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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nanopolysaccharide Builder: A User-Friendly Tool for Atomistic Models of Polysaccharide-Based Nanostructures

Here, we introduce Nanopolysaccharide Builder (NPB), a user-friendly software tool designed to construct polysaccharide nanostructures─mainly those based on cellulose, chitin, and chitosan─using experimental data or user-defined parameters. NPB enables the generation of cellulose and chitin allomorphs with customizable biochemical topologies and also facilitates the construction of large bundles that replicate nanostructures found in biological support systems, including plant cell walls and arthropod cuticles. The software outputs atomic Cartesian coordinates in Protein Data Bank (PDB) format and also provides atom connectivity files in PSF and PARM formats, ensuring seamless integration with major molecular dynamics (MD) engines such as NAMD, CHARMM, GROMACS, AMBER, OpenMM, and LAMMPS. Built on an interactive visualization framework, NPB features a graphical user interface (GUI) and supports both macOS and Linux operating systems. By enabling detailed atomic-scale studies of polysaccharide evolution in extracellular matrices and cell walls of algae, bacteria, fungi, and plants, NPB is poised to advance AI-guided research in sustainable chemical development and biomass utilization.

Wan, Zhangmin [Univ. of British Columbia, Vancouve↗
DOE OSTI2025

Correlated spin-wave generation and domain-wall oscillation in a topologically textured magnetic film

Spin waves, or magnons, are essential for next-generation energy-efficient spintronics and magnonics. Yet, visualizing spin-wave dynamics at nanoscale and microwave frequencies remains a formidable challenge due to the lack of spin-sensitive, time-resolved microscopy. Here, in this study, we report a breakthrough in imaging dipole-exchange spin waves in a ferromagnetic film owing to the development of laser-free ultrafast Lorentz electron microscopy, which is equipped with a microwave-mediated electron pulser for high spatiotemporal resolution. Using topological spin textures, we captured the emission, propagation, reflection and interference of spin waves from spin anti-vortices under radio-frequency excitations. Remarkably, we show that spin-wave generation is closely tied to the oscillatory motion of specific magnetic domain walls, providing the missing link between wave emission and wall dynamics near magnetic singularities. This work opens new possibilities in magnonics, offering a nanoscopic view of spin dynamics via transmission electron microscopy and enabling controlled excitation via radio-frequency fields for exploring non-equilibrium states in magnetic and multiferroic systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1999

Unifying Model-Based and Reactive Programming within a Model-Based Executive

Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

Williams, Brian C.↗
NASA NTRS1987

A flight expert system (FLES) for on-board fault monitoring and diagnosis

The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher workloads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) is developed to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with airplane status information during the diagnostic process. The simulator is based on the Lockheed Advanced Concept System (ACS), a future generation airplane, and on the Boeing 737. A distinction between two types of faults, maladjustments and malfunctions, has led to two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to access abnormal situations during airplane flight. Malfunctions and maladjustments are handled separately, diagnosed using domain knowledge.

Ali, Moonis↗
DOE OSTI2025

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴ speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously previously impossible with conventional tools while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.

Pathak, Abhishek [Fermilab] (ORCID:000000021704208↗
DOE OSTI2025

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴× speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously—previously impossible with conventional tools—while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.

Pathak, Abhishek [Fermilab] (ORCID:000000021704208↗
DOE OSTI2026

The LCLStream Ecosystem for Multi-Institutional Dataset Exploration

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications – AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique contribution to the DOE Integrated Research Infrastructure (IRI) landscape. By creating a flexible, API-driven data request service, we address a significant need for high-speed data streaming sources for the X-ray science data analysis community. With the combination of data request API, mutual authentication web security framework, job queue system, high-rate data buffer, and complementary nature to facility infrastructure, the LCLStreamer framework has prototyped and implemented several new paradigms critical for future generation experiments.

Rogers, David [ORNL] (ORCID:0000000251871768)↗
DOE OSTI2025

Extending High-Level Synthesis with AI/ML Methods

Artificial Intelligence (AI) and Machine Learning (ML) methods provide significant opportunities of improving quality of results when performing high-level synthesis (HLS). For example, they can be used to model and predict metrics of the final design (e.g., area, considering aspects such as interconnect overhead for different device technologies), facilitating exploration when searching for the best design trade-offs. They can also enable identifying hidden correlations across the various phases of the synthesis and the various optimizations performed, identifying the most effective pipelines. Finally, in more general terms, bio-inspired heuristic algorithms can improve the design space exploration for the synthesis process in terms of time and quality of the result. This paper discusses opportunities and challenges to augment HLS with AI/ML using as example flow the SODA Synthesizer, an open-source hardware generation toolchain which includes SODA-OPT, a hardware/software partitioning and pre-optimization tool developed with the MLIR framework, and PandA-Bambu, a state-of-the art HLS tool. SODA interfaces with OpenROAD to provide a complete end-to-end toolchain.

artificial intelligence↗
NASA NTRS1981

Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment

Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.

Chapman, G. M.↗
NASA NTRS1984

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report.

Dominick, Wayne D.↗
DOE OSTI2025

Certifying almost all quantum states with few single-qubit measurements

Certifying that an n -qubit state synthesized in the laboratory is close to a given target state is a fundamental task in quantum information science. However, existing rigorous protocols applicable to general target states have potentially prohibitive resource requirements in the form of either deep quantum circuits or exponentially many single-qubit measurements. Here we prove that almost all n -qubit target states, including those with exponential circuit complexity, can be certified from only O ( n 2 ) single-qubit measurements. Given access to the target state’s amplitudes, our protocol requires only O ( n 3 ) classical computation. This result is established by a technique that relates certification to the mixing time of a random walk. Our protocol has applications for benchmarking quantum systems, for optimizing quantum circuits to generate a desired target state and for learning and verifying neural networks, tensor networks and various other representations of quantum states using only single-qubit measurements. We show that such verified representations can be used to efficiently predict highly non-local properties of a synthesized state that would otherwise require an exponential number of measurements on the state. We demonstrate these applications in numerical experiments with up to 120 qubits and observe an advantage over existing methods such as cross-entropy benchmarking.

information theory and computation↗
NASA NTRS2003

Software for Planning Scientific Activities on Mars

Mixed-Initiative Activity Plan Generator (MAPGEN) is a ground-based computer program for planning and scheduling the scientific activities of instrumented exploratory robotic vehicles, within the limitations of available resources onboard the vehicle. MAPGEN is a combination of two prior software systems: (1) an activity-planning program, APGEN, developed at NASA s Jet Propulsion Laboratory and (2) the Europa planner/scheduler from NASA Ames Research Center. MAPGEN performs all of the following functions: Automatic generation of plans and schedules for scientific and engineering activities; Testing of hypotheses (or what-if analyses of various scenarios); Editing of plans; Computation and analysis of resources; and Enforcement and maintenance of constraints, including resolution of temporal and resource conflicts among planned activities. MAPGEN can be used in either of two modes: one in which the planner/scheduler is turned off and only the basic APGEN functionality is utilized, or one in which both component programs are used to obtain the full planning, scheduling, and constraint-maintenance functionality.

Ai-Chang, Mitchell↗
DOE OSTI2025

PMDT: AI-Enabled Predictive Maintenance Digital Twins for Advanced Nuclear Reactors

Our team made substantial technical progress on various fronts during the course of the program. Multiple milestones were geared towards demonstrating the feasibility of machine learning based predictive maintenance digital twins towards reducing O&M costs, whereas some other milestones actually focused on identifying technical gaps and developing technologies such as humble AI to provide necessary robustness to the ML-based models. We were able to demonstrate in many cases that Machine learning-based methods can be successfully adapted for Nuclear plant environments especially for remote monitoring applications. Detailed analyses were carried out with plant and full scope simulation data along with capabilities of enhanced analytics to assess and set realistic expectations on cost reductions in O&M. These assessments are paving the way for investments towards reactor design improvements as well project planning for SMR projects as they develop and mature in the next few years. Technology developed under this program got direct visibility to GE Hitachi and their utility customers and resulted in positive intents to deploy some of the elements from design phase. The project additionally resulted in several reports, publications, software and data generation that will be useful in deployment and O&M services for BWRX300 fleets.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Immunity-Based Aircraft Fault Detection System

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

Dasgupta, D.↗
DOE OSTI2026

Work With Us to Advance Cybersecurity for the Current and Future Grid

The energy sector is undergoing rapid change. Advanced technologies and controls, digitally connected devices, and a mix of generation sources offer increased energy reliability and security, reduced energy costs, and enhanced grid operations. But they can also carry physical and cybersecurity risks that are not yet fully understood. With deep expertise in advanced energy technologies, the National Laboratory of the Rockies is examining the cybersecurity implications of evolving systems and developing cutting-edge tools, novel approaches, and innovative research capabilities to address sector-wide challenges. Together with our partners, we are working to achieve a more secure, defensible, and reliable grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov–Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage.} Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robustness and accuracy of the prediction intervals under various hyperparameter settings. We show that the conformal KAN predictions can be applied to recent extensions of KANs, including Finite Basis KANs (FBKANs) and multifideilty KANs (MFKANs). The results demonstrate the potential of our approaches to significantly improve the reliability and applicability of KANs in scientific machine learning.

• Artificial intelligence (AI) / machine learning ↗
NASA NTRSDate not supplied

Machine-Learning for Safety Critical Airborne Applications Part II: Case Study

The exceptional progress in the field of Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology in recent years provides historic opportunities for the aviation industry. Current certification standards for avionics were developed prior to the ML renaissance and have several fundamental incompatibilities with the ML technology. WG-114 is working hard to release a new standard as soon as possible but for now there is no recognized means of compliance for ML based systems even of low criticality. In this talk, we present the custom ML workflow that can be used comply with all objectives of the current certification standards for a low-criticality (DAL D and C) ML-based system. To illustrate the practical application of the custom ML workflow we present a case study of a system based on a Deep Neural Network (DNN) intended to detect and identify airport runway signs. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D and DAL C systems.

Johann Schumann↗
DOE OSTI2025

Designing Antifouling and Antimicrobial Interfaces: Structural Characterization using CryoEM, Automated Microscopy, and AI Image Segmentation

The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].

Williams, Alexis [ORNL] (ORCID:0000000252835822)↗