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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

CFM-ID 4.0 – a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ~22% better and a compound identification accuracy that is ~35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ADAM: A web platform for graph-based modeling and optimization of supply chains

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Transitioning GlideinWMS, a multi domain distributed workload manager, from GSI proxies to tokens and other granular credentials

GlideinWMS is a distributed workload manager that has been used in production for many years to provision resources for experiments like CERN’s CMS, many Neutrino experiments, and the OSG. Its security model was based mainly on GSI (Grid Security Infrastructure), using X.509 certificate proxies and VOMS (Virtual Organization Membership Service) extensions. Even when other credentials, like SSH keys, were used to authenticate with resources, proxies were also added all the time, to establish the identity of the requestor and the associated memberships or privileges. This single credential was used for everything and was, often implicitly, forwarded wherever needed. The addition of identity and access tokens and the phase-out of GSI forced us to reconsider the security model of GlideinWMS, to handle multiple credentials which can differ in type, technology, and functionality. Both identity tokens and access tokens are supported. GSI proxies even if no more mandatory, are still used, together with various JWT (JSON Web Token) based tokens and other certificates. The functionality of the credentials, defined by issuer, audience, and scope, also differ: a credential can allow access to a computing resource, or can protect the GlideinWMS framework from tampering, or can grant read or write access to storage, can provide an identity for accounting or auditing, or can provide a combination of any the formers. Furthermore, the tools in use do not include automatic forwarding and renewal of the new credentials so credential lifetime and renewal requirements became part of the discussion as well. In this paper, we will present how GlideinWMS was able to change its design and code to respond to all these changes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Flexible visualization of a 3rd party Intrusion Prevention (Security) tool: A use case with the ELK stack

A difficult aspect of cyber security is the ability to achieve automated real time intrusion prevention across various sets of systems. To this extent, several companies are offering comprehensive solutions that leverage an "accuracy of scale" and moving much of the intelligence and detection on the Cloud, relying on an ever-growing set of data and analytics to increase decision accuracy. Often, they provide tools to visualize the decision workflows in attack prevention (as well as tune the algorithm) but those solutions are not always practical as companies see the problem as "global" that is, from a unified Cyber-security standpoint. However, a key to a successful Cyber-security program is transparency and trust: from an experimental team viewpoint, this specifically means having the ability to immediately see what and from where, who has been blocked and being able to inform the community in case of a revoked access without the need for filing a "ticket" (that may eventually be answered) – in other words, rapid response to their user-base is essential but solutions targeting "sub-groups" in an organization are not often available. We have come up with a versatile solution leveraging the ELK stack (Elasticsearch, Logstash, & Kibana) and an IPS (Intrusion Prevention System) based WAF (Web Application Firewall) from Signal Sciences. Signal Science allows the streaming of detailed logs in a Logstash format suitable for custom solutions for visualization. By combining these two tools, we have strengthened our security posture and enabled individual experiments to monitor their own traffic. Specifically, the IPS WAF provides unique data such as country of origin, protocol, response code, source IP, and paths accessed. In this contribution, we will show how we engineered a visualization solution so experiment groups could access a dashboard with predefined graphs but also, where they can create individual customizable dashboards used to display blocked traffic and troubleshoot latency issues. We will discuss the details and procedures for developing and configuring these tools and how it benefits cyber security postures across our scientific based environment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Contrary effects of phytoplankton Chlorella vulgaris and its exudates on mercury methylation by iron- and sulfate-reducing bacteria

Mercury (Hg) is a pervasive environmental pollutant and poses serious health concerns as inorganic Hg(II) can be converted to the neurotoxin methylmercury (MeHg), which bioaccumulates and biomagnifies in food webs. Phytoplankton, representing the base of aquatic food webs, can take up Hg(II) and influence MeHg production, but currently little is known about how and to what extent phytoplankton may impact Hg(II) methylation by itself or by methylating bacteria it harbors. This study investigated whether some species of phytoplankton could produce MeHg and how the live or dead phytoplankton cells and excreted algal organic matter (AOM) impact Hg(II) methylation by several known methylators, including iron-reducing bacteria (FeRB), Geobacter anodireducens SD-1 and Geobacter sulfurreducens PCA, and the sulfate-reducing bacterium (SRB) Desulfovibrio desulfuricans ND132 (or Pseudodesulfovibrio mercurii). Here our results indicate that, among the 4 phytoplankton species studied, none were capable of methylating Hg(II). However, the presence of phytoplankton cells (either live or dead) from Chlorella vulgaris (CV) generally inhibited Hg(II) methylation by FeRB but substantially enhanced methylation by SRB D. desulfuricans ND132. Enhanced methylation was attributed in part to CV-excreted AOM, which increased Hg(II) complexation and methylation by ND132 cells. In contrast, inhibition of methylation by FeRB was attributed to these bacteria incapable of competing with phytoplankton for Hg(II) binding and uptake. These observations suggest that phytoplankton could play different roles in affecting Hg(II) methylation by the two groups of anaerobic bacteria, FeRB and SRB, and thus shed additional light on how phytoplankton blooms may modulate MeHg production and bioaccumulation in the aquatic environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cybersecurity Value-at-Risk Framework

As more variable renewable energy sources are added to the grid, the role of hydropower as a reliable baseline and firming resource is growing more critical. However, the U.S hydropower fleet is not fully prepared to face modern issues such as cybersecurity threats. Hydropower accounts for 37% of U.S. utility-scale renewable electricity but is challenged by diverse infrastructure and legacy devices that predate modern security practices. While new cybersecurity solutions cannot simply be added to current hydropower generation and operation technologies, custom cybersecurity assessments can reveal system-specific threats and risk probabilities and identify mitigating enhancements.

cybersecurity valuation methodology↗
DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗
DOE OSTI2020

Simulations of coherent scattering experiments at storage ring synchrotron radiation sources in the hard x-ray range

Detailed simulations of experiments carried out at modern light sources are directly related to the most efficient and productive use of these facilities for research in multiple branches of science and technology. The “Synchrotron Radiation Workshop” computer code with its Python interface, and Sirepo web-browser-based graphical user interface, currently supports physical optics simulations of coherent X-ray scattering and imaging experiments on user-defined virtual samples. We present examples of simulations of coherent scattering experiments that are typically performed at the Coherent Hard X-ray beamline at Brookhaven National Laboratory’s (BNL) National Synchrotron Light Source II. We also present several comparisons of the simulations with the results of actual coherent X-ray scattering experiments with nano-fabricated test samples produced at BNL’s Center for Functional Nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

NOVA Trame Library

The nova-frame library provides a simple themed web application based on the Trame Framework

Watson, Gregory [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

Aquifer Injection Modeling (AIM) Toolbox User Guide

The Aquifer Injection Modeling Toolbox (“AIM Toolbox”) software was developed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Environmental Protection Agency’s (EPA) to provide a collection of analytical solutions suitable for evaluating the potential extent of the area impacted by subsurface injection operations. Subsurface injection operations are regulated under the EPA’s Underground Injection Control (UIC) program and are typically related to oil/gas development, waste disposal, or subsurface mining or storage. The analytical algorithms provided in the AIM Toolbox each have different approaches/assumptions/focus with respect to the nature and processes in subsurface and the nature of the injection operations. Collectively the set of analysis algorithms provides a broader evaluation of the area that can potentially be impacted by an injection operation. By providing estimates for the injectate plume extent, the software supports technical aspects of planning, evaluation, and overseeing injection activities. That is, the results help assess when further regulatory controls (e.g., monitoring, reporting) may be required and, in the case of disposal into an underground source of drinking water, the extent of the impacted area would require exemption from protection under the Safe Drinking Water Act. The AIM Toolbox software is available as a single-page web application, providing an interface to provide the necessary inputs, and both chart and map panes for visualizing the results. This User Guide describes the AIM Toolbox software, information required, user interactions, and the underlying basis of the calculations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Aquifer Injection Modeling (AIM) Toolbox User Guide

The Aquifer Injection Modeling Toolbox (“AIM Toolbox”) software was developed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Environmental Protection Agency’s (EPA) to provide a collection of analytical solutions suitable for evaluating the potential extent of the area impacted by subsurface injection operations. Subsurface injection operations are regulated under the EPA’s Underground Injection Control (UIC) program and are typically related to oil/gas development, waste disposal, or subsurface mining or storage. The analytical algorithms provided in the AIM Toolbox each have different approaches/assumptions/focus with respect to the nature and processes in subsurface and the nature of the injection operations. Collectively the set of analysis algorithms provides a broader evaluation of the area that can potentially be impacted by an injection operation. By providing estimates for the injectate plume extent, the software supports technical aspects of planning, evaluation, and overseeing injection activities. That is, the results help assess when further regulatory controls (e.g., monitoring, reporting) may be required and, in the case of disposal into an underground source of drinking water, the extent of the impacted area would require exemption from protection under the Safe Drinking Water Act. The AIM Toolbox software is available as a single-page web application, providing an interface to provide the necessary inputs, and both chart and map panes for visualizing the results. This User Guide describes the AIM Toolbox software, information required, user interactions, and the underlying basis of the calculations.

58 GEOSCIENCES↗
DOE OSTI2023

Cybersecurity Value-at-Risk Framework: Preprint

As more variable renewable energy sources are added to the grid, the critical role of hydropower as a reliable baseline and firming resource is rapidly growing. However, the U.S hydropower fleet is not fully prepared to face modern issues such as cybersecurity threats. Hydropower accounts for 37% of U.S. utility-scale renewable electricity but is challenged by diverse infrastructure and legacy devices that predate modern security practices. While new cybersecurity solutions cannot simply be added to current hydropower generation and operation technologies, custom cybersecurity assessments can reveal system-specific threats and risk probabilities and identify mitigating enhancements.

cybersecurity valuation↗
DOE OSTI2023

Integrating Artificial Intelligence into Science Gateways

Science gateways are altering the manner in which people interact with high performance computing (HPC) by providing a web browser based interface to advanced computing platforms. In particular, science gateways lower the barrier to using HPC by simplifying the process of submitting workloads to such systems and by offloading the efforts required to use HPC to the maintainers of the system. While science gateways decrease the time-to-science that comes with using such advanced systems, progress can still be made in improving the user's experience. In this paper we explore two strategies for integrating artificial intelligence tools commonly found in non-HPC service workflows: voice activated assistants and chatbots. Since August 2021, the HPC group at Idaho National Laboratory answers an average of 581 support tickets per month of which a large percentage could be addressed via these two strategies. This work defines the key capabilities that an HPC voice activated assistant and chatbot would need to address for a userbase consisting of largely non-expert users as well as a design for integration into the Open OnDemand science gateway.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automated Production of Optimization-Based Control Logics for Dynamic Facade Systems, with Experimental Application to Two-Zone External Venetian Blinds

The primary goal of this research is to devise a system that produces controllers for complex fenestration systems that perform nearly as well as Model Predictive Control but at a level of cost and implementation complexity that rivals simple heuristic controls. To this end, a cloud-based automated controller production system has been set up for a motorized external Venetian blind device, with a simple web interface that can be used by non-experts. The computation cost per controller is in the range of a few dollars, and the control logic is simple enough to be implemented on small and cheap distributed controllers. The web interface allows the user to specify some details of their particular building and window configuration, including orientation, latitude, interior geometries, and lighting and HVAC system parameters. Upon submittal, a cloud-based system configures the necessary files and commands, and then runs thousands of optimizations with them. Once the calculations are finished, the system produces a lookup table and interpolation-based controller scripts that can be used on a simple and cheap distributed controller. This paper describes the underlying models and optimization processes. It also describes the resulting control logics for two cases tested at Lawrence Berkeley National Laboratory’s Advanced Windows Testbed Facility: illuminance maximization subject to glare constraints; and lighting + HVAC energy minimization. The performance of the model-based controllers produced by the automated web-based system are compared to a heuristic ‘block beam’ controller in physical experiments at the Testbed. The experimental results are supplemented by simulation experiments with the same configuration as the Testbed. The results show the illuminance maximizing controller significantly outperforms the heuristic controller in terms of glare avoidance, and also outperforms it in terms of hours of daylight autonomy. The energy minimizing controller also outperforms the heuristic controller. This paper also discusses how the web-based system may be extended to consider other configurations, such as electrochromic windows and thermally massive HVAC systems. Potential roles for this type of system within the building design and construction industry are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗