SEARCH · Search NASA

Results for “model diagnostics”

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.

SourceResearch connectionFrom yearThrough year

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

At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗

DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗

DOE OSTI2025

Optimizing Simulation Fidelity in Direct-Drive Inertial Confinement Fusion with Cassio

Recently, the National Ignition Facility (NIF) demonstrated that inertial confinement fusion (ICF) is capable to achieve thermonuclear (TN) ignition in the laboratory, making it a crucial method on the path to replicate the Sun’s power production mechanism on Earth. However, the physics governing the high-energy density environments is very complex and remains a challenge to fully understand and model. For example, dopants in the TN fuel are important diagnostic tools to extract the thermodynamic conditions of the plasma. However, if their concentration is chosen too high, they can significantly degrade the performance of an ICF capsule. In this study, we use the Los Alamos National Laboratory radiation-hydrodynamics code Cassio to model ICF implosions of capsules which contain deuterium fuel with high-Z dopants like Krypton and Argon from the high-Z campaign conducted 15 years ago. We focus on how chosen computational and physics parameters influence the implosion outcomes. By systematically changing the resolution of the computational mesh and the photon energies as well as modifying settings for the laser drive and TN fuel pre-heat effects, we assess the impact on experimentally measured performance metrics like neutron production from TN burn and x-ray emission during the implosion. Our results will help to improve the fidelity of simulations and guide future numerical studies and experimental designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗

DOE OSTI2026

Majorana Edge Modes in Isolated Wires

Topological superconductors are believed to host exotic quasiparticle excitations known as Majorana zero modes (MZMs), with much of the evidence based on BCS mean-field theory. The direct application of mean-field arguments is tenuous in finite, isolated systems relevant in some experiments. Here, we develop a new correlation-based method for identifying MZMs in interacting, number-conserving systems. Using the density matrix renormalization group, we study fermion number-conserving models with long-range interactions, which under periodic boundary conditions exhibit robust topological and nontopological superconductivity, tuned by the strength of interaction [Ortiz et al., Phys. Rev. Lett. 113, 267002 (2014)]. We find evidence that, on the topological side, Majorana edge modes appear in open chains, manifesting as the vanishing of the energy splitting between odd- and even-parity ground states with increasing system size. Additionally, off-diagonal two-point correlation functions show nonlocal, parity-dependent edge effects. These correlations reveal the spatial structure of Majorana modes in the many-body wave function. We show that the correlation diagnostic applies broadly, including to short-range interacting models, where topological superconductivity is more fragile due to the absence of a bulk excitation gap.

Thomas-Markarian, Jaden↗

DOE OSTI2026

Final Report: A Multi-Channel Fusion Product

The goal of this project was to measure charged fusion products from the d(d,p)t reaction in MAST-U plasmas as a function of time and position with good energy resolution using a system of up to six charged particle detectors. The data from this new diagnostic will make it possible to determine the neutral beam ion density profile as a function of R, z, and t with reduced model dependency and contribute new information to a global analysis of fast ion diagnostic data needed for the determination of the fast ion distribution function (velocity space tomography).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Hydrogen from low-density polyethylene via nonthermal plasma: Effects of energy density and process parameters

Nonthermal plasma processes are promising for the modular valorization of plastic waste, especially into hydrogen and carbon materials, due to their high intensity, lack of reliance on catalysts or consumables, and suitability to be directly powered by electricity. We investigate the production of hydrogen from low-density polyethylene (LDPE) as a plastic waste model using streamer Dielectric Barrier Discharge (sDBD) plasma in nitrogen at atmospheric pressure. Here, we examine the effects of process energy density (energy input per unit of feedstock mass), feedstock mass, and plasma intensity (electric voltage) on plasma properties, hydrogen yield and energy efficiency via gas chromatography, optical emission spectroscopy, and electrical diagnostics, together with reactor-scale and nonlinear electric circuit modeling. The characteristic temperature of free electrons in the sDBD plasma is approximately 15000 K (1.3 eV), and that of gas species 10 times lower, demonstrating strong thermal non-equilibrium that can lead to molecular bond scission via charged species impact rather than direct heating. Experimental results show that higher energy density leads to greater hydrogen production and diminishing energy efficiency, and that higher plasma intensity and larger feedstock mass lead to greater hydrogen yield due to higher plasma temperatures and enhanced energy fluxes to the feedstock.

08 HYDROGEN↗

DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

The Paschen Jump as a Diagnostic of the Diffuse Nebular Continuum Emission in Active Galactic Nuclei*

Photoionization modeling of active galactic nuclei (AGN) predicts that diffuse continuum (DC) emission from the broad-line region makes a substantial contribution to the total continuum emission from ultraviolet through near-infrared wavelengths. Evidence for this DC component is present in the strong Balmer jump feature in AGN spectra, and possibly from reverberation measurements that find longer lags than expected from disk emission alone. However, the Balmer jump region contains numerous blended emission features, making it difficult to isolate the DC emission strength. In contrast, the Paschen jump region near 8200 Å is relatively uncontaminated by other strong emission features. Here, we examine whether the Paschen jump can aid in constraining the DC contribution, using Hubble Space Telescope Space Telescope Imaging Spectrograph spectra of six nearby Seyfert 1 nuclei. The spectra appear smooth across the Paschen edge, and we find no evidence of a Paschen spectral break or jump in total flux. We fit multicomponent spectral models over the range 6800–9700 Å and find that the spectra can still be compatible with a significant DC contribution if the DC Paschen jump is offset by an opposite spectral break resulting from blended high-order Paschen emission lines. The fits imply DC contributions ranging from ~10% to 50% at 8000 Å, but the fitting results are highly dependent on assumptions made about other model components. These degeneracies can potentially be alleviated by carrying out fits over a broader wavelength range, provided that models can accurately represent the disk continuum shape, Fe ii emission, high-order Balmer line emission, and other components.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6

The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific decadal variability (PDV) and the Atlantic multidecadal variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic–Pacific interactions and to investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV to PDV when analyzing the complete 1900–2014 period and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV to AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, large-ensemble (LE) simulations showed better network similarity when PDV and AMV were out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistical metrics of climate variability to provide a powerful tool for climate model evaluation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Measurement and Simulation of the Magnetic Fields from a 555 Timer Integrated Circuit Using a Quantum Diamond Microscope and Finite-Element Analysis

Quantum diamond microscope (QDM) magnetic field imaging is an emerging interrogation and diagnostic technique for integrated circuits (ICs). To date, the ICs measured with a QDM have been either too complex for us to predict the expected magnetic fields and benchmark the QDM performance or too simple to be relevant to the IC community. In this paper, we establish a 555 timer IC as a “model system” to optimize QDM measurement implementation, benchmark performance, and assess IC device functionality. To validate the magnetic field images taken with a QDM, we use a spice electronic circuit simulator and finite-element analysis (FEA) to model the magnetic fields from the 555 die for two functional states. Furthermore, we compare the advantages and the results of three IC-diamond measurement methods, confirm that the measured and simulated magnetic images are consistent, identify the magnetic signatures of current paths within the device, and discuss using this model system to advance QDM magnetic imaging as an IC diagnostic tool.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Post-DTL Beam Delivery

Ensuring that the beam delivered from the upgraded Front-End (FE) meets the Key Performance Parameters (KPPs) at each user facility is critical to the success of the LANSCE Accelerator Modernization Project (LAMP). For a high-intensity, multi-user facility like LANSCE, compliance with beam loss and radiation thresholds is as important as the charge delivered to each target. While early LAMPF/LANSCE operations relied on iterative tuning to minimize losses from beam halo and tail particles, the new FE may introduce different beam distributions and loss modes—making predictive modeling essential. To manage this, the F2E (Front-End to End) effort is developing detailed particle-tracking models that reflect realistic beamline conditions, including halo formation and expected diagnostic readings. These "snapshot" simulations aim to benchmark live machine performance at a given moment. This will help quantify how beam quality from the new FE will propagate downstream through the facility. Only by validating these models can we confidently assess and mitigate the potential impacts of the LAMP FE on beam delivery. Post-DTL, the beam splits to serve five major user facilities. Historically, low-energy beam transport has been modeled using TRACE, and higher-energy sections with TRANSPORT. These have now been unified into MAD-X format and validated with codes such as Elegant, pyOrbit, XSuite, Impact-Z, and HPSim. The primary focus now is on accurate modeling of full particle distributions (including beam halo) as they traverse the accelerator and beamlines to each experimental station. All models are at various stages of validation with empirical data.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Improvement in the spatial resolution of heavy ion beam probe measurements through application of ion optics

A technique for more accurately modeling and improving the spatial resolution of heavy ion beam probe measurements is described. We use a set of particle trajectories to numerically determine the focusing properties of a complicated three-dimensional magnetic field and characterize those properties with a transfer matrix. We then modify the transfer matrix approach of traditional ion optics to include a parameter that describes the ionization location of the detected ions. The ion optics model calculated using this technique enables a more accurate description of the particle trajectories than previously feasible. The model also allows one to easily determine an initial beam focus that could be used during experimental operation to optimize the spatial resolution of measurements. The technique has been applied to the design of a heavy ion beam probe diagnostic for the Wendelstein 7-X stellarator and improvements in the modeled spatial resolution by a factor of about 2 over previous estimates are possible. As a result, the improved spatial resolution will enable measurements of plasma fluctuations with smaller wavelengths than would otherwise be possible.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. Here, we develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗

DOE OSTI2025

Predictive links between microbial communities and biological oxygen utilization in the Arctic Ocean

Microbial metabolism influences rates of net community production (NCP), exerting a direct biological control on marine oxygen and carbon fluxes. In the Arctic, it is increasingly important to understand and quantify this process, as ecological and oceanographic conditions shift due to changing climate. Here, we describe potential ecological links between pelagic microbial diversity and an NCP precursor, biological oxygen utilization, using machine learning and paired observations of community structure and metabolic activity from a seasonally and spatially variable transect of the Arctic Ocean (2019–2020 MOSAiC Expedition). Community structure was determined using 16S (prokaryotic) and 18S (eukaryotic) rRNA gene amplicon sequencing, and metabolic activity was derived from ΔO 2 /Ar. Using self-organizing maps, we identified clear successional patterns in observed microbial community structure that were seasonally driven in the upper ocean and vertically stratified with depth. Metabolic activity was also stratified, with a primarily net heterotrophic water column (median −1.5% biological oxygen saturation), excepting periodic oxygen supersaturation (maximum: 13.6%) within the mixed layer. Using DNA sequences as predictor variables, we then constructed a random forest regression model that reliably reconstructed biological oxygen concentrations (root mean squared error = 4.14 μmol kg −1 ). Top predictors from this model were from heterotrophic (bacteria) or potentially mixotrophic (dinoflagellate) taxa. These analyses highlight biologically driven diagnostic tools that can be used to expand biogeochemical datasets and improve the microbial perspectives and metabolisms represented in ecological models of net productivity and carbon flux in a changing Arctic Ocean.

Chamberlain, Emelia J. [Univ. of San Diego, San Di↗

DOE OSTI2020

Disruption Prediction and Avoidance in High Beta Long Pulse KSTAR Plasmas

DoE Grant DE-SC0016409 (Disruption Prediction and Avoidance in High Beta Long Pulse KSTAR Plasmas) supported a collaboration among Columbia University, the Princeton Plasma Physics Laboratory (PPPL), and the Plasma Science and Fusion Center (PSFC) at MIT to conduct research on the KSTAR tokamak at the National Fusion Research Institute (NFRI) in Korea to improve predictive capability for disruptions in tokamaks. This initiative included upgrades to kinetic diagnostics at KSTAR whose measurements form the basis for the disruption prediction models. The major thrust of the PPPL and PSFC work was implementing a capability at KSTAR to allow partially-polarized background light seen by the KSTAR Motional Stark Effect (MSE) diagnostic to be compensated by ‘wavelength interpolation’ rather than the traditional ‘beam modulation’ technique. This objective was completed on time and on budget, and the MSE Background Polychromator is currently functioning at KSTAR. Primary responsibility for maintaining and operating the MSE-BP was transferred to Nova Photonics, Inc. in 2019.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Physically-based control-oriented modeling for turbocharged stoichiometric spark-ignited engine with cooled EGR and flexible VVT systems

Accurate estimation and prediction of engine gas exchange system and in-cylinder conditions are critical for spark-ignited engine control and diagnostic algorithm development. In this paper, a physically-based, control-oriented model for a 2.8 l turbocharged, variable valve timing (VVT) and low pressure (LP) exhaust gas recirculation (EGR)-utilizing SI engine was developed. The model includes the impact of modulation to any combination of 10 actuators, including the throttle valve, compressor bypass valve, fueling rate, waste-gate, LP EGR valve, number of deactivated cylinders, intake valve open (IVO) timing, intake valve close (IVC) timing, exhaust valve open (EVO) timing and exhaust valve close (EVC) timing. The accuracy of the model in capturing engine dynamics was demonstrated by validating it against high-fidelity engine GT-Power simulation results for various drive cycles, particularly emphasizing elevated loads. In comparison to the open literature, novel contributions of the effort described in this paper includes in-cylinder gas composition modeling and turbine-out pressure estimation.

Zhang, Xu↗