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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases

Background Large language models (LLMs) are increasingly used medicine for diverse applications including differential diagnostic support. The training data used to create LLMs such as the Generative Pretrained Transformer (GPT) predominantly consist of English-language texts, but LLMs could be used across the globe to support diagnostics if language barriers could be overcome. Initial pilot studies on the utility of LLMs for differential diagnosis in languages other than English have shown promise, but a large-scale assessment on the relative performance of these models in a variety of European and non-European languages on a comprehensive corpus of challenging rare-disease cases is lacking. Methods We created 4917 clinical vignettes using structured data captured with Human Phenotype Ontology (HPO) terms with the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema. These clinical vignettes span a total of 360 distinct genetic diseases with 2525 associated phenotypic features. We used translations of the Human Phenotype Ontology together with language-specific templates to generate prompts in English, Chinese, Czech, Dutch, French, German, Italian, Japanese, Spanish, and Turkish. We applied GPT-4o, version gpt-4o-2024-08-06, and the medically fine-tuned Meditron3-70B to the task of delivering a ranked differential diagnosis using a zero-shot prompt. An ontology-based approach with the Mondo disease ontology was used to map synonyms and to map disease subtypes to clinical diagnoses in order to automate evaluation of LLM responses. Findings For English, GPT-4o placed the correct diagnosis at the first rank 19.9% and within the top-3 ranks 27.0% of the time. In comparison, for the nine non-English languages tested here the correct diagnosis was placed at rank 1 between 16.9% and 20.6%, within top-3 between 25.4% and 28.6% of cases. The Meditron3 model placed the correct diagnosis within the first 3 ranks for 20.9% of cases in English and between 19.9% and 24.0% for the other nine languages. Interpretation The differential diagnostic performance of LLMs across a comprehensive corpus of rare-disease cases was largely consistent across the ten languages tested. This suggests that the utility of LLMs in clinical settings may extend to non-English clinical settings.

Artificial intelligence↗
DOE OSTI2024

Leveraging Structures in Fault Diagnosis for Lithium-Ion Battery Packs

Lithium-ion battery systems consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery systems is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach that distinguishes itself by leveraging informative structures inherent in battery systems such as architecture, uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery systems.

Farakhor, Amir↗
DOE OSTI2025

Active multi-mode data analysis to improve fault diagnosis in AHUs

Faults in heating, ventilation and air conditioning systems can lead to increased energy consumption, occupant comfort issues, and reduced equipment lifetime. Commercial fault detection and diagnosis (FDD) tools has been increasingly deployed in U.S. commercial buildings. While they are helping to achieve energy efficiency and operational reliability, there remain gaps in their fault diagnostic capabilities. The diagnostic results often contain multiple distinct candidate root causes (CRCs) or offer no insight into CRCs. This study developed a novel active rule-based multi-mode data analysis method to enhance diagnostic resolution by applying proven rule sets and additional new rules to data from multiple known operational modes. The proposed method was demonstrated using enhanced air handling unit performance assessment rule sets and validated with the simulated data of two air handling units. New metrics, namely, reduced number of CRCs and improvement ratio, were developed to quantify the improvement of fault diagnostic resolution. The validation results showed that the proposed method effectively reduced the number of CRCs in contrast to analyzing data solely for a single mode of operation. It achieved a median improvement ratio of 80% in 19 test cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
DOE OSTI2025

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Non-destructive electrochemical diagnosis of failure mechanisms in aqueous zinc batteries

The early detection of secondary reactions that affect the life and performance of zinc manganese oxide batteries requires a shift from conventional time-consuming and often destructive procedures to rapid lifetime-predictive techniques. In this work, an electrochemical approach is employed to elucidate independent signatures for four common types of failure mechanisms in zinc manganese dioxide (Zn||MnO2) batteries—namely, the loss of zinc inventory, the loss of active material at the cathode, electrolyte depletion, and increased cell impedance. Our findings, specific to coin cell configurations, reveal that each induced failure mechanism can be distinctively modeled and identified based on responses from the rest voltage and columbic-efficiency data for prompt detection. For instance, electrolyte depletion response manifests a distinctive abrupt (>80 %) decrease in columbic efficiency (CE) and charge-rest voltage (Vc) while the discharge-rest voltage remained constant at ~1.3 V. Furthermore, electrolyte rejuvenation of the cell increased the CE to >95 % and restored Vc from ~0.3 to >1.7 V. Recovery experiments and reference performance tests demonstrated consistency between electrochemical descriptors and their associated failure mechanisms. Further, the outcomes of this work provide valuable insights and data models for some of the dominant failure mechanisms present in zinc manganese battery chemistries, which are beneficial to accelerated early-lifetime diagnosis and advancement of Zn batteries development.

25 ENERGY STORAGE↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2026

A Physics-Aligned Multi-Domain Machine Learning Framework for Time-Localised Diagnosis of Power Electronics Faults

This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen testing reveals a critical failure in a baseline MLP. This is shown to arise from model selection based on validation accuracy. Using validation-loss-based selection restores correct unseen performance and improves confidence. Feature ablation shows that Fourier and wavelet features dominate, while computational analysis indicates that feature extraction, particularly HHT, governs runtime.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2025

Case Report: Differential diagnosis of hematuria in the emergency department: emphasizing double J stent-inferior vena cava fistula

Introduction Hematuria, a common clinical indicator of genitourinary tract pathology, arises from diverse etiologies including calculi, infections, malignancies, trauma, and iatrogenic causes. Initial evaluation requires hemodynamic assessment, identification of underlying causes, and urinary drainage optimization. This report highlights a rare case of iatrogenic hematuria secondary to double-J stent migration into the inferior vena cava. Case presentation A Chinese male presented with acute left flank pain and gross hematuria persisting for 4 h. Diagnostic imaging revealed a left ureteral stone, prompting double-J stent placement at a local hospital. Despite intervention, hematuria worsened, necessitating abdominal CT. Imaging identified proximal migration of the left double-J stent into the inferior vena cava, with no evidence of vascular injury. Due to concerns regarding inadequate drainage and infection risk, conservative management without catheter clamping was initiated prior to referral. Definitive treatment involved ureteroscopic stent removal under direct visualization at our institution, resulting in rapid symptom resolution. Conclusion This case emphasizes three critical clinical insights: (1) Persistent postoperative hematuria warrants consideration of iatrogenic causes, particularly following urologic device placement. (2) Imaging modalities, especially CT, are indispensable for detecting atypical stent migration. (3) Comprehensive history-taking must include prior urologic interventions to guide differential diagnosis. While double-J stent migration into major vessels remains exceptionally rare, its recognition prevents delayed management of potentially life-threatening complications. Clinicians should maintain heightened vigilance for device-related hematuria in patients with refractory symptoms post-procedurally, ensuring prompt imaging evaluation and multidisciplinary intervention when indicated.

Qi, Wenqi↗
DOE OSTI2026

Novel thermal energy storage component: Development, performance, and phase transition diagnosis

Thermal energy storage (TES) using phase change materials (PCMs) is a promising technology for capturing and storing excess thermal energy for later use. However, challenges such as poor heat transfer efficiency and a lack of modular, scalable designs have limited widespread adoption of TES in real-world applications. This study developed and evaluated modular brick-type and blade-type TES prototypes featuring an aluminum housing, an embedded serpentine coil for active or passive thermal exchange, and a cost-effective metal mesh to enhance PCM thermal conductivity. The blade-type TES achieved notable geometric efficiency, with a thickness-to-length ratio of 0.03 and a thickness-to-width ratio of 0.08, enabling highly compact and modular thermal storage suitable for space-constrained applications. The paper presents a detailed evaluation of the TES prototypes’ performance. The comparative analysis indicated that the TES prototypes provide a highly cost-effective, thermally optimized alternative for compact energy storage and load shifting. A novel diagnostic technique was also introduced: using a portable endoscope to capture real-time visualizations of PCM phase transitions inside the TES. This method provides critical insights into internal heat transfer mechanisms, identifies potential issues, and offers valuable support for optimizing the TES design and developing the control algorithm. Overall, the modular brick-type and blade-type TES designs demonstrated in this work provide a scalable, efficient, and economically viable solution for advancing TES across residential, commercial, and industrial sectors. The designs’ compact structure, enhanced thermal performance, and integrated diagnostic capabilities make them strong candidates for future deployment in energy-efficient systems.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
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 OSTI2024

Diagnosis of Alzheimer’s disease using plasma biomarkers adjusted to clinical probability

Abstract Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.

Cell Biology↗
DOE OSTI2025

Sequential spectral line analysis for accurate density and temperature diagnosis of laboratory opacity measurements

The accuracy of iron opacity calculated in stellar interiors has been questioned since the discovery of the “solar problem” and the discrepancies between the measured and modeled iron opacity reported in 2015. Experimental opacity benchmarks require accurate temperature and density measurements, which were inferred by analyzing tracer magnesium spectra in those experiments. Could the observed discrepancy be explained by insufficient accuracy in the inferred temperature, density, and their uncertainties? Previous analyses may have yielded biased results due to three limitations: (1) simultaneous multi-line fitting, (2) approximations in line-shape models, and (3) exclusion of certain spectral lines due to insufficient background characterization. Notably, the first issue is a common concern for many inversion methods, including Bayesian inferences. We present a refined analysis method that overcomes these limitations, applied to three categories of iron opacity experiments (Anchor 1, 2, and 3). In particular, the sequential fitting method yields unbiased results with more realistic uncertainties by accounting for line inconsistencies in the parameter uncertainties. The average electron temperature and density values are 162 ± 6 eV and (7.0 ± 1.9) × 10 21 cm −3 for six Anchor 1 experiments, 189 ± 7 eV and (3.4 ± 0.3) × 10 22 cm −3 for 21 Anchor 2 experiments, and 201 ± 6 eV and (4.8 ± 1.1) × 10 22 cm −3 for nine Anchor 3 experiments. These results show ∼4% temperature and ∼20% density reproducibility over a decade, which also aligns with the inferred parameter uncertainties. In conclusion, the resulting temperature and density uncertainties lead to a quasi-continuum iron opacity variation of ±4%–7% for wavelengths below 9.5 Å, which is insufficient to explain the significant model-data discrepancies reported in 2015.

Absorption spectroscopy↗