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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Adaptative Site Management for a 115 Acre Chlorinated Solvent Plume with Two Separate Source Areas at Kennedy Space Center, Florida

Background/Objectives. During Resource Conservation and Recovery Act (RCRA) Facility Investigation (RFI) activities, Geosyntec delineated a chlorinated volatile organic compound (CVOC) plume at the National Aeronautics and Space Administration’s (NASA’s) Vehicle Assembly Building (VAB) area located at KSC, Florida. The RFI activities identified an approximate 115-acre dissolved plume (primarily vinyl chloride) and a trichloroethene (TCE) source area in an active aerospace complex that is surrounded by sensitive wetland/waterbodies. Due to the size of the impacted area, the Corrective Measure Design included a multi-component strategy: (i) address the source area via bioremediation; (ii) protect sensitive wetlands from impacted groundwater discharge via biosparging; and (iii) Long Term Monitoring (LTM) of the remaining dissolved plume. After the Corrective Measures implementation (CMI), NASA and Geosyntec worked with Florida Department of Environmental Protection (FDEP) to implement an adaptive site management for the complex, 115-acre site outside of the traditional RCRA process. The adaptive site management approach relied on performing supplemental assessments and implementing Interim Measures (IMs) to further assess and implement remedies over time while working within site and budget constraints, with an overall goal of achieving enough mass reduction to transition the entire site to LTM and eventually achieve site closure. Approach/Activities. After the biosparge barrier was operational and bioremediation within the source area (referred to as Hot Spot 1) achieved the Corrective Action Objective (CAO), supplemental assessment of the area between Hot Spot 1 and the biosparge barrier was performed. The conceptual site model was updated using the supplemental assessment results and an air sparge system IM was designed to treat an approximate 1.2 acre area (referred to as Hot Spot 2). After installation of the air sparge system, supplemental assessment within the remainder of the 115-acre dissolved plume was performed and a second TCE source area was identified. The TCE source area and associated areas with elevated CVOC concentrations (referred to as Hot Spot 3) were delineated and a bioremediation IM was implemented. Also, the downgradient impacts from Hot Spot 3 were adjacent to a sensitive waterbody, and negotiations with the FDEP allowed the area to be monitored using LTM. Results/Lessons Learned. The performance of supplemental assessment activities and implementation of remedial alternatives as IMs allowed NASA to successfully address groundwater impacts over time, while working within the FDEP regulatory framework. The implementation of the CMI and multiple IMs has achieved the following goals: (i) the biosparge barrier has mitigated the potential discharge of impacted groundwater to an adjacent wetland; (ii) enhanced bioremediation within Hot Spot 1 achieved the CAO within 2 years and transitioned the area into LTM; (iii) operation of an air sparge system within Hot Spot 2 removed TCE as a constituent of concern and contributed to a reduction (approximately 43%) in the impacted groundwater area outside the air sparge treatment area (plume collapse); and (iv) bioremediation within Hot Spot 3 removed approximately 80% of the CVOC mass and contributed to a reduction (approximately 47%) in the impacted groundwater area outside the bioremediation IM treatment area. Overall, the adaptive approach is protecting the sensitive water bodies surrounding the complex site and reducing the area of impacted groundwater, which is moving the entire site towards LTM.

Rebecca C Daprato↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
NASA NTRSDate not supplied

Initial Exploration of STEReO (Scalable Traffic management for Emergency Response Operations) system user requirements for safe integration of small UAS

Environment-based disasters, such as wildfire, can cause substantial loss of life and loss of property and are costing billions of dollars annually. Existing disaster response operations are complex, with many challenges. The aim of the Scalable Traffic management for Emergency Response Operations (STEReO) system is to demonstrate the application of technologies that will enable small, low-altitude Unmanned Aerial System operations to safely take place in the airspace above a disaster event, increasing the effectiveness of the response. As a first step, the project aims to build a small prototype to demonstrate and test the core functions of this system based on user needs and requirements that were gathered from experts in the field during discussions and walkthroughs.

STEReO↗
NASA NTRS2019

High-Performance Spaceflight Computing (HPSC) Middleware Overview

High Performance Spacecraft Computing (HPSC) is a joint project between the National Aeronautics and Space Administration (NASA) and Air Force Research Lab (AFRL) to develop a high-performance multi-core radiation hardened flight processor. HPSC offers a new flight computing architecture to meet the needs of NASA missions through 2030 and beyond. Providing on the order of 100X the computational capacity of current flight processors for the same amount of power, the multicore architecture of the HPSC processor, or "Chiplet" provides unprecedented flexibility in a flight computing system by enabling the operating point to be set dynamically, trading among needs for computational performance, energy management and fault tolerance. The HPSC Chiplet is being developed by Boeing under contract to NASA, and is expected to provide prototypes in 2021. The HPSC Chiplet prototypes will be delivered with an evaluation board, system emulators, comprehensive system software, and a software development kit. In addition to the vendor deliverables, the AFRL is funding the development of a flexible Middleware to be developed by NASA Jet Propulsion Laboratory and NASA Goddard Space Flight Center. The HPSC Middleware provides a suite of thirteen high level services to manage the compute, memory and I/O resources of this complex device.This presentation will provide an overview of the HPSC project, including a hardware overview, system software overview, Middleware overview, and mission use cases. The hardware overview will provide a look at the 8 core High Performance Processing Subsystem (HPPS), the Real Time Processing Subsystem (RTPS), the Chiplet Configuration Management Subsystem, on chip peripherals, and high speed I/O. The system software overview will introduce the boot loaders, operating systems, device drivers, and software development environment. The Middleware overview will provide insight into the high-level services that will be provided to help mission developers manage the many resources and configurations made possible with the Chiplet. Finally, the presentation will provide a brief look at the mission use cases that can be enabled with this next generation architecture.

middleware↗
NASA NTRS1987

Program risk analysis handbook

NASA regulations specify that formal risk analysis be performed on a program at each of several milestones. Program risk analysis is discussed as a systems analysis approach, an iterative process (identification, assessment, management), and a collection of techniques. These techniques, which range from extremely simple to complex network-based simulation, are described in this handbook in order to provide both analyst and manager with a guide for selection of the most appropriate technique. All program risk assessment techniques are shown to be based on elicitation and encoding of subjective probability estimates from the various area experts on a program. Techniques to encode the five most common distribution types are given. Then, a total of twelve distinct approaches to risk assessment are given. Steps involved, good and bad points, time involved, and degree of computer support needed are listed. Why risk analysis should be used by all NASA program managers is discussed. Tools available at NASA-MSFC are identified, along with commercially available software. Bibliography (150 entries) and a program risk analysis check-list are provided.

Batson, R. G.↗
NASA NTRS1982

New Algorithms Manage Fourfold Redundancy

Redundant sensors, actuators, and computers improve reliability of complex control systems, such as those in nuclear powerplants and aircraft. If one or more redundant elements fail, another takes over so that normal operation is not interrupted. Quad selection filter rejects data from null-failed and hardover-failed and hardover-failed units.

Gelderloos, H. C.↗
NASA NTRS2014

Analysis and Perspective from the Complex Aerospace Systems Exchange (CASE) 2013

NASA Langley Research Center embedded four rapporteurs at the Complex Aerospace Systems Exchange (CASE) held in August 2013 with the objective to capture the essence of the conference presentations and discussions. CASE was established to provide a discussion forum among chief engineers, program managers, and systems engineers on challenges in the engineering of complex aerospace systems. The meeting consists of invited presentations and panels from industry, academia, and government followed by discussions among attendees. This report presents the major and reoccurring themes captured throughout the meeting and provides analysis and insights to further the CASE mission.

Jones, Kennie H.↗
NASA NTRS2014

Data Preservation, Information Preservation, and Lifecyle of Information Management at NASA GES DISC

Data lifecycle management awareness is common today; planners are more likely to consider lifecycle issues at mission start. NASA remote sensing missions are typically subject to life cycle management plans of the Distributed Active Archive Center (DAAC), and NASA invests in these national centers for the long-term safeguarding and benefit of future generations. As stewards of older missions, it is incumbent upon us to ensure that a comprehensive enough set of information is being preserved to prevent the risk for information loss. This risk is greater when the original data experts have moved on or are no longer available. Preservation of items like documentation related to processing algorithms, pre-flight calibration data, or input-output configuration parameters used in product generation, are examples of digital artifacts that are sometimes not fully preserved. This is the grey area of information preservation; the importance of these items is not always clear and requires careful consideration. Missing important metadata about intermediate steps used to derive a product could lead to serious challenges in the reproducibility of results or conclusions. Organizations are rapidly recognizing that the focus of life-cycle preservation needs to be enlarged from the strict raw data to the more encompassing arena of information lifecycle management. By understanding what constitutes information, and the complexities involved, we are better equipped to deliver longer lasting value about the original data and derived knowledge (information) from them. The NASA Earth Science Data Preservation Content Specification is an attempt to define the content necessary for long-term preservation. It requires new lifecycle infrastructure approach along with content repositories to accommodate artifacts other than just raw data. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) setup an open-source Preservation System capable of long-term archive of digital content to augment its raw data holding. This repository is being used for such missions as HIRDLS, UARS, TOMS, OMI, among others. We will provide a status of this implementation; report on challenges, lessons learned, and detail our plans for future evolution to include other missions and services.

data management↗
NASA NTRS1994

Program test objectives milestone 3

The following conclusions have been developed relative to propulsion system technology adequacy for efficient development and operation of recoverable and expendable launch vehicles (RLV and ELV) and the benefits which the integrated propulsion technology demonstrator will provide for enhancing technology: (1) Technology improvements relative to propulsion system design and operation can reduce program cost. Many features or improvement needs to enhance operability, reduce cost, and improve payload are identified. (2) The Integrated Propulsion Technology Demonstrator (IPTD) Program provides a means of resolving the majority of issues associated with improvement needs. (3) The IPTD will evaluate complex integration of vehicle and facility functions in fluid management and propulsion control systems, and provides an environment for validating improved mechanical and electrical components. (4) The IPTD provides a mechanism for investigating operational issues focusing on reducing manpower and time to perform various functions at the launch site. These efforts include model development, collection of data to validate subject models, and ultimate development of complex time line models. (5) The IPTD provides an engine test bed for tri/bi-propellant engine development firings which is representative of the actual vehicle environment. (6) The IPTD provides for only a limited multiengine configuration integration environment for RLV. Multiengine efforts may be simulated for a number of subsystems and a number of subsystems are relatively independent of the multiengine influences.

Gaynor, T. L.↗
DOE OSTI2024

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2026

Engineering Layer For System Analysis

ELSA offers various utility classes and methods to streamline the definition of regions, materials, and geometries in nuclear simulations. Key features include generating OpenMC regions, managing material properties, and providing convenient abstractions for complex geometrical and physical configurations. Additionally, ELSA supports the creation of submodels, enabling users to build modular and reusable components for their simulations. The codebase also includes robust extrusion and revolution capabilities, facilitating the efficient creation of 3D parametric geometries from 2D profiles through linear and rotational transformations.

Ferney, Paul [Idaho National Laboratory (INL), Ida↗
DOE OSTI2025

MRCI Task 2: Addressing Key Technical Challenges - Executive Summary Report

This report provides a high-level overview of Task 2 products that were developed through defining carbon storage systems, Precambrian basement structure and stress, developing regional technical collaboration, modeling and risk assessment. Subtask 2.1: Defining Sub-Regional CS/CCUS Systems Subtask 2.2: Defining Precambrian basement faulting/stress Subtask 2.3: Developing industrial partnerships and regional technical collaborations Subtask 2.4: Conducting regional/subregional analysis Subtask 2.5: Assessing and managing risk for potential commercial-scale storage complexes

MRCI,Technical Challenges↗
DOE OSTI2025

Implementation Risks of RFID in TA-55

Prior research has shown that radio frequency identification (RFID) technology offers potential benefits for streamlining inventory management amidst rising production levels across the NNSA/DOE complex, yet it also presents substantial risks. RFID presents challenges never faced by LANL but also significant opportunities for accelerated inventory identification, real-time locating of assets, and minimized radiation exposure. This risk assessment evaluates the implementation of RFID technology at different Technical Area 55 (TA-55) locations, primarily withing the RLUOB basement and PF-4.

42 ENGINEERING↗
NASA NTRS1975

Graphics Flutter Analysis Methods, an interactive computing system at Lockheed-California Company

An interactive computer graphics system, Graphics Flutter Analysis Methods (GFAM), was developed to complement FAMAS, a matrix-oriented batch computing system, and other computer programs in performing complex numerical calculations using a fully integrated data management system. GFAM has many of the matrix operation capabilities found in FAMAS, but on a smaller scale, and is utilized when the analysis requires a high degree of interaction between the engineer and computer, and schedule constraints exclude the use of batch entry programs. Applications of GFAM to a variety of preliminary design, development design, and project modification programs suggest that interactive flutter analysis using matrix representations is a feasible and cost effective computing tool.

Radovcich, N. A.↗
NASA NTRS1979

Engineering risk reduction in satellite programs

Methods developed in planning and executing system safety engineering programs for Lockheed satellite integration contracts are presented. These procedures establish the applicable safety design criteria, document design compliance and assess the residual risks where non-compliant design is proposed, and provide for hazard analysis of system level test, handling and launch preparations. Operations hazard analysis identifies product protection and product liability hazards prior to the preparation of operational procedures and provides safety requirements for inclusion in them. The method developed for documenting all residual hazards for the attention of program management assures an acceptable minimum level of risk prior to program deployment. The results are significant for persons responsible for managing or engineering the deployment and production of complex high cost equipment under current product liability law and cost/time constraints, have a responsibility to minimize the possibility of an accident, and should have documentation to provide a defense in a product liability suit.

Dean, E. S., Jr.↗