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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Simple Construction of High Order Rational Iterative Equation Solvers

This article proposes a general technique to construct arbitrarily high order rational one-point iterative equation solvers based on truncated Taylor expansion from lower order schemes. With adding one more function call, an iterative equation solver of convergence order n can be accelerated to order (2n-1). Many existing (some recently published) one-point and two-point iterative equation solvers are special cases of the proposed construction. The proposed approach may be used to obtain new iterative equation solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Audits Made Simple

A company just got notified there is a big external audit coming in 3 months. Getting ready for an audit can be challenging, scary, and full of surprises. This Gold Paper describes a typical audit from notification of the intent to audit through disposition of the final report including Best Practices, Opportunities for Improvement (OFI), and issues that must be fixed. Good preparation can improve the chances of success. Ensuring the auditors understand the environment and requirements is paramount to success. It helps the auditors understand that the enterprise really does think that security is important. Understanding and following a structured process ensures a smooth audit process. Ensuring follow-up on OFIs and issues in a structured fashion will also make the next audit easier. It is important to keep in mind that the auditors will use the previous report as a starting point. Now the only worry is the actual audit and subsequent report and how well the company has done. “Virtualization, mobilization, and cloud technology have created new points of entry into business, leaving them vulnerable to covert cyber-attacks,” states a report by Ernst & Young. Executives are struggling with the potential for a data breach, and resolving this business risk is high on their agenda. While most audit committee members are financially solid, they lack knowledge of technology issues, making understanding of this risk difficult. Success with an audit helps management understand the risk management is accepting, highlight gaps in security, and generate ideas on how to improve. These results are an important aspect of managing the business risk.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Simple Correlation for Neutron Capture Rates from Nuclear Masses

Recent studies of neutron capture performed at LANL have revealed a previously unrecognized connection between nuclear masses and the average neutron capture cross section. A team of three scientists from Los Alamos (P-27), Yale Univ., and Istanbul Univ. (Turkey) recently discovered this connection and have published their results as a Rapid Communication in Physical Review C. Neutron capture is a reaction in which a free neutron is absorbed by the nucleus, keeping the element unchanged, but changing isotopes. This reaction is typically exothermic. As a result, the reaction can proceed even when many other reaction channels are closed. In an astrophysical environment, this means that neutron capture is the primary mechanism by which all of the elements with atomic number greater than nickel are produced is neutron capture.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Simple grab sampling methods for the measurement of WL(Rn) and WL(Tn) concentrations

If one is air sampling for short -lived radon ( 222 Rn) progeny only, then it is the so-called working level concentration (WL) that is normally determined. Often, a so-called short-term grab air sample is utilized followed by gross α counting techniques. Care on the part of health physics personnel is required here, as is an uninterrupted work time.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

A Simple Search for Sterile Neutrino Dark Matter

We have tested the feasibility of an experiment to search for keV sterile neutrino dark matter with superconducting radiation detectors. For this, we have implanted radioactive 7 Be nuclei into our detectors, which decay by electron capture with a half-life of 53 days. The electron neutrino produced in this decay escapes from the detector, and we can measure the recoil energy of the 7 Li daughter nucleus with high accuracy. The recoil spectrum consists of four peaks, two due to capture of a 1s electron (“K-capture”) and two due to capture of a 2s electron (“L-capture”) for decay into the 7 Li ground state and the 7 Li* excited state. If the electron neutrino contains some admixture of a keV sterile neutrino, the 7 Li recoil energy will be shifted and produce an additional peak in the spectrum whose energy depends on the sterile neutrino mass and whose amplitude depends on the neutrino admixture.

42 ENGINEERING↗
DOE OSTI2019

A simple json format for storing and exchanging code calculation and experimental results

Calculation codes and code systems give their results in a plethora of different formats and/or files. For instance, MCNP gives a user the MCNP specific output file, the mctal file, the ptrac file, etc. while PARTISN has a PARTISN specific output file and a number of binary files with various kinds of information about the calculation. However, the results we are interested in are always the same. These can be single values such as the effective multiplication factor k eff or the effective delayed neutron fraction β eff . It can be histogram data (such as particle spectra, reaction rates, sensitivity profiles, etc.) or even pointwise data (for example the nuclide composition as a function of time, etc.). It is also worthwhile to note that this would also apply to experimental data that we may wish to compare with as well. Calculation and experimental results can basically be split into two components: attributes (or metadata) and the actual result itself. Attributes represent information about the result that are not necessarily required for its’ interpretation but that are still good to have. Often, these attributes are what we will want to filter results on. This means that these attributes are often the answer to specific questions we may have about the result. The following is a non exhaustive list of such basic questions: what type of result is it: e.g. the effective multiplication factor k eff , the effective delayed neutron fraction β eff , etc.; which case does it relate to: e.g. a specific ICSBEP case such as PU-MET-FAST-001-001; which code and version produced this result: e.g. MCNP 6.2, etc.; which nuclear data library was used to obtain the result: e.g. ENDF/B-VIII.0 or JEFF 3.3; which nuclide and reaction does it apply to (for reaction rates and sensitivity profiles); which volume in the geometry was it calculated in? The second component of the calculation or experimental result is the actual numerical data of the result. It is this data that we want to compare, exchange, plot, interpret, etc. It consists of values with optional uncertainties and units. It also includes the underlying structure (if any structure is required to interpret the result). Depending on the type of result, it may be a single value (e.g. the effective multiplication factor), an array of values (e.g. a reaction rate or particle spectrum) or even a multidimensional array. In order to exchange results between different organisations, for instance for benchmarking, people often resort to spreadsheets or custom text files that need to be filled out. While this approach works well if little information is to be exchanged, it can become cumbersome when dealing with large amounts of data (like for instance particle spectra for thousands of cases). In addition, such an approach does not always capture the metadata we may be interested in to allow for automated processing of these files. The purpose of this report is to propose a standardised json structure to store any type of result and present an associated python interface to interact with these results directly. For such a structure to be useful, it must fulfill a specific set of functional requirements that we try to meet with this proposal. The format must allow for the calculation result to stand by itself. This means that we should not have to look in multiple places to be able to understand it. For example, if the result relates to a particle spectrum, the group structure in which the result is given should be stored with it. The format should be code agnostic. This means that we do not need to know where it came from to understand it. The format should be result type agnostic. This means that the format must be generic enough so that we can even use it for results we have not thought of yet. The format should be relatively easy to interact with through different scripting or programming languages (i.e. the machine interface) while still providing a clean structure that can be used directly by any human.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Simple Algorithms for a Physics Code Base

Project 1: Developing a container for state variables in a physics code. Project 2: Setting up near-continuous performance and usage metrics monitoring of a physics code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗