visualization and identification of structures within cells at the nanometer scale, information regarding the cellular environment, such as pH, membrane potential, ionic strength, etc., which influences the observed structures remains absent. Fluorescence microscopy can potentially be used to reveal this information when specific labels, known as fluorescent biosensors, are used, but there has been minimal use of such biosensors in cryo-CLEM to date. Here we demonstrate the applicability of one such biosensor, the fluorescent protein roGFP2, for cryo-CLEM experiments. At room temperature, the ratio of roGFP2 emission brightness when excited at 425 nm or 488 nm is known to report on the local redox potential. When samples containing roGFP2 are rapidly cooled to 77 K in a manner compatible with cryo-EM, the ratio of excitation peaks remains a faithful indicator of the redox potential at the time of freezing. Using purified protein in different oxidizing/reducing environments, we generate a calibration curve which can be used to analyze in situ measurements. As a proof-of-principle demonstration, we investigate the oxidation/reduction state within vitrified Caulobacter crescentus cells. The polar organizing protein Z (PopZ) localizes to the polar regions of C. crescentus where it is known to form a distinct microdomain. Finally, by expressing an inducible roGFP2-PopZ fusion we visualize individual microdomains in the context of their redox environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗

NASA NTRS1979

Indirect adaptive stabilization of a large, flexible, spinning spacering Simulation studies

A recently formulated approach to the adaptive control of distributed systems combines the simultaneous identification and control philosophy of indirect adaptive control with a modal expansion description of distributed systems. The regulation of a large, flexible, spinning spacering, one half of a dual momentum control device proposed for attitude control of large space systems, provides the example upon which the capabilities of such a synthesis method are tested via simulation.

Hamm, A. L.↗

NASA NTRS2014

Vibro-Acoustic Modulation Based Damage Identification in a Composite Skin-Stiffener Structure

The vibro-acoustic modulation method is applied to a composite skin-stiffener structure to investigate the possibilities to utilize this method for damage identification in terms of detection, localisation and damage quantification. The research comprises a theoretical part and an experimental part. An impact load is applied to the skin-stiffener structure, resulting in a delamination underneath the stiffener. The structure is interrogated with a low frequency pump excitation and a high frequency carrier excitation. The analysis of the response in a frequency band around the carrier frequency is employed to assess the damage identification capabilities and to gain a better understanding of the modulations occurring and the underlying physical phenomena. Though vibro-acoustic is shown to be a sensitive method for damage identification, the complexity of the damage, combined with a high modal density, complicate the understanding of the relation between the physical phenomena and the modulations occurring. more research is recommended to reveal the physics behind the observations.

Ooijevaar, T. H.↗

NASA NTRS1997

Identification of Rotorcraft Structural Dynamics from Flight and Wind Tunnel Data

Excessive vibration remains one one of the most difficult problems that faces the helicopter industry today, affecting all production helicopters at some phase of their development. Vibrations in rotating structures may arise from external periodic dynamic airloads whose frequencies are are close to the natural frequencies of the rotating system itself. The goal for the structures engineer would thus be to design a structure as free from resonance effects as possible. In the case of a helicopter rotor blade these dynamic loads are a consequence of asymmetric airload distribution on the rotor blade in forward flight, leading to a rich collection of higher harmonic airloads that force rotor and airframe response. Accurate prediction of the dynamic characteristics of a helicopter rotor blade will provide the opportunity to affect in a positive manner noise intensity, vibration level, durability, reliability and operating costs by reducing objectionable frequencies or moving them to a different frequency range and thus providing us with a lower vibration rotor. In fact, the dynamic characteristics tend to define the operating limits of a rotorcraft. As computing power has increased greatly over the last decade, researchers and engineers have turned to analyzing the vibrational characteristics of aerospace structures at the design and development stage of the production of an aircraft. Modern rotor blade construction methods lead to products with low mass and low inherent damping so careful design and analysis is required to avoid resonance and an undesirable dynamic performance. In addition, accurate modal analysis is necessary for several current approaches in elastic system identification and active control.

McKillip, Robert M., Jr.↗

DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1989

In-house experiments in large space structures at the Air Force Wright Aeronautical Laboratories Flight Dynamics Laboratory

The Flight Dynamics Laboratory is committed to an in-house, experimental investigation of several technical areas critical to the dynamic performance of future Air Force large space structures. The advanced beam experiment was successfully completed and provided much experience in the implementation of active control approaches on real hardware. A series of experiments is under way in evaluating ground test methods on the 12 meter trusses with significant passive damping. Ground simulated zero-g response data from the undamped truss will be compared directly with true zero-g flight test data. The performance of several leading active control approaches will be measured and compared on one of the trusses in the presence of significant passive damping. In the future, the PACOSS dynamic test article will be set up as a test bed for the evaluation of system identification and control techniques on a complex, representative structure with high modal density and significant passive damping.

Gordon, Robert W.↗

NASA NTRS1997

On-Line Modal State Monitoring of Slowly Time-Varying Structures

Monitoring the dynamic response of structures is often performed for a variety of reasons. These reasons include condition-based maintenance, health monitoring, performance improvements, and control. In many cases the data analysis that is performed is part of a repetitive decision-making process, and in these cases the development of effective on-line monitoring schemes help to speed the decision-making process and reduce the risk of erroneous decisions. This report investigates the use of spatial modal filters for tracking the dynamics of slowly time-varying linear structures. The report includes an overview of modal filter theory followed by an overview of several structural system identification methods. Included in this discussion and comparison are H-infinity, eigensystem realization, and several time-domain least squares approaches. Finally, a two-stage adaptive on-line monitoring scheme is developed and evaluated.

Johnson, Erik A.↗

NASA NTRSDate not supplied

High Fidelity Adaptively Refined CFD and Reduced Order Models of a High Aspect Ratio Aeroelastic Wing Wind-Tunnel Model

The NASA Advanced Air Transport Technology (AATT) goal of reduced fuel burn for transport aircraft has led to the NASA N+3 High Aspect Ratio Wing (HARW) subproject. This project requires identifying, developing, and demonstrating key technologies and integrated multidisciplinary solutions to enable a safe, high performance, aeroelastic wing. Since this aircraft will have a high aspect ratio wing, aeroelasticity is expected to be a major issue in the design. In this paper high fidelity computational fluid dynamics (CFD) is performed with flow adapted meshes. A system identification of the aerodynamics is developed using both a multi-modal multi-sine time-marching and a multi-mode linear frequency domain method. GLA, MLA and flutter suppression simulations will be performed.

Robert Bartels↗

DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

DOE OSTI2021

Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to understand and describe their behavior. However, there is a scarcity of plasma models of lower fidelity than magnetohydrodynamics (MHD), although these reduced models hold promise for understanding key physical mechanisms, efficient computation, and real-time optimization and control. Galerkin models, obtained by projection of the MHD equations onto a truncated modal basis, and data-driven models, obtained by modern machine learning and system identification, can furnish this gap in the lower levels of the model hierarchy. This work develops a reduced-order modeling framework for compressible plasmas, leveraging decades of progress in projection-based and data-driven modeling of fluids. We begin by formalizing projection-based model reduction for nonlinear MHD systems. To avoid separate modal decompositions for the magnetic, velocity, and pressure fields, we introduce an energy inner product to synthesize all of the fields into a dimensionally consistent, reduced-order basis. Next, we obtain an analytic model by Galerkin projection of the Hall-MHD equations onto these modes. We illustrate how global conservation laws constrain the model parameters, revealing symmetries that can be enforced in data-driven models, directly connecting these models to the underlying physics. We demonstrate the effectiveness of this approach on data from high-fidelity numerical simulations of a three-dimensional spheromak experiment. Finally, this manuscript builds a bridge to the extensive Galerkin literature in fluid mechanics and facilitates future principled development of projection-based and data-driven models for plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗