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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Marine Corps Base Camp Lejeune Barrier Island Shore History

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package contains historic shorelines and vegetation lines at MCBL Barrier Island Shore digitized from aerial photography. The years represented include 1938, 1956, 1979, 1989, 1996, and 1998.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Marine Corps Base Camp Lejeune Geospatial Marsh Model: Habitat Maps

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package contains PNGs (images) of habitat maps (key is provided as single PNG) for Freeman, Traps, and French marsh modeling domains in the target years 2013 (initial), 2035, 2065, & 2100 after modeling marsh under the five SLR scenarios provided by the base.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Marine Corps Base Camp Lejeune New River Estuary (NRE) - Autonomous Vertical Profiler (AVP) Stone's Bay Data 2008-2013

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Stone's Bay at Station 3 (AVP1). Description from Monitoring Report: Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Marine Corps Base Camp Lejeune New River Estuary (NRE) - Autonomous Vertical Profiler (AVP) Morgan Bay Data 2008-2016

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Morgan Bay upstream of Station 5 (AVP2): Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Onslow Beach Washover Fan Morphology from 2012 to 2015

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.These grid files were created from data collected using a Riegl 3-D laser scanner. Millions of x, y, and z points from the F2 Washover Fan on Onslow Beach were processed using the MARS software package to define a bare-earth model. Grid files were imported into Golden Software Surfer version 19 for editing and exported from that application as .grd files. Files can be opened in Surfer or ArcMap, but the coordinate system information will be lost if opened in ArcMap. These data were collected to quantify the morphological changes that occur on a washover fan through time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Advanced Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Demonstration

A key area of the Advanced Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) strategy is the development of methodologies and tools that will be used to predict the safety, security, safeguards, performance, and deployment viability of SMRs. The goal of the SMR PRA activity will be to develop quantitative methods and tools and the associated analysis framework for assessing a variety of risks. Development and implementation of SMR-focused safety assessment methods may require new analytic methods or adaptation of traditional methods to the advanced design and operational features of SMRs. We will need to move beyond the current limitations such as static, logic-based models in order to provide more integrated, scenario-based models based upon predictive modeling which are tied to causal factors. The development of SMR-specific safety models for margin determination will provide a safety case that describes potential accidents, design options (including postulated controls), and supports licensing activities by providing a technical basis for the safety envelope. This report documents the progress that was made to implement the PRA framework, specifically by way of demonstration of an advanced 3D approach to representing, quantifying and understanding flooding risks to a nuclear power plant.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Format requirements of thermal neutron scattering data in a nuclear data format to succeed the ENDF format

In November 2012, the Working Party on Evaluation Cooperation Subgroup 38 (WPEC-SG38) began with the task of developing a nuclear data format and supporting infrastructure to replace the now nearly 50 year old ENDF format. The first step in this process is to develop requirements for the new format and infrastructure. In this talk, I will review the status of ENDF's Thermal Scattering Law (TSL) formats as well as support for this data in the GND format (from which the new format is expected to evolve). Finally, I hope to begin a dialog with members of the thermal neutron scattering community so that their data needs can be accurately and easily accommodated by the new format and tools, as captured by the requirements document. During this discussion, we must keep in mind that the new tools and format must; Support what is in existing data files; Support new things we want to put in data files; and Be flexible enough for us to adapt it to future unanticipated challenges.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

MAMA User Guide v2.0.1

Morphological signatures of bulk SNM materials have significant promise, but these potential signatures are not fully utilized. This document describes software tools, collectively called the MAMA (Morphological Analysis for Material Attribution) software that can help provide robust and accurate quantification of morphological features in bulk material microscopy images (Optical, SEM). Although many of the specific tools are not unique to Mama, the software package has been designed specifically for nuclear material morphological analysis, and is at a point where it can be easily adapted (by Los Alamos or by collaborators) in response to new, different, or changing forensics needs. The current release of the MAMA software only includes the image quantification, descriptions, and annotation functionality. Only limited information on a sample, its pedigree, and its chemistry are recorded inside this part of the software. This was decision based on initial feedback and the fact that there are several analytical chemistry databases being developed within the community. Currently MAMA is a standalone program that can export quantification results in a basic text format that can be imported into other programs such as Excel and Access. There is also a basic report generating feature that produces HTML formatted pages of the same information. We will be working with collaborators to provide better integration of MAMA into their particular systems, databases and workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Overview of the NCSP Nuclear Data Program [Slides]

The mission of the NCSP is to provide sustainable expert leadership, direction and the technical infrastructure necessary to develop, maintain and disseminate the essential technical tools, training and data required to support safe, efficient fissionable material operations within the Department of Energy. The vision of the NCSP is to create a continually improving, adaptable and transparent program that communicates and collaborates globally to incorporate technology, practices and programs to be responsive to the essential technical needs of those responsible for developing, implementing and maintaining nuclear criticality safety.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

Using output to evaluate and refine rules in rule-based expert systems

The techniques described provide an effective tool which knowledge engineers and domain experts can utilize to help in evaluating and refining rules. These techniques have been used successfully as learning mechanisms in a prototype adaptive diagnostic expert system and are applicable to other types of expert systems. The degree to which they constitute complete evaluation/refinement of an expert system depends on the thoroughness of their use.

St.clair, D. C.↗
NASA NTRS2016

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

Nguyen, Nhan T.↗
NASA NTRS2019

NASA Armstrong (AFRC) Human Factors Program

Armstrong Flight Research Center is NASA’s premier flight research center. It is located on Edwards AFB in the high desert of California. The AFRC Safety and Mission Assurance Directorates Human Factors Program is one tool that enables Armstrong to be the national center of excellence for conducting leading edge aerospace flight research, airborne science, and airborne astronomy by providing responsive, effective, and adaptive Safety & Mission Assurance support. The AFRC Human Factors Specialist is also a member of the Agency Human Factors Task Force which provides and expands the understanding and impact of Human Factors across the Agency. The Program is managed by the Office of Safety and Mission Assurance at NASA Headquarters.

Kiriokos, Nick C.↗
DOE OSTI2016

Models of TCP in high-BDP environments and their experimental validation

In recent years, the computer networking community has seen a steady growth in bandwidth-delay products (BDPs). Several TCP variants were created to combat the shortcomings of legacy TCP when it comes to operation in high-BDP environments. These variants, among which are CUBIC, STCP, and H-TCP, have been extensively studied in some empirical contexts, and some analytical models exist for CUBIC and STCP. However, since these studies have been conducted, BDPs have risen even more, and new bulk data transfer tools have emerged that utilize multiple parallel TCP streams. In view of these new developments, it is imperative to revisit the question: Which congestion control algorithms are best adapted to current networking environments? In order to help resolve this question, we contribute the following: (i) using first principles, we develop a general throughput-prediction framework that takes into account buffer sizes and maximum window constraints; (ii) we validate the models using measurements and achieve low prediction errors; (iii) we note differences in TCP dynamics between two experimental configurations and find one of them to be significantly more deterministic than the other; we also find that CUBIC and H-TCP outperform STCP, especially when multiple streams are used; and (iv) we present preliminary results for modelling multiple TCP streams for CUBIC and STCP.

Vardoyan, G.↗
DOE OSTI2020

Systems, methods and computer program products for self-tuning sensor data processing

Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.

Draelos, Timothy J.↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗