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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
DOE OSTI2017

Abstract - Cooperative Research and Development Agreement between DERP Technologies, LLC and National Energy Technology Laboratory

The CRADA between NETL and Derp Tech aims to evaluate potential for early adoption of novel utilityscale advanced power control systems in distributed generation systems. The goal is to enable the grid to add significant hybrid sources, including hybrid power and thermal/electric storage systems, for flexible and adaptive islanding microgrid capabilities. Controls research conducted using the Hybrid Performance Project facility (Hyper) has illustrated the potential for fuel cell turbine hybrid power systems to deal with load transients and offdesign operation. DERP Tech is involved in the design and deployment of commercial solutions for adding microgrids and DG (hybrid combinations for Utility marketplace) to the grid. The information gained from DERP’s sample cases will enable NETL to quantify its control strategies for real world scenarios and applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

CLUSTER GLIMPSES WITH RAVEN: AO-CORRECTED NEAR AND MID-INFRARED IMAGES OF GLIMPSE C01 AND GLIMPSE C02

We discuss images of the star clusters GLIMPSE C01 (GC01) and GLIMPSE C02 (GC02) that were recorded with the Subaru IRCS. Distortions in the wavefront were corrected with the RAVEN adaptive optics (AO) science demonstrator, allowing individual stars in the central regions of both clusters—where the fractional contamination from non-cluster objects is lowest—to be imaged. In addition to J , H , and K ′ images, both clusters were observed through a narrow-band filter centered near 3.05 μ m; GC01 was also observed through two other narrow-band filters that sample longer wavelengths. Stars in the narrow-band images have an FWHM that is close to the telescope diffraction limit, demonstrating that open-loop AO systems like RAVEN can deliver exceptional image quality. The near-infrared color–magnitude diagram of GC01 is smeared by non-uniform extinction with a 1 σ dispersion Δ A{sub K} = ±0.13 mag. Spatial variations in A{sub K} are not related in a systematic way to location in the field. The Red Clump is identified in the K luminosity function (LF) of GC01, and a distance modulus of 13.6 is found. The K LF of GC01 is consistent with a system that is dominated by stars with an age >1 Gyr. As for GC02, the K LF is flat for K > 16, and the absence of a sub-giant branch argues against an old age if the cluster is at a distance of ∼7 kpc. Archival SPITZER [3.6] and [4.5] images of the clusters are also examined, and the red giant branch-tip is identified. It is demonstrated in the Appendix that the [3.6] surface brightness profiles of both clusters can be traced out to radii of at least 100 arcsec.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Legacy Analysis of Milky Way Dwarf Spheroidal Satellite Galaxies: An Update

Studies of Fermi Large Area Telescope (LAT) data coincident with dwarf spheroidal satellite galaxies (dSphs) of the Milky Way have put the most stringent constraints on models of annihilating dark matter (DM) with candidate masses in the GeV–TeV range. Recent results found the presence of small, local significance excesses from these targets, at the 2σ–3σ level. However, these excesses disagree on the predicted properties of the DM candidate, and their significance vanishes when considering correction factors for the number of trials. In this work, we apply key improvements to the analysis of dSphs. We use stricter cuts on the data, implement a method to adaptively model the background, and assume an updated framework for DM annihilation. We find that our improved background modeling leads to a better agreement between the model and the data. This produces an increase in the local and global significance of the dSph excess compared to previous studies. Finally, we find that the DM properties obtained in this work are less dependent on the sample of dSphs being considered compared to previous studies, while remaining in agreement with the predictions from the Galactic center excess observed by Fermi/LAT and the antiproton excess observed by the Alpha Magnetic Spectrometer (AMS-02). Considering our improvements, a future significant increase in the number of dwarfs may lead to a definitive confirmation or exclusion of the DM interpretation of the Galactic center excess.

cold dark matter↗
DOE OSTI2015

Further Development of Crack Growth Detection Techniques for US Test and Research Reactors

One of the key issues facing Light Water Reactors (LWRs) in extending lifetimes beyond 60 years is characterizing the combined effect of irradiation and water chemistry on material degradation and failure. Irradiation Assisted Stress Corrosion Cracking (IASCC), in which a crack propagates in a susceptible material under stress in an aggressive environment, is a mechanism of particular concern. Full understanding of IASCC depends on real time crack growth data acquired under relevant irradiation conditions. Techniques to measure crack growth in actively loaded samples under irradiation have been developed outside the US - at the Halden Boiling Water Reactor, for example. Several types of IASCC tests have also been deployed at the MITR, including passively loaded crack growth measurements and actively loaded slow strain rate tests. However, there is not currently a facility available in the US to measure crack growth on actively loaded, pre-cracked specimens in LWR irradiation environments. A joint program between the Idaho National Laboratory (INL) and the Massachusetts Institute of Technology (MIT) Nuclear Reactor Laboratory (NRL) is currently underway to develop and demonstrate such a capability for US test and research reactors. Based on the Halden design, the samples will be loaded using miniature high pressure bellows and a compact loading mechanism, with crack length measured in real time using the switched Direct Current Potential Drop (DCPD) method. The basic design and initial mechanical testing of the load system and implementation of the DCPD method have been previously reported. This paper presents the results of initial autoclave testing at INL and the adaptation of the design for use in the high pressure, high temperature water loop at the MITR 6 MW research reactor, where an initial demonstration is planned in mid-2015. Materials considerations for the high pressure bellows are addressed. Design modifications to the loading mechanism required by the size constraints of the MITR water loop are described. The safety case for operation of the high pressure gas-driven bellows mechanism is also presented. Key issues are the design and response of systems to limit gas flow in the event of a high pressure gas leak in the in-core autoclave. Integrity of the autoclave must be maintained and reactivity effects due to voiding of the loop coolant must be shown to be within the reactor technical specifications. The technical development of the crack growth monitor for application in the INL Advanced Test Reactor or the MITR can act as a template for adaptation of this technology in other reactors. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

A new adaptive light beam focusing principle for scanning light stimulation systems

In this article a novel principle to achieve optimal focusing conditions or rather the smallest possible beam diameter for scanning light stimulation systems is presented. It is based on the following methodology: First, a reference point on a camera sensor is introduced where optimal focusing conditions are adjusted and the distance between the light focusing optic and the reference point is determined using a laser displacement sensor. In a second step, this displacement sensor is used to map the topography of the sample under investigation. Finally, the actual measurement is conducted, using optimal focusing conditions in each measurement point at the sample surface, that are determined by the height difference between camera sensor and the sample topography. This principle is independent of the measurement values, the optical or electrical properties of the sample, the used light source, or the selected wavelength. Furthermore, the samples can be tilted, rough, bent, or of different surface materials. In the following the principle is implemented using an optical beam induced current system, but basically it can be applied to any other scanning light stimulation system. Measurements to demonstrate its operation are shown, using a polycrystalline silicon solar cell.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.

25 ENERGY STORAGE↗
DOE OSTI2021

DR-RINS: Digital real-time relative intensity noise suppressor for pump–probe spectroscopy and microscopy

Relative intensity noise (RIN) inherent in fiber lasers poses a serious obstacle to their use in pump–probe spectroscopy and imaging. RIN can be removed through an analog balanced detector, or, as we have previously shown, software adaptive noise cancellation (ANC) on digitized signals. One major drawback to software ANC is the added time required for digitizing and post-processing. In this article, we describe a design for ANC on a field-programmable gate array (FPGA), making use of high-level synthesis tools and fixed-point arithmetic to achieve real-time laser RIN suppression at 25 MHz sample rates. Unlike the software-ANC approach, the FPGA-ANC device can serve as a dedicated drop-in denoiser, placed between the detectors and a commercial lock-in amplifier. We demonstrate its application to transient absorption spectroscopy and microscopy, lowering the noise floor to ~17 dB above the shot noise limit. Furthermore, we demonstrate a dramatic improvement in data acquisition time from ~6 h to ~5 min in a real-time imaging scenario.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
DOE OSTI2014

Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies

Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Simulation Based Inference with Domain Adaptation for Strong Gravitational Lensing

Simulation based inference leverages machine learning to carry out Bayesian inference for systems with intractable likelihoods. However, transitioning a network trained on simulated data to real data runs the risk of encountering domain shift, leading to performance losses. We attempt to implement domain adaptation into the sbi neural posterior estimation framework using the Maximum Mean Discrepancy as an additional network loss. We test two network architectures and use masked autoregressive flow for density estimation. We test the network on a set of 400,000 simulated strong gravitational lensing images generated using deeplenstronomy. The source domain is defined as low noise whereas the target domain has a noise profile sampled from experimentally derived DES survey conditions. We find that, while DA does lead to performance improvements, they are marginal at ~6% less inference error. We also find a similar marginal improvement in uncertainty calibration at around 8%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Mycorrhiza Better Predict Soil Fungal Community Composition and Function than Aboveground Traits in Temperate Forest Ecosystems

Forests in the northeastern US are experiencing shifts in community composition due to the northward migration of warm-adapted tree species and certain species’ declines (for example, white ash and eastern hemlock) due to invasive insects. Changes in belowground fungal communities and associated functions will inevitably follow. Therefore, we sought to investigate the relative importance of two important tree characteristics—mycorrhizal type [ectomycorrhizal (EcM) or arbuscular mycorrhizal (AM)] and leaf habit (deciduous or evergreen) on soil fungal community composition and organic matter cycling. We sampled soil in the organic and mineral horizons beneath two AM-associated (Fraxinus americana and Thuja occidentalis) and two ECM-associated tree species (Betula alleghaniensis and Tsuga canadensis), with an evergreen and deciduous species in each mycorrhizal group. To characterize fungal communities and organic matter decomposition beneath each tree species, we sequenced the ITS1 region of fungal DNA and measured the potential activity of carbon- and nitrogen-targeting extracellular enzymes. Each tree species harbored distinct fungal communities, supporting the need to consider both mycorrhizal type and leaf habit. However, between tree characteristics, mycorrhizal type better predicted fungal communities. Across fungal guilds, saprotrophic fungi were the most important group in shaping fungal community differences in soils beneath all tree species. The effect of leaf habit on carbon- and nitrogen-targeting hydrolytic enzymes depended on tree mycorrhizal association in the organic horizon, while oxidative enzyme activities were higher beneath EcM-associated trees across both soil horizons and leaf habits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Instrument for evaluating the electrical resistance and wavelength-resolved transparency of stretchable electronics during strain

A complete analysis of strain tolerance in a stretchable transparent conductor (TC) should include tracking of both electrical conductivity and transparency during strain; however, transparency is generally neglected in contemporary analyses. In this paper, we describe an apparatus that tracks both parameters while TCs of arbitrary composition are deformed under stretching-mode strain. We demonstrate the tool by recording the electrical resistance and light transmission spectra for indium tin oxide-coated plastic substrates under both linearly increasing strain and complex cyclic strain processes. The optics are sensitive across the visible spectrum and into the near-infrared region (∼400-900 nm), and without specifically optimizing for sampling speed, we achieve a time resolution of ∼200 ms. In our automated analysis routine, we include a calculation of a common TC figure of merit (FOM), and because solar cell electrodes represent a key TC application, we also weigh both our transparency and FOM results against the solar power spectrum to determine “solar transparency” and “solar FOM.” Finally, we demonstrate how the apparatus may be adapted to measure the basic performance metrics for complete solar cells under uniaxial strain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗