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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

P4: Portable Parallel Processing Pipelines for Interactive Information Visualization

We present P4, an information visualization toolkit that combines declarative design specification and GPU computing for building high-performance interactive systems. Most of the existing information visualization toolkits do not harness the power of parallel processors in today's mainstream computers. P4 leverages GPU computing to accelerate both data processing and visualization rendering for interactive visualization applications. P4's programming interface offers a declarative visualization grammar for rapid specifications of data transformations, visual encodings, and interactions. By simplifying the development of GPU-accelerated visualization systems while supporting a high degree of flexibility and customization for design specification, P4 narrows the gap between expressiveness and scalability in information visualization toolkits. Through a range of examples and benchmark tests, we demonstrate that P4 provides high efficiency for creating interactive visualizations and offers drastic performance improvement over current state-of-the-art toolkits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is a signature feature of a mature TC. Therefore, knowing the eyes’ locations and movements is crucial for both operational weather forecasts and climate risk assessments. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness (i.e., structural stability) of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. Herein this paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics (e.g., frequency, intensity, duration, latitudes with maximum intensity, and genesis) that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗
DOE OSTI2024

Estimating UV-B, UV-Erithemic, and UV-A Irradiances From Global Horizontal Irradiance and MERRA-2 Ozone Column Information

The ground ultraviolet (UV) solar radiation is relevant due to its impacts on plastics degradation (mainly UVA) and on human health (UVB and erithemic UV (UVE)). UV ground measurements are not as ubiquitous as the relatively common global horizontal irradiance (GHI) measurements. Three simple models that estimate the UVA, UVB, and UVE components of solar irradiance from GHI and ozone column information are locally adjusted and validated. Five one-minute datasets from three sites in southeastern South America and two in the United States are used for simultaneous solar irradiance and UV data. All sites correspond to temperate mid-latitude regions. Simultaneous atmospheric total ozone column information is obtained from the reanalysis modern-era retrospective analysis for research and applications (MERRA-2) database for each site. Aside from locally adjusted models, average models with a single set of coefficients are also evaluated. For instance, the best average model is able to estimate UVE with a typical uncertainty below 12% and mean biases between +-3%, relative to the average of the measurements. Similar results are reported for the UVB and UVA components. These results, which can be useful in regions with similar climate and geography, provide a simple way to estimate UV irradiance under all-sky conditions with known uncertainty. This is an alternative to global satellite-based UV estimates, which can have high uncertainties at specific locations. Because MERRA-2 information has a global coverage, when coupled with good satellite-based estimates for GHI, UV irradiances can be estimated by this method over a large territory.

environmental UV radiation↗
DOE OSTI2014

SU-E-J-179: Prediction of Pelvic Nodal Coverage Using Mutual Information Between Cone-Beam and Planning CTs

Purpose: To investigate if pelvic nodal coverage for prostate patients undergoing intensity modulated radiotherapy (IMRT) can be predicted using mutual image information computed between planning and cone-beam CTs (CBCTs). Methods: Four patients with high-risk prostate adenocarcinoma were treated with IMRT on a Varian TrueBeam. Plans were designed such that 95% of the nodal planning target volume (PTV) received the prescription dose of 45 Gy (N=1) or 50.4 Gy (N=3). Weekly CBCTs (N=25) were acquired and the nodal clinical target volumes and organs at risk were contoured by a physician. The percent nodal volume receiving prescription dose was recorded as a ground truth. Using the recorded shifts performed by the radiation therapists at the time of image acquisition, CBCTs were aligned with the planning kVCT. Mutual image information (MI) was calculated between the CBCT and the aligned planning CT within the contour of the nodal PTV. Due to variable CBCT fields-of-view, CBCT images covering less than 90% of the nodal volume were excluded from the analysis, resulting in the removal of eight CBCTs. Results: A correlation coefficient of 0.40 was observed between the MI metric and the percent of the nodal target volume receiving the prescription dose. One patient's CBCTs had clear outliers from the rest of the patients. Upon further investigation, we discovered image artifacts that were present only in that patient's images. When those four images were excluded, the correlation improved to 0.81. Conclusion: This pilot study shows the potential of predicting pelvic nodal dosimetry by computing the mutual image information between planning CTs and patient setup CBCTs. Importantly, this technique does not involve manual or automatic contouring of the CBCT images. Additional patients and more robust exclusion criteria will help validate our findings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-G-BRD-02: Characterizing Information Loss in a Sparse-Sampling-Based Dynamic MRI Sequence (k-T BLAST) for Lung Motion Monitoring

Purpose: Rapid MRI is an attractive, non-ionizing tool for soft-tissue-based monitoring of respiratory motion in thoracic and abdominal radiotherapy. One big challenge is to achieve high temporal resolution while maintaining adequate spatial resolution. K-t BLAST, sparse-sampling and reconstruction sequence based on a-priori information represents a potential solution. In this work, we investigated how much “true” motion information is lost as a-priori information is progressively added for faster imaging. Methods: Lung tumor motions in superior-inferior direction obtained from ten individuals were replayed into an in-house, MRI-compatible, programmable motion platform (50Hz refresh and 100microns precision). Six water-filled 1.5ml tubes were placed on it as fiducial markers. Dynamic marker motion within a coronal slice (FOV: 32×32cm{sup 2}, resolution: 0.67×0.67mm{sup 2}, slice-thickness: 5mm) was collected on 3.0T body scanner (Ingenia, Philips). Balanced-FFE (TE/TR: 1.3ms/2.5ms, flip-angle: 40degrees) was used in conjunction with k-t BLAST. Each motion was repeated four times as four k-t acceleration factors 1, 2, 5, and 16 (corresponding frame rates were 2.5, 4.7, 9.8, and 19.1Hz, respectively) were compared. For each image set, one average motion trajectory was computed from six marker displacements. Root mean square error (RMS) was used as a metric of spatial accuracy where measured trajectories were compared to original data. Results: Tumor motion was approximately 10mm. The mean(standard deviation) of respiratory rates over ten patients was 0.28(0.06)Hz. Cumulative distributions of tumor motion frequency spectra (0–25Hz) obtained from the patients showed that 90% of motion fell on 3.88Hz or less. Therefore, the frame rate must be a double or higher for accurate monitoring. The RMS errors over patients for k-t factors of 1, 2, 5, and 16 were.10(.04),.17(.04), .21(.06) and.26(.06)mm, respectively. Conclusions: K-t factor of 5 or higher can cover the high frequency component of tumor respiratory motion, while the estimated error of spatial accuracy was approximately.2mm.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Preparing Precollege Students for the Second Quantum Revolution with Core Concepts in Quantum Information Science

After the passage of the U.S. National Quantum Initiative Act in December 2018, the National Science Foundation (NSF) and the Office of Science and Technology Policy (OSTP) recently assembled an interagency working group and conducted a workshop titled “Key Concepts for Future Quantum Information Science Learners” that focused on identifying core concepts for future curricular and educator activities to help precollege students engage with quantum information science (QIS). Helping precollege students learn these key concepts in QIS is an effective approach to introducing them to the second quantum revolution and inspiring them to become future contributors in the growing field of quantum information science and technology as leaders in areas related to quantum computing, communication, and sensing. This paper is a call to precollege educators to contemplate including QIS concepts into their existing courses at appropriate levels and get involved in the development of curricular materials suitable for their students. Also, research shows that compare-and-contrast activities can provide an effective approach to helping students learn. Therefore, we illustrate a pedagogical approach that contrasts the classical and quantum concepts so that educators can adapt them for their students in their lesson plans to help them learn the differences between key concepts in quantum and classical contexts.

Education & Educational Research↗
DOE OSTI2025

Quantum information meets high-energy physics: input to the update of the European strategy for particle physics

Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy laboratory setups. The feasibility of these studies in the high-energy regime explored by particle colliders was only recently shown and has gathered the attention of the scientific community. For the range of particles and fundamental interactions involved, particle colliders provide a novel environment where quantum information theory can be probed, with energies exceeding by about 12 orders of magnitude those employed in dedicated laboratory setups. Furthermore, collider detectors have inherent advantages in performing certain quantum information measurements and allow for the reconstruction of the state of the system under consideration via quantum state tomography. Here, we elaborate on the potential, challenges, and goals of this innovative and rapidly evolving line of research and discuss its expected impact on both quantum information theory and high-energy physics.

Physics↗
DOE OSTI2024

Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings

Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.

Prakash, Anand Krishnan↗
DOE OSTI2011

Co-PylotDB - A Python-Based Single-Window User Interface for Transmitting Information to a Database

Co-PylotDB, written completely in Python, provides a user interface (UI) with which to select user and data file(s), directories, and file content, and provide or capture various other information for sending data collected from running any computer program to a pre-formatted database table for persistent storage. The interface allows the user to select input, output, make, source, executable, and qsub files. It also provides fields for specifying the machine name on which the software was run, capturing compile and execution lines, and listing relevant user comments. Data automatically captured by Co-PylotDB and sent to the database are user, current directory, local hostname, current date, and time of send. The UI provides fields for logging into a local or remote database server, specifying a database and a table, and sending the information to the selected database table. If a server is not available, the UI provides for saving the command that would have saved the information to a database table for either later submission or for sending via email to a collaborator who has access to the desired database.

Barnette, DanielW.↗
DOE OSTI2024

Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome

In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2012

RELIABILITY MODELS OF AGING PASSIVE COMPONENTS INFORMED BY MATERIALS DEGRADATION METRICS TO SUPPORT LONG-TERM REACTOR OPERATIONS

Paper describes a methodology for the synthesis of nuclear power plant service data with expert-elicited materials degradation information to estimate the future failure rates of passive components. This method should be an important resource to long-term plant operations and reactor life extension. Conventional probabilistic risk assessments (PRAs) are not well suited to addressing long-term reactor operations. Since passive structures and components are among those for which replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant; yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA presents challenges. Service data to quantify component reliability models are sparse, and this is exacerbated by the greater data demands of age-dependent reliability models. Another factor is that there can be numerous potential degradation mechanisms associated with the materials and operating environment of a given component. This deepens the data problem since risk-informed management of component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates metrics of materials degradation susceptibility with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by predicted long-term operational risk.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Markov Decision Processes for Intelligent, Risk-Informed Asset-Management Decision-Making

Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.

Grabaskas, David↗
DOE OSTI2025

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most state-of-the-art deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Frequency-bin photonic quantum information

Discrete frequency modes, or bins, present a blend of opportunities and challenges for photonic quantum information processing. Frequency-bin-encoded photons are readily generated by integrated quantum light sources, naturally high-dimensional, stable in optical fiber, and massively parallelizable in a single spatial mode. Yet quantum operations on frequency-bin states require coherent and controllable multifrequency interference, making them significantly more challenging to manipulate than more traditional spatial degrees of freedom. In this mini-review, we describe recent developments that have transformed these challenges and propelled frequency bins forward. Focusing on sources, manipulation schemes, and detection approaches, we introduce the basics of frequency-bin encoding, summarize the state of the art, and speculate on the field’s next phases. Given the combined progress in integrated photonics, high-fidelity quantum gates, and proof-of-principle demonstrations, frequency-bin quantum information is poised to emerge from the lab and leave its mark on practical quantum information processing—particularly in networking where frequency bins offer unique tools for multiplexing, interconnects, and high-dimensional communications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Paradigm for universal quantum information processing with integrated acousto-optic frequency beamsplitters

Frequency-bin encoding offers tremendous potential in quantum photonic information processing, in which a single waveguide can support hundreds of lightpaths in a naturally phase-stable fashion. This stability, however, comes at a cost: arbitrary unitary operations can be realized by cascaded electro-optic phase modulators and pulse shapers, but require nontrivial numerical optimization for design and have thus far been limited to discrete tabletop components. In this article, we propose, formalize, and computationally evaluate a new paradigm for universal frequency-bin quantum information processing using acousto-optic scattering processes between distinct transverse modes. We show that controllable phase matching in intermodal processes enables 2 × 2 frequency beamsplitters and transverse-mode-dependent phase shifters, which together comprise cascadable FRequency-transverse-mODe Operations (FRODOs) that can synthesize any unitary via analytical decomposition procedures. Modeling the performance of both random gates and discrete Fourier transforms, we demonstrate the feasibility of high-fidelity quantum operations with existing integrated photonics technology, highlighting prospects of parallelizable operations achieving 100% bandwidth utilization. Our approach is realizable with CMOS technology, opening the door to scalable on-chip quantum information processing in the frequency domain.

Lukens, Joseph M. [Purdue Univ., West Lafayette, I↗
DOE OSTI2012

Probing the Informational and Regulatory Plasticity of a Transcription Factor DNA–Binding Domain

Transcription factors have two functional constraints on their evolution: (1) their binding sites must have enough information to be distinguishable from all other sequences in the genome, and (2) they must bind these sites with an affinity that appropriately modulates the rate of transcription. Since both are determined by the biophysical properties of the DNA–binding domain, selection on one will ultimately affect the other. We were interested in understanding how plastic the informational and regulatory properties of a transcription factor are and how transcription factors evolve to balance these constraints. To study this, we developed an in vivo selection system in Escherichia coli to identify variants of the helixturn-helix transcription factor MarA that bind different sets of binding sites with varying degrees of degeneracy. Unlike previous in vitro methods used to identify novel DNA binders and to probe the plasticity of the binding domain, our selections were done within the context of the initiation complex, selecting for both specific binding within the genome and for a physiologically significant strength of interaction to maintain function of the factor. Using MITOMI, quantitative PCR, and a binding site fitness assay, we characterized the binding, function, and fitness of some of these variants. We observed that a large range of binding preferences, information contents, and activities could be accessed with a few mutations, suggesting that transcriptional regulatory networks are highly adaptable and expandable.

59 BASIC BIOLOGICAL SCIENCES↗