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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Next Generation Environmentally-Friendly Driving Feedback Systems Research and Development

The objective of this project is to design, develop, and demonstrate a next-generation, federal safety- and emission-complaint driving feedback system that can be deployed across the existing vehicle fleet and improve fleet average fuel efficiency by at least 2%. The project objective was achieved with the driving feedback system that encourages fuel-efficient vehicle travel and operation through: 1) Eco-Routing Navigation module that suggests the most fuel-efficient route from one stop to the next, 2) Eco-Driving Feedback module that provides sensible information, recommendation, and warning regarding fuel-efficient vehicle operation, and 3) Eco-Score and Eco-Rank module that provides a means for driving performance tracking, self-evaluation, and peer comparison. The system also collects and stores vehicle travel and operation data, which are used by Algorithm Updating module to customize the other modules for specific vehicles and adapts them to specific drivers over time. The driving feedback system was designed and developed as an aftermarket technology that can be retrofitted to vehicles in the existing fleet. It consists of a mobile application for smart devices running Android operating system, a vehicle on-board diagnostics connector, and a data server. While the system receives and utilizes real-time vehicle and engine data from the vehicle’s controller area network bus through the vehicle’s on-board diagnostic connector, it does not modify or interfere with the vehicle’s controller area network bus, and thus, is in compliance with federal safety and emission regulations. The driving feedback system was demonstrated and then installed on 45 vehicles from three different fleets for field operational test. These include 15 private vehicles of the general public, 15 pickup trucks of the California Department of Transportation that are assigned to individual employees for business use, and 15 shuttle buses of the Riverside Transit Agency that are used for paratransit service. Detailed vehicle travel and operation data including route taken, driving speed, acceleration, braking, and the corresponding fuel consumption, were collected both before and during the test period. The data analysis results show that the fleet average fuel efficiency improvements for the three fleets with the use of the driving feedback system are in the range of 2% to 9%. The economic viability of the driving feedback system is high. A fully deployed system would require capital investment in smart device ($150-$350) and on-board diagnostics connector ($50-$100) as well as paying operating costs for wireless data plan and subscription fees ($20-$30 per month) for connecting to the data server and receiving various system services. For individual consumers who already own a smart device (such as smartphone) and commercial fleets that already use some kind of telematics services, the costs for deploying this driving feedback system would be much lower.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Fast spark-detection system for GEM detectors

The sPHENIX experiment is currently under commissioning at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL). The Time Projection Chamber (TPC) serves as a tracking detector for the experiment. The sPHENIX TPC uses a stack of four Gas Electron Multipliers (GEMs) as a gain stage in a reduced ion back-flow configuration. To mitigate the damaging effects of sparks in the GEMs, an online spark monitoring system was created. Once the system detects a spark in a GEM stack, the voltages across the GEMs in that stack can be lowered to prevent further sparking without affecting the gain and efficiency of the other modules. Spark signals are coupled out of the GEM stack by a pick-off capacitor attached to the bottom of the bottom GEM. Custom PCBs convert the oscillatory spark signal into a mono-polar pulse that is then digitized. The software then saves the waveform in a server and uses experimentally derived thresholds to determine how to react. As a result, the system has so far proven to be effective at improving the stability of the TPC and preventing damaging events while collecting cosmic ray data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A New Integrated Analysis Suite for Fast-Ion Study in KSTAR

Here, an integrated workflow for fast-ion analysis was developed by adapting the One Modeling Framework for Integrated Task (OMFIT) workflow manager to support a standard and unified analysis platform for KSTAR users. The newly established analysis suite offers a graphical user interface–based workflow to enable users to readily access and handle experimental data archived in various data formats and servers. Further, users can analyze the data by importing modules designed for conducting certain tasks, such as profile fitting, equilibrium reconstruction, and postprocessing of tokamak data. The procedures for preparing the inputs for fast-ion simulations are streamlined by a common workflow manager, which enables the parallel processing of various tasks to efficiently analyze large fast-ion datasets. The OMFIT platform comprises a flexible Python-based application that enables users to freely manipulate the Python scripts for applications that are unavailable in the standard workflow. The framework also offers mapping tools to translate the output data into the Integrated Modeling and Analysis Suite format to maintain application compatibility for future ITER burning plasma experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Belle II Conditions Database

The Belle II experiment at KEK is preparing for taking first collision data in early 2018. For the success of the experiment it is essential to have information about varying conditions available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II Conditions Database was designed to make maintenance as easy as possible. To this end, a HTTP REST service was developed with industry-standard tools such as Swagger for the API interface development, Payara for the Java EE application server, and the Hazelcast in-memory data grid for support of scalable caching as well as transparent distribution of the service across multiple sites. On the client side, the online and offline software has to be able to obtain conditions data from the Belle II Conditions Database in a robust and reliable way under very different situations. As such the client side interface to the Belle II Conditions Database has been designed with a variety of access mechanisms which allow the software to be used with and without an internet connection. Different methods to access the payload information are implemented to allow for a high level of customization per site and to simplify testing of new payloads locally. Changes to the conditions data are usually handled transparently but users can actively check whether an object has changed or register callback functions to be called whenever a conditions data object is updated. In addition a command line user interface has been developed to simplify inspection and modification of the database contents.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1997

Opus: A Coordination Language for Multidisciplinary Applications

Data parallel languages, such as High Performance fortran, can be successfully applied to a wide range of numerical applications. However, many advanced scientific and engineering applications are multidisciplinary and heterogeneous in nature, and thus do not fit well into the data parallel paradigm. In this paper we present Opus, a language designed to fill this gap. The central concept of Opus is a mechanism called ShareD Abstractions (SDA). An SDA can be used as a computation server, i.e., a locus of computational activity, or as a data repository for sharing data between asynchronous tasks. SDAs can be internally data parallel, providing support for the integration of data and task parallelism as well as nested task parallelism. They can thus be used to express multidisciplinary applications in a natural and efficient way. In this paper we describe the features of the language through a series of examples and give an overview of the runtime support required to implement these concepts in parallel and distributed environments.

Chapman, Barbara↗
DOE OSTI2025

Securing Federated Learning Against Active Reconstruction Attacks

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Improvements to the Ionizing Radiation Risk Assessment Program for NASA Astronauts

To perform dosimetry and risk assessment, NASA collects astronaut ionizing radiation exposure data from space flight, medical imaging and therapy, aviation training activities and prior occupational exposure histories. Career risk of exposure induced death (REID) from radiation is limited to 3 percent at a 95 percent confidence level. The Radiation Health Office at Johnson Space Center (JSC) is implementing a program to integrate the gathering, storage, analysis and reporting of astronaut ionizing radiation dose and risk data and records. This work has several motivations, including more efficient analyses and greater flexibility in testing and adopting new methods for evaluating risks. The foundation for these improvements is a set of software tools called the Astronaut Radiation Exposure Analysis System (AREAS). AREAS is a series of MATLAB(Registered TradeMark)-based dose and risk analysis modules that interface with an enterprise level SQL Server database by means of a secure web service. It communicates with other JSC medical and space weather databases to maintain data integrity and consistency across systems. AREAS is part of a larger NASA Space Medicine effort, the Mission Medical Integration Strategy, with the goal of collecting accurate, high-quality and detailed astronaut health data, and then securely, timely and reliably presenting it to medical support personnel. The modular approach to the AREAS design accommodates past, current, and future sources of data from active and passive detectors, space radiation transport algorithms, computational phantoms and cancer risk models. Revisions of the cancer risk model, new radiation detection equipment and improved anthropomorphic computational phantoms can be incorporated. Notable hardware updates include the Radiation Environment Monitor (which uses Medipix technology to report real-time, on-board dosimetry measurements), an updated Tissue-Equivalent Proportional Counter, and the Southwest Research Institute Radiation Assessment Detector. Also, the University of Florida hybrid phantoms, which are flexible in morphometry and positioning, are being explored as alternatives to the current NASA computational phantoms.

Semones, E. J.↗
NASA NTRS2005

SQL-RAMS

SQL-RAMS (where "SQL" signifies Structured Query Language and "RAMS" signifies Rocketdyne Automated Management System) is a successor to the legacy version of RAMS -- a computer program used to manage all work, nonconformance, corrective action, and configuration management on rocket engines and ground support equipment at Stennis Space Center. The legacy version resided in the File-Maker Pro software system and was constructed in modules that could act as standalone programs. There was little or no integration among modules. Because of limitations on file-management capabilities in FileMaker Pro, and because of difficulty of integration of FileMaker Pro with other software systems for exchange of data using such industry standards as SQL, the legacy version of RAMS proved to be limited, and working to circumvent its limitations too time-consuming. In contrast, SQL-RAMS is an integrated SQL-server-based program that supports all data-exchange software industry standards. Whereas in the legacy version, it was necessary to access individual modules to gain insight into a particular workstatus document, SQL-RAMS provides access through a single-screen presentation of core modules. In addition, SQL-RAMS enables rapid and efficient filtering of displayed statuses by predefined categories and test numbers. SQL-RAMS is rich in functionality and encompasses significant improvements over the legacy system. It provides users the ability to perform many tasks, which in the past required administrator intervention. Additionally, many of the design limitations have been corrected, allowing for a robust application that is user centric.

Alfaro, Victor O.↗
DOE OSTI2020

CPU and memory efficient coherent mode decomposition for the partially coherent x-ray simulations

In this work, the method of the Coherent Mode Decomposition (CMD) is applied to numerical wave propagation calculations for partially-coherent X-rays, using the Fourier optics and compatible methods. Its CPU and memory efficiency is discussed in various cases of the wavefront at the source and the beam waist. With the absence of the quadratic phase terms, the required sampling density of the electric fields is effectively reduced. The problem size is thus moderate and the method is feasible to be implemented on a single-node CPU server. In other cases, the same argument holds with proper treatments of the quadratic phase terms. Tests on CMD and the modes propagation are done for the case of the Coherent Hard X-ray beamline of the National Synchrotron Light Source II, using the Synchrotron Radiation Workshop software. We observe a few hundred or less dominant decomposed modes that resemble the electric fields converge to the wavefront intensity at a high accuracy of over 99%.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2005

SQL-RAMS

SQL-RAMS (where "SQL" signifies Structured Query Language and "RAMS" signifies Rocketdyne Automated Management System) is a successor to the legacy version of RAMS a computer program used to manage all work, nonconformance, corrective action, and configuration management on rocket engines and ground support equipment at Stennis Space Center. The legacy version resided in the FileMaker Pro software system and was constructed in modules that could act as stand-alone programs. There was little or no integration among modules. Because of limitations on file-management capabilities in FileMaker Pro, and because of difficulty of integration of FileMaker Pro with other software systems for exchange of data using such industry standards as SQL, the legacy version of RAMS proved to be limited, and working to circumvent its limitations too time-consuming. In contrast, SQL-RAMS is an integrated SQL-server-based program that supports all data-exchange software industry standards. Whereas in the legacy version, it was necessary to access individual modules to gain insight to a particular work-status documents, SQL-RAMS provides access through a single-screen presentation of core modules. In addition, SQL-RAMS enable rapid and efficient filtering of displayed statuses by predefined categories and test numbers. SQL-RAMS is rich in functionality and encompasses significant improvements over the legacy system. It provides users the ability to perform many tasks which in the past required administrator intervention. Additionally many of the design limitations have been corrected allowing for a robust application that is user centric.

Alfaro, Victor O.↗
NASA NTRS2009

Service Management Database for DSN Equipment

This data- and event-driven persistent storage system leverages the use of commercial software provided by Oracle for portability, ease of maintenance, scalability, and ease of integration with embedded, client-server, and multi-tiered applications. In this role, the Service Management Database (SMDB) is a key component of the overall end-to-end process involved in the scheduling, preparation, and configuration of the Deep Space Network (DSN) equipment needed to perform the various telecommunication services the DSN provides to its customers worldwide. SMDB makes efficient use of triggers, stored procedures, queuing functions, e-mail capabilities, data management, and Java integration features provided by the Oracle relational database management system. SMDB uses a third normal form schema design that allows for simple data maintenance procedures and thin layers of integration with client applications. The software provides an integrated event logging system with ability to publish events to a JMS messaging system for synchronous and asynchronous delivery to subscribed applications. It provides a structured classification of events and application-level messages stored in database tables that are accessible by monitoring applications for real-time monitoring or for troubleshooting and analysis over historical archives.

Zendejas, Silvino↗
NASA NTRS2018

Restructuring Big Data to Improve Data Access and Performance in Analytic Services Making Research More Efficient for the Study of Extreme Weather Events and Application User Communities

By developing and enhancing various services and tools, the GES DISC provides users with the capability to access and visualize data, and to make comparisons of data from multiple sensor and models via a number of cross-discipline projects. Discovering Data via Faceted Web Interface Web interface to data products and services Search and Download mechanisms Dataset Landing Pages Accessing Data through Interoperable Services: GDS – GrADS Data Server OPeNDAP - Open-source Project for a Network Data Access Protocol WMS – OGC service GIS connector – allowing IS tools to access data easier (coming soon) HTTPS -- direct online access Downloading Data Basics: Subset and egridding Service – Parameter, Spatial, Time, Vertical, Mean averaging, format conversion, and regridding for L3/L4 gridded data Swath Data Subsetter – Parameter, spatial subset of L2 /L1 data. Visualizing Data Online: Giovanni –Visualization and Analysis L3/L4 gridded data AIRS NRT Viewer – AIRS near-real-time DQVis – L2 data quality visualization

data cube↗
DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
DOE OSTI2016

Experimental Analysis of File Transfer Rates over Wide-Area Dedicated Connections

File transfers over dedicated connections, supported by large parallel file systems, have become increasingly important in high-performance computing and big data workflows. It remains a challenge to achieve peak rates for such transfers due to the complexities of file I/O, host, and network transport subsystems, and equally importantly, their interactions. We present extensive measurements of disk-to-disk file transfers using Lustre and XFS file systems mounted on multi-core servers over a suite of 10 Gbps emulated connections with 0-366 ms round trip times. Our results indicate that large buffer sizes and many parallel flows do not always guarantee high transfer rates. Furthermore, large variations in the measured rates necessitate repeated measurements to ensure confidence in inferences based on them. We propose a new method to efficiently identify the optimal joint file I/O and network transport parameters using a small number of measurements. We show that for XFS and Lustre with direct I/O, this method identifies configurations achieving 97% of the peak transfer rate while probing only 12% of the parameter space.

Rao, Nageswara S.↗
DOE OSTI2021

Improving Data Center Energy Efficiency Through End-to-End Cooling Modeling and Optimization. Final Report

This report summarizes the development and demonstration of the three features developed for the DOE project “Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization” (Award No. DE-EE0007688). The development of the three software features, Feature 1: Optimization of Cooling System Operation, Feature 2: Optimization of Airflow Management System, and Feature 3: Simultaneous Optimization of Airflow Management and Cooling Systems, are described first. Feature 1 is implemented in Phase 1 of the project and demonstrates 15% energy savings from cooling system optimization by applying the proposed retrofit solutions. Feature 2 is implemented in Phase 2 and the assessments show cooling energy-saving potential between 19.3% and 54.0%, depending on the energy-saving measures employed. In addition, investigating the energy saving potential for a hypothetical simple data center shows about 6.8% energy savings by optimizing the server load placement. Feature 3 is implemented in Phase 3 and the results show energy savings of 53.4% and 74.6% for the two partner data centers, respectively, compared to the baselines defined in Phase 1. Lastly, the project outcomes including software releases and publications are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗