Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model interpretation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗

Design and testing of ultrasound probe adapters for a robotic imaging platform

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited. Instead, robotics and computer vision technology can simplify image acquisition. As a first step towards this larger goal, here, we focus on the development of prototypes for ultrasound probe securement using a robotics platform. The ability of four probe adapter technologies to precisely capture images at anatomical locations, repeatedly, and with different ultrasound transducer types were evaluated across more than five scoring criteria. Testing demonstrated two of the adapters outperformed the traditional robot gripper and manual image capture, with a compact, rotating design compatible with wireless imaging technology being most suitable for use at the point of injury. Next steps will integrate the robotic platform with computer vision and deep learning image interpretation models to automate image capture and diagnosis. This will lower the skill threshold needed for medical imaging-based triage, enabling this procedure to be available at or near the point of injury.

47 OTHER INSTRUMENTATION↗

Observation of D 2 molecule line emission after massive D 2 injection into runaway electron plateaus in DIII-D

Molecular deuterium line emission is observed in both the visible and ultraviolet (UV) wavelength ranges after massive (> 100 Torr-L) injection of D 2 gas into post-disruption runaway electron (RE) dominated plasmas in the DIII-D tokamak. D 2 UV line emission is found to be the dominant source of radiated power, surpassing D Lyα. Interpretive modeling with a collisional-radiative model (CRM) indicates that D 2 radiation surpasses D radiation because Lyα is strongly trapped, while D 2 UV lines are mostly untrapped. The CRM also indicates that the D 2 line emission is completely dominated by RE impact (rather than thermal electron impact), so the D 2 line emission can serve as a good diagnostic for the spatial localization of REs. Analysis of D 2 visible lines indicates that the D 2 molecules in the plasma are thermally equilibrated with the background plasma, with vibrational, rotational, and kinetic temperatures all near 0.3 eV. D 2 spectroscopy therefore serves as a convenient diagnostic of background plasma temperature. As a result, measurement of D 2 radiated power also appears to serve as a useful diagnostic for constraining neutral transport modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Characterizing W sources in the all-W wall, all-RF WEST tokamak environment

In this work, experimental data, together with interpretive modeling tools, are examined to study trends in the tungsten (W) source in the all-W environment of the WEST tokamak, both from the divertor and from the main chamber. In particular, a poloidal limiter protecting an ion cyclotron resonance heating (ICRH) antenna is used as proxy for main chamber sourcing. The key study is carried out by stepping up lower hybrid current drive (LHCD) power, as the only auxiliary power source. Limiter and divertor W sources exhibit a qualitatively similar proportionality to the total power crossing the separatrix, P SEP , although the main chamber source remains substantially lower than the divertor source, for the range of P SEP accessible in the experiments. Intepretive modeling of the limiter source is carried out with a particle-in-cell (PIC) sheath model coupled to a surface sputtering model. Oxygen is used as a proxy for all light impurity species allowing for characterization of the critical W erosion regions. To get a good quantitative match to the data, it is necessary to assume that the oxygen arrives at the surface mostly at high ionization stages (4+ and above). A separate simulation with SOLEDGE-EIRENE, constrained to measured upstream scrape-off-layer plasma profiles, gives oxygen fractional abundances that are compatible with the PIC simulation result. This is understood to arise from transport processes that dominate over recombination. Substituting the LHCD by ICRH, in an equivalent experiment, the local W source exhibits a 3× enhancement. This can be matched by the simulation, by assuming local RF electric field rectification, based on ~100 eV peak-to-peak, near-antennna electric field. This work has highlighted the particular importance of understanding the ion charge state balance of light impurities as these are most likely the dominant sputtering species in fusion devices with high-Z walls.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Localized divertor leakage measurements using isotopic tungsten sources during edge-localized mode-y H-mode discharges on DIII-D

Experiments carried out on DIII-D using a novel setup of isotopic tungsten (W) sources in the outer divertor have characterized how the W leakage from this region depends on both the exact source location and edge-localized mode (ELM) behavior. The sources are toroidally-symmetric and poloidally-localized to two regions: (1) the outer strike point (OSP) with natural abundance of W isotopes; and (2) the far-target with highly-enriched 182 W isotopes. With the use of a dual-faced collector probe (CP) in the main scrape-off layer (SOL) near the outside midplane and source-rate spectroscopy, a proxy for divertor impurity leakage is developed herein. Using this proxy, it is found that for the OSP W location, there is a nearly linear increase of leakage with the power across the separatrix ( ), which is consistent with the effect of an increased upstream ion temperature parallel gradient force in the near-SOL; trends in the pedestal density and collisionality are also seen. Conversely, it is found that for the far-target W location leakage falls off rapidly as increases and ELM size decreases, which is suggestive that ELM size plays a role in the leakage from this location. Indications for main SOL W contamination is evidenced by the measurement of large deposition asymmetries on the two opposite CP faces. These measurements are coupled with interpretive modeling showing SOL W accumulation near the separatrix furthest from both targets driven by forces parallel to the magnetic field. This experimental setup, together with the target and upstream W measurements, provides information on the transport from different divertor W source locations and leakage. These studies help to elucidate the physics driving divertor impurity source rates and leakage, with and without ELMs, and provide better insight on the link in the chain connecting wall impurity sources to core impurity levels in magnetic fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The role of B T -dependent flows on W accumulation at the edge of the confined plasma

Abstract Near-separatrix impurity accumulation between the crown and the outer midplane of tokamaks is a common feature in results from codes such as SOLPS-ITER and DIVIMP; however, experimental evidence of accumulation has only recently been obtained and is reported here. The codes find that the poloidal distribution of impurity ions in the scrape-off layer (SOL) depends primarily on toroidal field ( B T )-dependent parallel flow patterns of the background plasma and the parallel ion temperature gradient (∇ ‖ T ion ) force. Experimentally, Mach probes used in L-mode plasmas with favorable (for H-mode access) B T measure fast ( M ∼ 0.3–0.5) inner-target-directed (ITD) background plasma flows at the crown of single-null discharges. This study reports a set of DIVIMP simulations for two similar H-mode discharges from the DIII-D W metal rings campaign differing primarily in B T -direction to assess the effect that fast ITD flows have on the distribution of W ions in the SOL. It is found that for imposed ITD flows of M = 0.3, W ions that otherwise accumulate due to the ∇ ‖ T ion -force are largely flushed out. It is also found that doubling the radial diffusion coefficient from 0.3 to 0.6 m 2 s −1 prevents accumulation due to rapid cross-field transport into the far-SOL, where background plasma flows drain W ions to the divertors. Far-SOL W distributions from DIVIMP are then used to specify input to the impurity transport code 3DLIM, which is used to interpretively model collector probe (CP) deposition patterns measured in the ‘wall-SOL’. It is demonstrated that the deposition patterns are consistent with the DIVIMP predictions of near-SOL accumulation for the unfavorable- B T direction, and little/no accumulation for the favorable- B T direction. The wall-SOL CPs have thus provided the first experimental evidence, albeit indirect, of near-SOL W accumulation—finding it occurs for the unfavorable- B T direction only. For the favorable- B T direction, fast flows can largely prevent accumulation from occurring.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Turbulent drifts of impurity ions as an explanation for anomalous radial transport in the far-SOL of DIII-D

Abstract Successful fusion reactor operation relies on minimal core contamination by impurities, otherwise too much power may be radiated and harm performance. This requires reliable predictions of impurity transport from the scrape-off layer (SOL) into the core, beyond the traditional ‘anomalous’ diffusion approach. We report a set of far-SOL tungsten transport simulations that demonstrate the role of turbulent drifts on radial impurity transport. A turbulent plasma background is simulated using the gyrokinetic SOL code Gkeyll. Tungsten ions are followed within the plasma background using only their drifts. We find that tungsten tends to travel radially outwards with velocities between v r = 300–1200 m s −1 primarily due to polarization drift. We also extract an anomalous radial diffusion coefficient that varies from D r anom = 5–20 m 2 s −1 . These results are compared to and agree with previous interpretive modeling results. We also show how the turbulent polarization drift can transport some tungsten ions from the wall inwards with effective pinch velocities up to 10 000 m s −1 . We conclude that turbulent drifts are a likely explanation for historically anomalous radial impurity transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Progress in pedestal and edge physics: Chapter 3 of the special issue: on the path to tokamak burning plasma operation

This paper describes the extensive progress that has been made in the understanding of tokamak pedestal physics since the 2007 publication of ‘Progress in the ITER Physics Basis’ (Ikeda 2007 Nucl. Fusion 47 E01–S500). It serves as Chapter 3 of the 2025 Nuclear Fusion Special Issue titled ‘On the Path to Tokamak Burning Plasma Operation’ (Campbell et al 2025 Nucl. Fusion ). This review was compiled by the pedestal and edge physics (PEP) community affiliated with the International Tokamak Physics Activity organization. It attempts to collect in one place citations to the majority of published literature on the pedestal physics topics that will be most important for the operation of a future power producing burning plasma tokamak. These include citations to publications describing the physics of the pedestal plasmas in many operating tokamaks worldwide and the pedestal physics projections for several near-term future devices including ITER. Descriptions of experimental results, interpretive modeling and predictive extrapolations are integrated together and comprehensive references are provided. This review is organized around four primary technical sections, viz.: pedestal structure, edge localized mode (ELM) characteristics, ELM control and regimes without large ELMs. Key results from many of the references are described briefly and set into the tokamak burning plasma power plant context. In addition, different perspectives on pedestal physics topics that are currently under debate within the community are also described, to provide guidance on needs for future research. Finally, attempts are made to describe conclusions from all of this progress consistent with discussions by the pedestal physics community at this time. The goal of this review is to provide a useful reference document for pedestal physics researchers going forward toward operation of a burning tokamak fusion plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Identification of Prompt Proton Emission in $N = Z - 1$ 61 Ga: Isospin Symmetry at the Limit of Nuclear Binding

Excited states in the proton drip line nucleus 61 Ga were populated via the fusion-evaporation reaction 24 Mg ⁢( 40 Ca, 𝑝⁢2⁢𝑛)⁢ 61 Ga. The experimental setup at Argonne National Laboratory comprised a novel combination of the Gammasphere array with two CD-shaped double-sided Si-strip detectors inside the Microball CsI(Tl) charged-particle detection array, as well as the Neutron-Shell liquid scintillators and the Fragment Mass Analyzer. Owing to the setup’s unprecedented in-beam proton spectroscopy and tracking capabilities, a coincidence between a 957.6(5)-keV 𝛾 ray and a 1.876(24)-MeV proton line was observed, which identifies the quasibound proton 𝜋⁢𝑔 9/2 single-particle state in 61 Ga at 𝐸 𝑥 = 2150⁢(34) keV. This probes isospin symmetry at the limit of nuclear binding by providing a unique challenge for the shell-model interpretation of mirror nuclei beyond doubly magic 56 Ni .

Hrabar, Yuliia [Lund Univ. (Sweden)] (ORCID:000000↗

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (i) the shallow-water equations, (ii) a two-dimensional cylinder flow, (iii) the wake of a NACA0012 airfoil with a Gurney flap, and (iv) the NOAA sea surface temperature data set. Furthermore, the present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow surrogate model but also systematically quantifies the uncertainty therein to assist with model interpretability.

42 ENGINEERING↗

From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Bolme, David↗

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Background Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. Results We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (“inertia”) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. Conclusions Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Mathematical & Computational Biology↗

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗

Linear viscoelastic measurements of commerical therapy putties for design and intuition

This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation. Additional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material. The collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.

C. Marsh, Maxwell↗

Phase changes in fluoropolymer binders

Motivation: Understanding fluoropolymer binder structure and aging response continues to be an important goal for the prediction and assessment of polymer bonded explosive compositions within the stockpile. As part of ongoing efforts within NNM, we are developing new methods to quantify crystallization and chemical degradation within aged Fluoropolymer materials. Significance: Time, temperature and radiation are known to alter the mechanical properties of fluoropolymers as crystalline domains nucleate and grow within the material. However, current methods do not accurately quantify and assess the degree or rapidity of these physical aging processes. For this reason, we are developing methods that directly measure crystalline domain, composition, size and shape and cross-correlating them with traditional techniques. Progress: Most recently, we thermally aged fluoropolymer binder, FK800, for various durations up to 23 days at several temperatures including: 25C (red), 35C (green), and 60C (blue). We used wide angle X-ray scattering (WAXS) to observe when crystalline peaks emerged, signaling a phase transition from amorphous to partially crystalline. Some of these data are summarized in Figure 1. As expected, samples held at 25C (red), which is below the glass transition temperature at 28C, remain amorphous – the WAXS data (Figure 1b) lacks sharp peaks indicative of crystallinity and the ultra-small angle X-ray scattering (USAXS) also has a smooth background without peaks indicative of structure (Figure 1a). By contrast, samples held at 35C and 60C have new peaks in both the USAXS and WAXS data. The structure is clearer after subtracting off the amorphous background from the WAXS data (Figure 1c). These data show that samples aged at 60C are more crystalline as determined by the more defined peaks and also have a larger lattice constant as seen by the shift in peak position as compared to the 35C samples. The corresponding USAXS data (Figure 1a) indicate that the size of the crystals was larger at 60C than at 35C as seen by the shift in the knee position to lower q-values. Interestingly, the shape of the scatter curve also changed, which indicates a change in morphology. Atomic force microscopy (AFM) was used to image the fluoropolymer (Figure 1c-e). At 35C, ~50nm long stacked crystal plates are observed. High resolution imaging shows that the stacks consist of 2-6 lamellae that are spaced approximately 3nm apart. At 60C, the individual lamella appear both thicker and longer and are intertwined to fill the surface. Morphological data such as these images help to interpret & model volume averaging techniques like X-ray scattering and NMR. We also expect the shift from isolated islands to intertwined clusters to impact mechanical properties.

36 MATERIALS SCIENCE↗

Sandia-UT Academic Alliance Project Summary

This project seeks to leverage various hyperspectral tensor products for the purposes of target classification/detection/prediction. In addition to hyperspectral, these products may be images, time series, geometries, or other modalities. The scenarios in which the targets of interest must be identified are typically from remote sensing platforms such as satellites. As such, there are numerous real-world constraints that drive algorithmic formulation. Cost, complexity, and feasibility of the algorithm should all be considered. Targets of interest are exceedingly rare, and collecting many data samples is prohibitively expensive. Furthermore, model interpretability is paramount due to the application space. The goal of this project is to develop a constrained supervised tensor factorization framework for use on hyperspectral data products. Supervised tensor factorizations already exist in the literature, although they have not seen widespread adoption in the remote sensing domain. The novelty of this project will be the formulation and inclusion of constraints that take into account mission considerations and physics based limits to learn a factorization that is both physically interpretable and mission deployable. This will represent a new contribution to the field of remote sensing for performing supervised learning tasks with hyperspectral data.

97 MATHEMATICS AND COMPUTING↗

Ex-Situ Surface Characterization Studies and Boundary Plasma Diagnostic Development for DIII-D (Final Report)

This report details the accomplishments for award DE-SC0016318, which sponsored collaborative research activities between the University of Tennessee-Knoxville and the DIII-D experiment at General Atomics, led by the PI (Donovan), which officially began on August 1, 2016 and ended July 31, 2020. Though the official start date for the award was August 1, 2016, the PI had already initiated collaborative activities with DIII-D during 2015 supported by internal UTK start-up funds utilized by the PI. This prior work enabled UTK to have a substantial role in the June 2016 Metal Rings Campaign (MRC). The DOE funds then provided the opportunity to expand the UTK team with funding for students and a postdoc to perform ex-situ analysis on the wide array of samples exposed during the MRC and develop more sophisticated analysis tools and interpretive modeling techniques.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗