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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 OSTI2021

AI-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability

Focal Areas: (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Focal areas 2 and 3 have critical dependencies to the modernization described. Key benefits to the focal areas: (1) Modernized observatory framework capable of agile adaptive observation, (2) Advanced instrument and data tagging supporting AI data acquisition for assimilation or validation, and (3) Widespread data interoperability bridging Earth system prediction scales

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Normality of I-V Measurements Using ML

There is an increased interest in instrument-computing ecosystems (ICEs) that support science workflows empowered by AI-automated experiments and computations in diverse areas. In particular, electrochemistry ICEs are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell, as illustrated in Fig. 1. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. In particular, we developed Python wrappers for Application Programming Interfaces (APIs) of instrument commands and Pyro client-server modules that enable them to be executed from remote computers. The entire workflow is orchestrated by a Jupyter notebook running on a remote computer.

Al Najjar, Anees↗
DOE OSTI2016

A combined time-of-flight and depth-of-interaction detector for total-body positron emission tomography

Purpose: In support of a project to build a total-body PET scanner with an axial field-of-view of 2 m, the authors are developing simple, cost-effective block detectors with combined time-of-flight (TOF) and depth-of-interaction (DOI) capabilities. Methods: This work focuses on investigating the potential of phosphor-coated crystals with conventional PMT-based block detector readout to provide DOI information while preserving timing resolution. The authors explored a variety of phosphor-coating configurations with single crystals and crystal arrays. Several pulse shape discrimination techniques were investigated, including decay time, delayed charge integration (DCI), and average signal shapes. Results: Pulse shape discrimination based on DCI provided the lowest DOI positioning error: 2 mm DOI positioning error was obtained with single phosphor-coated crystals while 3–3.5 mm DOI error was measured with the block detector module. Minimal timing resolution degradation was observed with single phosphor-coated crystals compared to uncoated crystals, and a timing resolution of 442 ps was obtained with phosphor-coated crystals in the block detector compared to 404 ps without phosphor coating. Flood maps showed a slight degradation in crystal resolvability with phosphor-coated crystals; however, all crystals could be resolved. Energy resolution was degraded by 3%–7% with phosphor-coated crystals compared to uncoated crystals. Conclusions: These results demonstrate the feasibility of obtaining TOF–DOI capabilities with simple block detector readout using phosphor-coated crystals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI↗
DOE OSTI2026

Ultrasonic Resonance Techniques for Materials Research

Mechanical resonances are directly related to the physical behavior of a system at the bulk and microscopic levels. In materials science, resonant ultrasound spectroscopy (RUS) has long been a preferred nondestructive method to study mechanical resonances of solids and precisely measure quantitative material properties, namely elasticity. In recent years, advances in computational power and hardware have enabled RUS to be relevant for an increasing range of applications, such as advanced manufacturing. An extension of this technique, nonlinear RUS (NRUS), has been demonstrated to provide unmatched sensitivity to early-stage damage. NRUS was originally developed to probe geologic materials but has become a vital tool in nondestructive evaluation and materials research, offering a powerful means of quantifying and characterizing microstructural nonlinearity in a broad range of materials. This review summarizes recent developments and growth opportunities in RUS and NRUS techniques, modeling, and applications across a wide range of material systems including metals, composites, geomaterials, and explosives.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exact Coherent Structures and Phase Space Geometry of Preturbulent 2D Active Nematic Channel Flow

Confined active nematics exhibit rich dynamical behavior, including spontaneous flows, periodic defect dynamics, and chaotic “active turbulence.” Here, we study these phenomena using the framework of exact coherent structures, which has been successful in characterizing the routes to high Reynolds number turbulence of passive fluids. Exact coherent structures are stationary, periodic, quasiperiodic, or traveling wave solutions of the hydrodynamic equations that, together with their invariant manifolds, serve as an organizing template of the dynamics. We compute the dominant exact coherent structures and connecting orbits in a preturbulent active nematic channel flow, which enables a fully nonlinear but highly reduced-order description in terms of a directed graph. Using this reduced representation, we compute instantaneous perturbations that switch the system between disparate spatiotemporal states occupying distant regions of the infinite-dimensional phase space. Overall, our results lay the groundwork for a systematic means of understanding and controlling active nematic flows in the moderate- to high-activity regime.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Threshold magnitudes for a multichannel correlation detector in background seismicity

Colocated explosive sources often produce correlated seismic waveforms. Multichannel correlation detectors identify these signals by scanning template waveforms recorded from known reference events against "target" data to find similar waveforms. This screening problem is challenged at thresholds required to monitor smaller explosions, often because non-target signals falsely trigger such detectors. Therefore, it is generally unclear what thresholds will reliably identify a target explosion while screening non-target background seismicity. Here, we estimate threshold magnitudes for hypothetical explosions located at the North Korean nuclear test site over six months of 2010, by processing International Monitoring System (IMS) array data with a multichannel waveform correlation detector. Our method (1) accounts for low amplitude background seismicity that falsely triggers correlation detectors but is unidentifiable with conventional power beams, (2) adapts to diurnally variable noise levels and (3) uses source-receiver reciprocity concepts to estimate thresholds for explosions spatially separated from the template source. Furthermore, we find that underground explosions with body wave magnitudes m b = 1.66 are detectable at the IMS array USRK with probability 0.99, when using template waveforms consisting only of P -waves, without false alarms. We conservatively find that these thresholds also increase by up to a magnitude unit for sources located 4 km or more from the Feb.12, 2013 announced nuclear test.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-G-BRB-02: An Open-Source Software Analysis Library for Linear Accelerator Quality Assurance

Purpose: Routine linac quality assurance (QA) tests have become complex enough to require automation of most test analyses. A new data analysis software library was built that allows physicists to automate routine linear accelerator quality assurance tests. The package is open source, code tested, and benchmarked. Methods: Images and data were generated on a TrueBeam linac for the following routine QA tests: VMAT, starshot, CBCT, machine logs, Winston Lutz, and picket fence. The analysis library was built using the general programming language Python. Each test was analyzed with the library algorithms and compared to manual measurements taken at the time of acquisition. Results: VMAT QA results agreed within 0.1% between the library and manual measurements. Machine logs (dynalogs & trajectory logs) were successfully parsed; mechanical axis positions were verified for accuracy and MLC fluence agreed well with EPID measurements. CBCT QA measurements were within 10 HU and 0.2mm where applicable. Winston Lutz isocenter size measurements were within 0.2mm of TrueBeam’s Machine Performance Check. Starshot analysis was within 0.2mm of the Winston Lutz results for the same conditions. Picket fence images with and without a known error showed that the library was capable of detecting MLC offsets within 0.02mm. Conclusion: A new routine QA software library has been benchmarked and is available for use by the community. The library is open-source and extensible for use in larger systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

TU-D-209-02: A Backscatter Point Spread Function for Entrance Skin Dose Determination

Purpose: To determine the distribution of backscattered radiation to the skin resulting from a non-uniform distribution of primary radiation through convolution with a backscatter point spread function (PSF). Methods: A backscatter PSF is determined using Monte Carlo simulation of a 1 mm primary beam incident on a 30 × 30 cm × 20 cm thick PMMA phantom using EGSnrc software. A primary profile is similarly obtained without the phantom and the difference from the total provides the backscatter profile. This scatter PSF characterizes the backscatter spread for a “point” primary interaction and can be convolved with the entrance primary dose distribution to obtain the total entrance skin dose. The backscatter PSF was integrated into the skin dose tracking system (DTS), a graphical utility for displaying the color-coded skin dose distribution on a 3D graphic of the patient during interventional fluoroscopic procedures. The backscatter convolution method was validated for the non-uniform beam resulting from the use of an ROI attenuator. The ROI attenuator is a copper sheet with about 20% primary transmission (0.7 mm thick) containing a circular aperture; this attenuator is placed in the beam to reduce dose in the periphery while maintaining full dose in the region of interest. The DTS calculated primary plus backscatter distribution is compared to that measured with GafChromic film and that calculated using EGSnrc Monte-Carlo software. Results: The PSF convolution method used in the DTS software was able to account for the spread of backscatter from the ROI region to the region under the attenuator. The skin dose distribution determined using DTS with the ROI attenuator was in good agreement with the distributions measured with Gafchromic film and determined by Monte Carlo simulation Conclusion: The PSF convolution technique provides an accurate alternative for entrance skin dose determination with non-uniform primary x-ray beams. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-BRC-05: Stereotactic Radiosurgery Optimized with Orthovoltage Beams

Purpose: To achieve improved stereotactic radiosurgery (SRS) dose distributions using orthovoltage energy fluence modulation with inverse planning optimization techniques. Methods: A pencil beam model was used to calculate dose distributions from the institution’s orthovoltage unit at 250 kVp. Kernels for the model were derived using Monte Carlo methods as well as measurements with radiochromic film. The orthovoltage photon spectra, modulated by varying thicknesses of attenuating material, were approximated using open-source software. A genetic algorithm search heuristic routine was used to optimize added tungsten filtration thicknesses to approach rectangular function dose distributions at depth. Optimizations were performed for depths of 2.5, 5.0, and 7.5 cm, with cone sizes of 8, 10, and 12 mm. Results: Circularly-symmetric tungsten filters were designed based on the results of the optimization, to modulate the orthovoltage beam across the aperture of an SRS cone collimator. For each depth and cone size combination examined, the beam flatness and 80–20% and 90–10% penumbrae were calculated for both standard, open cone-collimated beams as well as for the optimized, filtered beams. For all configurations tested, the modulated beams were able to achieve improved penumbra widths and flatness statistics at depth, with flatness improving between 33 and 52%, and penumbrae improving between 18 and 25% for the modulated beams compared to the unmodulated beams. Conclusion: A methodology has been described that may be used to optimize the spatial distribution of added filtration material in an orthovoltage SRS beam to result in dose distributions at depth with improved flatness and penumbrae compared to standard open cones. This work provides the mathematical foundation for a novel, orthovoltage energy fluence-modulated SRS system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Toward perturbation theory methods on a quantum computer

Perturbation theory, used in a wide range of fields, is a powerful tool for approximate solutions to complex problems, starting from the exact solution of a related, simpler problem. Advances in quantum computing, especially over the past several years, provide opportunities for alternatives to classical methods. Here, we present a general quantum circuit estimating both the energy and eigenstates corrections that is far superior to the classical version when estimating second-order energy corrections. We demonstrate our approach as applied to the two-site extended Hubbard model. In addition to numerical simulations based on qiskit, results on IBM’s quantum hardware are also presented. Our work offers a general approach to studying complex systems with quantum devices, with no training or optimization process needed to obtain the perturbative terms, which can be generalized to other Hamiltonian systems both in chemistry and physics.

97 MATHEMATICS AND COMPUTING↗