An approachable problem for the Bayesian analysis of a variety of uncertainties in hierarchical physical models
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Validation/Uncertainty-Quantification↗
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Results for “RAJA”
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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Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems
Critical infrastructure networks, such as power and transportation networks, are often modelled as cyber-physical systems. With ever increasing complexity of these systems, there is a need for newer and more relevant metrics and design tools that will co-optimize the physical system components and control policies to guarantee resilience against cyber and natural threats. To this end, a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives has been developed. The capabilities of the developed co-design engine are demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.
42 ENGINEERING↗
High-Fidelity Computational Studies of Intermediate Pressure Capacitively Coupled Plasmas
This report discussed progress made in the development of a computational model for intermediate pressure capacitively coupled plasma discharge. A new full fidelity argon chemistry model has been developed that includes electron, argon ion, three lumped states for electronically excited species, and the background ground state argon. As such the mechanism has been validated over a wide range of intermediate pressure conditions from 100 mTorr to a few Torr. Uncertainties in the electron impact cross sections and the rate coefficients for the reactions are propagated through the model to establish uncertainties that emerge in the model due the reaction mechanism and its impact on the discharge properties. The model is therefore validated in face of uncertainties.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Sensor Enabled Modeling of Future Distribution Systems with Distributed Energy Resources
The objective of this project is to create an integrated systems approach which can enhance distribution system monitoring, observability, and control by harnessing the flexibility of DERs.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Innovative Technology for Continuous, Online (in situ) Monitoring of Corrosivity of Molten Salts to Prevent Catastrophic Failure of Solar Thermal Plants
This project aims to evaluate and develop the concept of optical basicity to solar molten salts, design an online monitoring system for implementation in commercial plants, and train a diverse workforce for the CSP industry. This is the final report.
14 SOLAR ENERGY↗
Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization
This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.
36 MATERIALS SCIENCE↗
Integration and Validation of Multi-Layer Mitigation Strategies for Cyber Physical Systems Resilience (RD2C Capstone I Project) (Technical Report)
This project demonstrated the effectiveness of RD2C-developed, resilience-driven control strategies on high-fidelity system models with high penetrations of inverter-based resources, under the impacts of unexpected natural events and cyber-physical attacks. Controller performance and overall system resilience were quantified using data from a comprehensive suite of scenarios, and provided a clear understanding on how to operate and deploy these mitigation strategies.
42 ENGINEERING↗
AI Model Benchmarking for Nonproliferation Applications: Steel Thread Benchmarking Task Force Technical Report (Rev. 2)
Steel Thread is a NA-22 venture that seeks to build trustworthy, reliable AI models that can be used in a wide variety of nonproliferation tasks. A key aspect of building these models is developing appropriate benchmarks and evaluation methods, which will enable the venture to identify and adapt models to provide the most value in the nonproliferation domain. Benchmarks must be relevant to key tasks in this domain, such as question answering, information retrieval, document summarization and classification, consensus analysis, and image and data analysis. This report 1) provides an overview of benchmark design, evaluation, and challenges; 2) reviews a variety of open benchmarks, with a focus on language models and tasks; and 3) identifies benchmarks that are most relevant to Steel Thread. This report is intended to serve as a basis for further efforts to classify and evaluate benchmarks and their correlation with success on nonproliferation-specific tasks. The Steel Thread venture has defined benchmarks to be a particular combination of a dataset (or datasets) and a metric (or metrics) conceptualized as representing one or more specific tasks or sets of abilities for a specific modality. It is adopted by a research community as a shared framework for comparing methods.1 It includes 1) Data: Labeled (a designated subset not used for training, which could be all the data), 2) Metric: A way to quantify performance, 3) Task/Ability: What the benchmark is testing, 4) Protocol: A structured and repeatable evaluation process, 5) Baseline/Reference Model: For comparison; could be statistical, rule-based, SME-derived, or another model, and 6) Maintenance Plan: to update with new information over time; important for long-term utility. For further clarity, the definition includes what a benchmark, in this context, is not. It is not a corpus of training data, specific to a model (it is intended to apply to a range of models), a universal evaluation of performance, a guarantee that the ‘top’ model on the leaderboard will be the best fit for every specific use case, an all-encompassing proof of a model’s universal quality, nor is it a one-size-fits-all measure of success. It does not cover every real-world constraint (like operational, ethical, or cost considerations), a systems integration test, or a unit test. This definition was inspired by and resulted from discussions within the Steel Thread Benchmarking Task Force. This group was formed to define what we would mean as a benchmark within Steel Thread but persisted as the need to develop a thorough understanding of the large and expanding existing benchmarking space. This technical report is a result of the group’s divide and conquer approach to exploring this space. The release of benchmarks might not be progressing as quickly as model development, but it is moving very fast, as many benchmarks quickly become saturated, when state-of-the-art models score so close to the benchmark’s ceiling that their results are virtually indistinguishable. At that point, the test no longer differentiates between new systems, so researchers usually stop reporting scores as the benchmark no longer informs about improvements from the next generation of models. In the OpenAI announcement of GPT-5, they reported results on six flagship public benchmarks (AIME 2025, SWE-bench Verified, Aider Polyglot, MMMU, HealthBench Hard, GPQA) but the full system-card covers roughly thirty-five separate evaluations, comprising hundreds of test task items in total. There have been some efforts to summarize benchmarks in specific fields, like for text-to-image generation, but these surveys have had a narrow methodology scope. Therefore, a comprehensive survey of all benchmarks or even all benchmarks that could be relevant to Steel Thread is outside of the scope of this report. We chose some specific benchmarks to investigate in detail.
97 MATHEMATICS AND COMPUTING↗
Reliable Integration of AI Data Centers at Scale – Analysis, Modeling and Synthetic Data Generation
This report analyzes the power consumption of large dynamic digital loads using the open-source MIT supercloud and SURF datasets. With an emphasis on the MIT data, we calculate important power consumption characteristics to help system operators improve generation planning and resource allocation. We also introduce a rudimentary model for generating synthetic load profiles.
97 MATHEMATICS AND COMPUTING↗
Ocean surface radiation measurement best practices
Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.
54 ENVIRONMENTAL SCIENCES↗
Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer
Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate the exact gradient of the log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), where obtaining samples is faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results of RBM trained using quantum annealing are compared with the CD-based method. The performance of the two approaches is compared with respect to the classification accuracies, image reconstruction, and log-likelihood results. The classification accuracy results indicate comparable performances of the two methods. Image reconstruction and log-likelihood results show improved performance of the CD-based method. It is shown that the samples obtained from quantum annealer can be used to train an RBM on a 64-bit “bars and stripes” dataset with classification performance similar to an RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer could be useful as it eliminates computationally expensive MCMC steps of CD.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory
X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.
59 BASIC BIOLOGICAL SCIENCES↗
Chlorophyll fluorescence is a potential indicator to measure photochemical efficiency in early to late soybean maturity groups under changing day lengths and temperatures
In this study, we employed chlorophyll a fluorescence technique, to indicate plant health and status in response to changing day lengths (photoperiods) and temperatures in soybean early and late maturity groups. Chlorophyll a fluorescence study indicates changes in light reactions in photosystem II. Experiments were performed for 3-day lengths (12.5, 13.5, and 14.5 h) and five temperatures (22/14°C, 26/18°C, 30/22°C, 34/26°C, and 40/32°C), respectively. The I-P phase declined for changing day lengths. Active reaction centers decreased at long day length for maturity group III. We observed that low temperatures impacted the acceptor side of photosystem II and partially impacted electron transport toward the photosystem I end electron acceptor. Results emphasized that higher temperatures (40/32°C) triggered damage at the oxygen-evolving complex and decreased electron transport and photosynthesis. We studied specific leaf areas and aboveground mass. Aboveground parameters were consistent with the fluorescence study. Chlorophyll a fluorescence can be used as a potential technique for high-throughput phenotyping methods. The traits selected in the study proved to be possible indicators to provide information on the health status of various maturity groups under changing temperatures and day lengths. These traits can also be deciding criteria for breeding programs to develop inbreed soybean lines for stress tolerance and sensitivity based on latitudinal variations.
59 BASIC BIOLOGICAL SCIENCES↗
Impact of ultraviolet-B radiation on early-season morpho-physiological traits of indica and japonica rice genotypes
Ultraviolet (UV)-B radiation is considered one of the major detrimental rays coming from the Sun. UV-B radiation has a harmful impact on plant growth and development. The effect of UV-B radiation was studied on 64 rice (Oryza sativa L.) genotypes during the vegetative season. An equal number of genotypes from the japonica (50%) and indica (50%) subspecies were phenotyped using the Soil-Plant-Atmosphere-Research (SPAR) units. The 10 kJ UV-B was imposed 12 days after planting (DAP) and continued for three weeks (21 d). Based on the combined ultraviolet-B radiation response index (CUVBRI) for each genotype, the 64 rice genotypes were classified into sensitive, moderately sensitive, moderately tolerant, and tolerant. Various shoot traits, such as plant height, tiller, and leaf numbers, were measured. We also studied critical root phenological traits like root volume, diameter, tips, and forks. Out of all the studied shoot traits, leaf area showed maximum reduction for both indica (54%) and japonica (48%). Among the root traits, root length decreased by negligible (1%) for indica as compared to japonica (5%), while root crossing and forks showed a maximum decline for japonica (37 and 42%), respectively. This study is timely, meaningful, and required because it will help breeders select a tolerant or sensitive rice line for better yield and production under abiotic stresses.
59 BASIC BIOLOGICAL SCIENCES↗
Magnetic Vortex and Hyperthermia Suppression in Multigrain Iron Oxide Nanorings
Single-crystal iron oxide nanorings have been proposed as a promising candidate for magnetic hyperthermia application because of their unique shape-induced vortex-domain structure, which supports good colloidal stability and enhanced magnetic properties. However, the synthesis of single crystalline iron oxide has proven to be challenging. In this article, we showed that chemically synthesized multigrain magnetite nanorings disfavor a shape-induced magnetic vortex-domain structure. Our results indicate that the multigrain Fe 3 O 4 nanorings with an average outer diameter of ~110 nm and an inner to outer diameter ratio of ~0.5 do not show a shape-induced vortex-domain structure, which was observed in the single-crystal Fe 3 O 4 nanorings of similar dimensions. At 300 Ks, multigrain magnetite nanorings showed an effective anisotropy field of 440 Oe, which can be attributed to its high surface area and intraparticle interaction. Both calorimetric and AC loop measurements showed a moderate inductive heating efficiency of multigrain magnetite nanorings of ~300 W/g at 800 Oe. Our results shed light on the magnetic ground states of chemically synthesized multigrain Fe 3 O 4 nanorings.
36 MATERIALS SCIENCE↗
Role of Magnetic Anisotropy on the Hyperthermia Efficiency in Spherical Fe3−xCoxO4 (x = 0–1) Nanoparticles
The use of magnetic nanoparticles in the treatment of cancer using alternating current hyperthermia therapy has shown the potential to replace or supplement conventional cancer treatments, radiotherapy and chemotherapy, which have severe side effects. Though the nearly spherical sub-10 nm iron oxide nanoparticles have their approval from the US Food and Drug Administration, their low heating efficiency and removal from the body after hyperthermia treatment raises serious concerns. The majority of magnetic hyperthermia research is working to create nanomaterials with improved heating efficiency and long blood circulation time. Here, we have demonstrated a simple strategy to enhance the heating efficiency of sub-10 nm Fe3O4 nanoparticles through the replacement of Fe+2 ions with Co+2 ions. Magnetic and hyperthermia experiments on the 7 nm Fe3−xCoxO4 (x = 0–1) nanoparticles showed that the blocking temperature, the coercivity at 10 K, and the specific absorption rate followed a similar trend with a maximum at x = 0.75, which is in corroboration with the theoretical prediction. Our study revealed that the heating efficiency of the Fe3−xCoxO4 (x = 0–1) nanoparticles varies not just with the size and saturation magnetization but also with the magnetocrystalline anisotropy of the particles.
36 MATERIALS SCIENCE↗
Tailoring the Magnetic and Hyperthermic Properties of Biphase Iron Oxide Nanocubes through Post-Annealing
Tailoring the magnetic properties of iron oxide nanosystems is essential to expanding their biomedical applications. In this study, 34 nm iron oxide nanocubes with two phases consisting of Fe3O4 and α-Fe2O3 were annealed for 2 h in the presence of O2, N2, He, and Ar to tune the respective phase volume fractions and control their magnetic properties. X-ray diffraction and magnetic measurements were carried out post-treatment to evaluate changes in the treated samples compared to the as-prepared samples, showing an enhancement of the α-Fe2O3 phase in the samples annealed with O2 while the others indicated a Fe3O4 enhancement. Furthermore, the latter samples indicated enhancements in crystallinity and saturation magnetization, while coercivity enhancements were the most significant in samples annealed with O2, resulting in the highest specific absorption rates (of up to 1000 W/g) in all the applied fields of 800, 600, and 400 Oe in agar during magnetic hyperthermia measurements. The general enhancement of the specific absorption rate post-annealing underscores the importance of the annealing atmosphere in the enhancement of the magnetic and structural properties of nanostructures.
Crystallography↗
Chloride-Induced Stress Corrosion Cracking of Friction Stir-Welded 304L Stainless Steel: Effect of Microstructure and Temperature
Dry storage canisters of used nuclear fuels are fabricated using SUS 304L stainless steel. Chloride-induced stress corrosion cracking (CISCC) is one of the major failure modes of dry storage canisters. The cracked canisters can be repaired by friction stir welding (FSW), a low-heat input ‘solid-phase’ welding process. It is important to evaluate the ClSCC resistance of the friction stir welded material. Stress corrosion cracking (SCC) studies were carried out on mill-annealed base materials and friction stir welded 304L stainless U-bend specimens in 3.5% NaCl + 5 N H2SO4 solution at room temperature and boiling MgCl2 solution at 155 °C. The engineering stress on the outer fiber of the FSW U-bend specimen was ~60% higher than that of the base metal (BM). In spite of the higher stress level of the FSW, both materials (FSW and BM) showed almost similar SCC failure times in the two different test solutions. The SCC occurred in the thermo-mechanically affected zone (TMAZ) of the FSW specimens in the 3.5% NaCl + 5 N H2SO4 solution at room temperature, while the stirred zone (SZ) was relatively crack-free. The failure occurred at the stirred zone when tested in the boiling MgCl2 solution. Hydrogen reduction was the cathodic reaction in the boiling MgCl2 solution, which promoted hydrogen-assisted cracking of the heavily deformed stirred zone. The emergence of the slip step followed by passive film rupture and dissolution of the slip step could be the SCC events in the 3.5% NaCl + 5 N H2SO4 solution at room temperature. However, the slip step height was not sufficient to cause passivity breakdown in the fine-grained SZ. Therefore, the SCC occurred in the partially recrystallized softer TMAZ. Overall, the friction-stirred 304L showed higher tolerance to ClSCC than the 304L base metal.
304L austenitic stainless steel↗