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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeled sensitivity of multi-MA accelerator performance to electrode contaminant inventory

Significant particle-in-cell code development has enabled simulations of power flow in multi-MA accelerators to include the desorption of surface contaminants, their ionization into surface plasmas, and the impact of these plasmas on efficiency. The simulations base desorption on an Arrhenius equation, whose most significant unknown is the surface contaminant inventory. The sensitivity of power-flow simulations to this inventory is studied here using Sandia National Laboratories' Z accelerator with a 7-nH MagLIF load [Phys. Plasmas 17, 056303 (2010)]. Simulations are conducted in 3D cylindrical coordinates for the current-adder, or “convolute,” region of Z and in 2D for the final feed only. Simulated contaminant inventories are varied from 1 to 32 monolayers (MLs) in 2D, and 2 to 4 ML in 3D. The results reveal sensitivities to the local ratio of E/B⁠. The high B-field, low E-field region near the short-circuit load is insensitive to the contaminant inventory, where assumed values of 4–32 ML change the load current by ≤ 2%, and agree with experiment to within 2% at peak current. A 1-ML value is the outlier, increasing the load current by 5%, but still within measurement uncertainty. In contrast, the relatively higher E-field, lower B-field convolute region has slower contaminant desorption and higher-magnitude E-field penetration of the surface plasmas. The current loss in the convolute region does increase with contaminant inventory. The loss assuming 4 ML is 12% larger than for 2 ML, with 4 ML being the better match to experiment.

Arrhenius equation↗
DOE OSTI2022

Deceptive Infusion of Data: A Novel Data Masking Paradigm for High-Valued Systems

This work addresses how analysts of a high-valued system (e.g., nuclear reactor, aircraft turbine designs) can extract findable, accessible, interoperable, and reusable scientific data for public dissemination to artificial intelligence and machine-learning (AI/ML) researchers in a manner that cannot be reverse-engineered, potentially compromising sensitive or proprietary information. State-of-the-art methods address this problem through data masking techniques, which allow access to a subset of the information while obfuscating private and potentially identifying information (e.g., personally identifying medical data). These methods are unsuitable for industrial engineering processes, where AI/ML tools need explicit access to all the data available to draw the best inference about the system to help optimize its performance and identify its vulnerabilities, etc. Our novel deceptive infusion of data paradigm provides a solution to this conundrum by developing a mathematical approach capable of concealing the identity of the system while providing full access to all the features employed by AI/ML tools to ensure their optimal performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Quantum Approach for Implementing Fixed-Point Arithmetic in Solving Ordinary Differential Equations

Differential equations (DEs) serve as fundamental tools in mathematical modeling across scientific disciplines, yet classical numerical solvers face limitations with large-scale or computationally intensive problems. This study explores a quantum-inspired approach to solving DEs, combining quantum-inspired techniques with classical methods. It focuses on fixed-point arithmetic on quantum circuits, utilizing basic quantum gates to manipulate DE solutions. We expand upon the techniques introduced by Zanger et al. [Quantum, 5, 502 (2021)] by offering a precise computation for a fixed-point signed multiplication scheme, while also presenting a quantum circuit capable of executing the fixed-point division algorithm. We demonstrate the feasibility of our approach through the simulation of a linear Ordinary Differential Equation (ODE), where initial conditions and parameters are encoded into quantum circuits using fixed-point representation. By executing sequences of quantum gates mimicking numerical integration steps, we obtain approximate solutions to the ODE with specified fixed-point precision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xJR NEON Jornada LTER (JORN)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xJR NEON Jornada LTER (JORN). This is the FLUXNET version of the carbon flux data for the site US-xJR NEON Jornada LTER (JORN) produced by applying the standard ONEFlux (1F) software. Site Description - Land use at Jornada has contrasting livestock grazing histories (including ungrazed lands), dating back to 1902, which are representative of the pervasive land use in desert-southwest wildlands. Historical accounts of the region report significant changes in vegetation starting in the late 1800s coincident with the expansion of livestock grazing. By 1912, sufficiently dramatic changes lead area scientists and private landowners to convince the U.S. government to set aside Public Domain Lands for the Jornada Range Reserve for the purpose of scientific investigation on shrub invasion and subsequent loss of forage grasses. Much of this early research focused on quantifying utilization levels for forage species, developing livestock production strategies to deal with drought, and developing methods for shrub control and grass recovery. Exclosures were constructed and long-term plots were established throughout the range to monitor the continued expansion of shrubs across the landscape. Over the decades, numerous trials of various remediation approaches were implemented, from manual and mechanical shrub removal to herbicide application to construction of terraces or other means of redirecting surface flow of runoff.

Network), NEON (National Ecological Observatory↗
NASA NTRS1992

A self-consistent field method for galactic dynamics

The present study describes an algorithm for evolving collisionless stellar systems in order to investigate the evolution of systems with density profiles like the R exp 1/4 law, using only a few terms in the expansions. A good fit is obtained for a truncated isothermal distribution, which renders the method appropriate for galaxies with flat rotation curves. Calculations employing N of about 10 exp 6-7 are straightforward on existing supercomputers, making possible simulations having significantly smoother fields than with direct methods such as tree-codes. Orbits are found in a given static or time-dependent gravitational field; the potential, phi(r, t) is revised from the resultant density, rho(r, t). Possible scientific uses of this technique are discussed, including tidal perturbations of dwarf galaxies, the adiabatic growth of central masses in spheroidal galaxies, instabilities in realistic galaxy models, and secular processes in galactic evolution.

Hernquist, Lars↗
NASA NTRS1994

Capillary electrophoresis: Biotechnology for separation of DNA and chromosomes

Electrophoresis has been used for the separation of particles, ions, and molecules for a number of years. The technology for separation and detection of the results has many applications in the life sciences. One of the major goals of the scientific community is to separate DNA molecules and intact chromosomes based upon their different lengths or number of base pairs. This may be achieved by using some of the commercially available and widely used methods, but these processes require a considerable amount of time. The challenge is to achieve separation of intact chromosomes in a short time, preferably in a matter of minutes.

Williams, George O., Jr.↗
NASA NTRS2000

The Interplay Between Hardware and Control System Design in the Development of the Active Rack Isolation System

A primary mission of the International Space Station (ISS) is to provide a premier microgravity laboratory environment for conducting acceleration sensitive scientific research. In order to accomplish this goal, vibroacoustic disturbances caused by station activities that occur during the microgravity mode of operation, must be controlled. In addition to source isolation and other passive isolation methods, the ISS uses active isolation at the receiver, through the use of an Active Rack Isolation System (ARIS), as part of its overall vibration isolation strategy. A schematic diagram of a typical ARIS payload rack is shown. The ARIS isolation control system senses rack acceleration via three triaxial accelerometer heads and uses eight pushrod actuators to perform active vibration attenuation. Position sensors housed in the actuator assembly are used to sense the relative position between the rack and the station. Electrical power, data and other essential resources are routed through a set of umbilicals that interface with a passthrough panel at the bottom of the rack. A representative umbilical set is shown.

Fialho, Ian J.↗
NASA NTRS1999

An ecologic study of dietary links to prostate cancer

BACKGROUND: The etiology of prostate cancer has not been fully resolved in the scientific and medical literature, although the non-fat portion of milk and calcium are emerging as leading dietary risk factors, with lycopene (found in tomatoes) and vitamin D apparently being risk reduction factors. METHODS: The ecologic (multi-country statistical) approach is used to study dietary links to prostate cancer. Mortality data from 1986 for various age groups in 41 countries are compared with national consumer macronutrient supply values for 1983 and tomato supply values for 1985. RESULTS: For 28 countries with more than five Kcal/day of tomatoes in the consumer supply, a linear combination of non-fat milk (risk factor) and tomatoes (risk reduction factor) was found to have the highest statistical association with prostate cancer mortality rates for men over the age of 35, with the Pearson regression coefficient (R2) for those aged 65-74 years = 0.67 and p < 0.001. For the 13 countries with fewer than six Kcal/day of tomatoes, non-fat milk had the highest association (R2 = 0.92, p < 0.001 for men aged 65-74 years). For 41 countries combined, the non-fat portion of milk had the highest association with prostate cancer mortality rates (R2 = 0.73, p < 0.001 for men aged 65-74 years). CONCLUSIONS: These results support the results of several cohort studies which found the non-fat portion of milk to have the highest association with prostate cancer, likely due to the calcium, and tomatoes to reduce the risk of prostate cancer, most likely due to lycopene.

Diet/adverse effects↗
NASA NTRS2017

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗
NASA NTRS2018

System and Method of Optical Axis Alignment Monitor and Feedback Control for a Spectrometer

The present invention relates to an apparatus and method of a real-time, monitoring and control feedback system for a 2-D spectrometer application, to correct for active optical axis pointing misalignments or jitter (i.e., tip, tilt), that result in degraded scientific image integrity, unwanted spatial crosstalk and image blurring artifacts which severely limit the applications for high resolution spectrometer image data. The present invention provides a unique system architecture which ensures the most direct optical axis motion detection and control capability that will enable sub-pixel image motion monitoring and boresight control stability, thus, maximizing the science image quality.

Marx, Catherine↗
NASA NTRSDate not supplied

Power Measurements to Excavate Lunar Soil Simulant GRC-3B Using Arc Backhoe Trajectories

Understanding, mitigating, and minimizing power and mass requirements are crucial to developing the next generation excavation technologies for lunar and planetary in-situ resource utilization (ISRU). Towards this end, electrical power to the Advanced Planetary Excavator (APEX) was measured while excavating granular lunar soil simulant GRC-3B. Using a 21.6-cm wide bucket having a 30° leading edge, arc-shaped backhoe trajectories were dug to depths of 5, 10, and 20 cm with rake angles of 10, 20, and 30 degrees. Maximum net power, net energy, net energy per mass excavated, rate of mass excavated, horizontal distance from soil entry to exit (at bucket tip), and the trajectory to bucket volume percentage are presented to inform the scientific community as trajectories and designs are planned for future missions. Using APEX’s capability to evaluate power and energy consumption of excavation tools and processes gives guidance to developing power requirements and efficient methods for lunar excavation.

Excavation↗
NASA NTRS1999

The Importance of Meteorological Measurements for the Scientific Study of the Mesosphere

The rocket-borne falling sphere technique has been providing temperature and wind measurements to 90 km. For example, the HRDI experiment on the Upper Atmosphere Research Satellite (UARS) and during the GUARA Campaign. The measurement of temperature and wind using the falling sphere method is well known, but will be briefly reviewed. The vertical resolution of the data is examined at different geographical locations and seasons. Time/height sections characterizing the temperature and wind structure will be presented for low (Alcantara, Brazil), middle (Wallops Island and Barking Sands, Hawaii), and high latitudes (ARR and ESRANGE). We address radar quality required, data smoothing, and expected measurement accuracy. Special emphasis will be given to the data gathered during the GUARA campaign, especially small- and large-scale motions including transports of momentum and heat. In the GUARA campaign, the combined use of small meteorological payloads to measure large scale temperature and wind variations coupled with measurements of small scale turbulence from the larger rockets provided clear cut evidence of gravity wave breaking in the 85 to 90 km region.

Schmidlin, F. J.↗
NASA NTRS2012

Vesta and the HED Meteorites: Comparison of Spectral Properties

We present the main results obtained comparing the visible-near infrared (VIS-NIR) spectra Vesta s surface with howardites, eucrites, diogenites (HEDs). HEDs are commonly associated with Vesta based on spectral similarities. Because of such association, much effort is being made to merge the information from HEDs as well as Vestoids with that from Vesta to characterize the lithologic diversity of the surface of this asteroid and to infer clues regarding its thermal history. The Dawn spacecraft, orbiting around Vesta since July 2011, is performing detailed observations of this body and thus improving our knowledge of its properties. Dawn s scientific payload includes an imaging spectrometer, VIR-MS, sensitive to the VIS-NIR spectral range. VIR-MS began acquiring spectra during the approach phase that started in May 2011 and will continue its observations through July 2012 when the spacecraft will depart Vesta to travel to Ceres. The observations are uniformly distributed in latitude and longitude, allowing a global view of Vesta s crustal spectral properties. Using the information provided by VIR spectra, we studied the distribution of the spectral heterogeneities on the surface and used our findings to perform a comparison with HED spectra in the VIS-NIR spectral range searching for analogies and/or incompatibilities. In our analysis, we utilized a method to compare the results obtained at microscopic scale on HED samples and the one obtained at macroscopic scale on the surface of Vesta. The intent of this study is to improve our understanding of the connection between Vesta and the HEDs, which is one of the primary Dawn scientific objectives. Dawn VIR spectra are characterized by pyroxene absorptions and most of the surface materials exhibit howardite-like spectra. However, some large areas can be interpreted to be material richer in diogenite (based on pyroxenes band depths and band centers) and some others like eucrite-rich howardite terrains. In particular, VIR data strongly indicate in the south polar region (Rheasilvia) the presence of Mg-pyroxene-rich terrains. The hypothesis that Vesta is the HED parent body is consistent with, and strengthened by, the geologic and spectral context for pyroxene distribution provided by VIR on Dawn.

Ammannito, E.↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2025

Counterpart identification and classification for eRASS1 and characterisation of the active galactic nuclei content

Context. Accurately accounting for the Active Galactic Nucleus (AGN) phase in galaxy evolution requires a large, clean AGN sample. This is now possible with SRG/eROSITA, which completed its first all-sky X-ray survey (eRASS1) on June 12, 2020. The public Data Release 1 (DR1, Jan 31, 2024) includes 930,203 sources from the western Galactic hemisphere. Aims. The data enable the selection of a large AGN sample and the discovery of rare sources. However, scientific return depends on accurate characterisation of the X-ray emitters, requiring high-quality multi-wavelength data. This paper presents the identification and classification of optical and infrared counterparts to eRASS1 sources. Methods. Counterparts to eRASS1 X-ray point sources were identified using Gaia DR3, CatWISE2020, and Legacy Survey DR10 (LS10) with the Bayesian NWAY algorithm and trained priors. Sources were classified as Galactic or extragalactic via a machine-learning model combining optical/IR and X-ray properties, trained on a reference sample. For extragalactic LS10 sources, photometric redshifts were computed using CIRCLEZ. Results. Within the LS10 footprint, all 656,614 eROSITA/DR1 sources have at least one possible optical counterpart; ∼570 000 are extragalactic and likely AGN. Half are new detections compared to AllWISE, Gaia, and Quaia AGN catalogues. Gaia and CatWISE2020 counterparts are less reliable, due to the survey’s shallowness and the limited amount of features available to assess the probability of being an X-ray emitter. In the Galactic plane, where the overdensity of stellar sources also increases the chance of associations, using conservative reliability cuts, we identified approximately 18 000 Gaia and 55 000 CatWISE2020 extragalactic sources. Conclusions. We have released three high-quality counterpart catalogues – plus the training and validation sets – as a benchmark for the field. These datasets have many applications, but in particular, they empower researchers to build AGN samples tailored for completeness and purity, accelerating the hunt for the Universe’s most energetic engines.

X-rays: general↗
DOE OSTI2022

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS

In this work, we investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the specification of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines classical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This finding corroborates the well-known fact from scientific computing that building as much structural information as is available into an algorithm can enhance its efficiency and/or accuracy.

97 MATHEMATICS AND COMPUTING↗