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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

DeVol, Nathaniel↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1992

An approach to integrating and creating flexible software environments

Engineers and scientists are attempting to represent, analyze, and reason about increasingly complex systems. Many researchers have been developing new ways of creating increasingly open environments. In this research on VEHICLES, a conceptual design environment for space systems, an approach was developed, called 'wrapping', to flexibility and integration based on the collection and then processing of explicit qualitative descriptions of all the software resources in the environment. Currently, a simulation is available, VSIM, used to study both the types of wrapping descriptions and the processes necessary to use the metaknowledge to combine, select, adapt, and explain some of the software resources used in VEHICLES. What was learned about the types of knowledge necessary for the wrapping approach is described along with the implications of wrapping for several key software engineering issues.

Bellman, Kirstie L.↗
NASA NTRS2004

Earth Science Education Plan: Inspire the Next Generation of Earth Explorers

The Education Enterprise Strategy, the expanding knowledge of how people learn, and the community-wide interest in revolutionizing Earth and space science education have guided us in developing this plan for Earth science education. This document builds on the success of the first plan for Earth science education published in 1996; it aligns with the new framework set forth in the NASA Education Enterprise Strategy; it recognizes the new educational opportunities resulting from research programs and flight missions; and it builds on the accomplishments th'at the Earth Science Enterprise has made over the last decade in studying Earth as a system. This document embodies comprehensive, practicable plans for inspiring our children; providing educators with the tools they need to teach science, technology, engineering, and mathematics (STEM); and improving our citizens' scientific literacy. This plan describes an approach to systematically sharing knowledge; developing the most effective mechanisms to achieve tangible, lasting results; and working collaboratively to catalyze action at a scale great enough to ensure impact nationally and internationally. This document will evolve and be periodically reviewed in partnership with the Earth science education community.

Source record↗
DOE OSTI2026

MCNP® Code Version 6.3.2 Theory & User Manual (Rev. 1)

This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.2, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.

42 ENGINEERING↗
DOE OSTI2015

Integrative Curriculum Development in Nuclear Education and Research Vertical Enhancement Program

Using a vertical education enhancement model, a Nuclear Education and Research Vertical Enhancement (NERVE) program was developed. The NERVE program is aimed at developing nuclear engineering education and research to 1) enhance skilled workforce development in disciplines relevant to nuclear power, national security and medical physics, and 2) increase the number of students and faculty from underrepresented groups (women and minorities) in fields related to the nuclear industry. The program uses multi-track training activities that vertically cut across the several education domains: undergraduate degree programs, graduate schools, and post-doctoral training. In this paper, we present the results of an integrative curriculum development in the NERVE program. The curriculum development began with nuclear content infusion into existing science, engineering and technology courses. The second step involved the development of nuclear engineering courses: 1) Introduction to Nuclear Engineering, 2) Nuclear Engineering I, and 2) Nuclear Engineering II. The third step is the establishment of nuclear engineering concentrations in two engineering degree programs: 1) electrical engineering, and 2) mechanical engineering. A major outcome of the NERVE program is a collaborative infrastructure that uses laboratory work, internships at nuclear facilities, on-campus research, and mentoring in collaboration with industry and government partners to provide hands-on training for students. The major activities of the research and education collaborations include: - One-week spring training workshop at Brookhaven National Laboratory: The one-week training and workshop is used to enhance research collaborations and train faculty and students on user facilities/equipment at Brookhaven National Laboratory, and for summer research internships. Participants included students, faculty members at Alabama A and M University and research collaborators at BNL. The activities include 1) tour and introduction to user facilities/equipment at BNL that are used for research in room-temperature semiconductor nuclear detectors, 2) presentations on advances on this project and on wide band-gap semiconductor nuclear detectors in general, and 3) graduate students' research presentations. - Invited speakers and lectures: This brings collaborating research scientist from BNL to give talks and lectures on topics directly related to the project. Attendance includes faculty members, researchers and students throughout the university. - Faculty-students team summer research at BNL: This DOE and National Science Foundation (NSF) program help train students and faculty members in research. Faculty members go on to establish research collaborations with scientists at BNL, develop and submit research proposals to funding agencies, transform research experience at BNL to establish and enhance reach capabilities at home institution, and integrate their research into teaching through class projects and hands-on training for students. The students go on to participate in research work at BNL and at home institution, co-author research papers for conferences and technical journals, and transform their experiences into developing senior and capstone projects. - Grant proposal development: Faculty members in the NERVE program collaborate with BNL scientists to develop proposals, which often help to get external funding needed to expand and sustain the continuity of research activities and supports for student's wages and scholarships (stipends, tuition and fees). - Faculty development and mentoring: The above collaboration activities help faculty professional development. The experiences, grants, joint publications in technical journals, and supervision of student's research, including thesis and dissertation research projects, contribute greatly to faculty development. Senior scientists at BNL and senior faculty members on campus jointly mentor junior faculty members to enhance their professional growth. - Graduate thesis and dissertation research: Brookhaven National Laboratory provides unique opportunities and outstanding research resources for the NERVE program graduate research. Scientists from BNL serve in master's degree thesis and PhD dissertation committees, where they play active roles in the supervision of the research. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

MFIX-DEM Phi: Performance and Capability Improvements Towards Industrial Grade Open-source DEM Framework with Integrated Uncertainty Quantification

Two major challenges hinder the effective use and adoption of multiphase computational fluid dynamics tools by the industry. The first is the need for significant computational resources, which is inversely proportional to the accuracy of solutions due to computational intensity of the algorithms. The second barrier is assessing the prediction credibility and confidence in the simulation results. In this project, a multi-tiered approach has been proposed under four broad activities to overcome these challenges while addressing all of the objectives outlined in FOA-0001238 through Phases 1 and 2 of the project. The present report consists of the results for only Phase 1, which was the funded performance period. From the start the project, all of the objectives outlined in FOA were addressed through four major activity tasks in an integrated and balanced fashion to improve adoption of MFIX suite of solvers for industrial use. The first task aimed to improve the performance of MFIX-DEM specifically targeting to acquire the peak performance on Intel Xeon and Xeon Phi based systems, which are expected to be one of the primary high-performance computing platforms both affordable and available for the industrial users in the next two to five years. However, due to a number of changes in course of the project, the scope of the performance improvements related task was significantly reduced to avoid duplicate work. Hence, more emphasis was placed on the other three tasks as discussed below. The second task aimed at physical modeling enhancements through implementation of polydispersity capability and validation of heat transfer models in MFIX. An extended verification and validation (V&V) study was performed for the new polydispersity feature implemented in MFIX-DEM both for granular and coupled gas-solid flows. The features of the polydispersity capability and results for an industrially relevant problem were disseminated through journal papers (one published and one under review at the time of writing of the final technical report). As part of the validation efforts, another industrially relevant problem of interest based on rotary drums was studied for several modes of heat transfer and results were presented in conferences. Third task was aimed towards an important and unique contribution of the project, which was to develop a unified uncertainty quantification framework by integrating MFIX-DEM with a graphical user interface (GUI) driven uncertainty quantification (UQ) engine, i.e., MFIX-GUI and PSUADE. The goal was to enable a user with only modest knowledge of statistics to effectively utilize the UQ framework offered with MFIX-DEM Phi to perform UQ analysis routinely. For Phase 1, a proof-of-concept demonstration of the proposed framework was completed and shared. Direct industry involvement was one of the key virtues of this project, which was performed through forth task. For this purpose, even at the proposal stage, the project team received strong interest in the proposed capabilities from two major corporations, which were further expanded throughout Phase 1 and a new collaboration with another major corporation from chemical industry was also initiated. The level of interest received and continued collaboration for the project during Phase 1 clearly shows the relevance and potential impact of the project for the industrial users.

42 ENGINEERING↗
DOE OSTI2020

SAM Plug-in Development (Phase I Final Report)

The DOE Office of Nuclear Energy (NE) has created an extensive set of advanced modeling and simulation tools for nuclear engineering analysis. The advanced capabilities of these newer analysis codes require more in-depth training, skills, and knowledge in order to effectively utilize them for the design, analysis, and licensing of advanced nuclear systems and experiments. A high learning curve for inexperienced users may deter organizations from incorporating these tools into their internal processes. This project involved development of a plug-in to the Symbolic Nuclear Analysis Package (SNAP) for the System Analysis Module (SAM) tool. SAM is an advanced system analysis tool for reactor transient analyses being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SNAP provides a highly flexible framework for creating, modifying and documenting input for engineering analysis codes such as SAM as well as extensive functionality for submitting, monitoring, and interacting with the codes through an intuitive graphical user interface (GUI). The common user interface provided by SNAP minimizes the learning curve for engineers starting with a new analysis code and provides an intuitive framework for transitioning between different analysis codes. SNAP provides a powerful but intuitive interface to facilitate access to advanced modeling and simulation tools for inexperienced users. Unlike many “form based” GUI’s, SNAP maps each engineering code’s component input to an internal database which manages all component input parameters along with component interconnections. This level of abstraction permits SNAP to support several advanced capabilities such as renodalization, model validation and consistency checks, embedded documentation, model notebook generation, data ownership and reviewer tracking, and variable assignment for inputs to name a few. SNAP includes a built-in Python interpreter and is interfaced to several commercial and open source packages including CPython, MATLAB/OCTAVE, Microsoft Office, Open Office, and SANDIA’s DAKOTA package which provides Uncertainty Quantification analysis through the SNAP plug-ins. Phase I of this project involved development a fully functional basic SAM plug-in to SNAP. This plug-in provides the ability to import existing models, graphically construct, edit and submit models using SNAP’s extensive functionality.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1994

Knowledge-based processing for aircraft flight control

This Contractor Report documents research in Intelligent Control using knowledge-based processing in a manner dual to methods found in the classic stochastic decision, estimation, and control discipline. Such knowledge-based control has also been called Declarative, and Hybid. Software architectures were sought, employing the parallelism inherent in modern object-oriented modeling and programming. The viewpoint adopted was that Intelligent Control employs a class of domain-specific software architectures having features common over a broad variety of implementations, such as management of aircraft flight, power distribution, etc. As much attention was paid to software engineering issues as to artificial intelligence and control issues. This research considered that particular processing methods from the stochastic and knowledge-based worlds are duals, that is, similar in a broad context. They provide architectural design concepts which serve as bridges between the disparate disciplines of decision, estimation, control, and artificial intelligence. This research was applied to the control of a subsonic transport aircraft in the airport terminal area.

Painter, John H.↗
DOE OSTI2015

Reactive transport benchmarks for subsurface environmental simulation

Over the last 20 years, we have seen firsthand the evolution of multicomponent reactive transport modeling and the expanding range and increasing complexity of subsurface applications it is being used to address. There is a growing reliance on reactive transport modeling (RTM) to address some of the most compelling issues facing our planet: climate change, nuclear waste management, contaminant remediation, and pollution prevention. While these issues are motivating the development of new and improved capabilities for subsurface environmental modeling using RTM (e.g., biogeochemistry from cell-scale physiology to continental-scale terrestrial ecosystems, nonisothermal multiphase conditions, coupled geomechanics), there remain longstanding challenges in characterizing the natural variability of hydrological, biological, and geochemical properties in subsurface environments and limited success in transferring models between sites and across scales. An equally important trend over the last 20 years is the evolution of modeling from a service sought out after data has been collected to a multifaceted research approach that provides (1) an organizing principle for characterization and monitoring activities; (2) a systematic framework for identifying knowledge gaps, developing and integrating new knowledge; and (3) a mechanistic understanding that represents the collective wisdom of the participating scientists and engineers. There are now large multidisciplinary projects where the research approach is model-driven, and the principal product is a holistic predictive simulation capability that can be used as a test bed for alternative conceptualizations of processes, properties, and conditions. Much of the future growth and expanded role for RTM will depend on its continued ability to exploit technological advancements in the earth and environmental sciences. Advances in measurement technology, particularly in molecular biology (genomics), isotope fractionation, and high-resolution X-ray spectroscopy, have created new lines of research that can be used to inform the conceptualization of reactions and rate laws and validate mechanistic models. For example, spectroscopy has identified the oxidation states of key components and elemental distributions at increasingly smaller scales and lower concentrations; molecular biology has progressed from identifying the presence of microbes to characterization of which microbial communities are active and what they are doing (i.e., microbial function), which has led in turn to the identification of active processes under conditions beyond what analytical chemistry can discern; isotope ratios in pore water and solid phases that can be used to distinguish between biotic from abiotic processes, sorption from precipitation, and origin and age of groundwater. The other noteworthy development that is expanding the role of RTM in subsurface environmental modeling is he advance in computational technology that is enabling the simulation of more coupled processes with increasing mechanistic detail. In some cases, this involves the inclusion of more reactive species and/or microbial populations in the simulations; in other cases, the impact is through the ability to achieve high resolution of property distributions over longer simulated times. To achieve these ambitious objectives for subsurface reactive transport simulation, the subsurface science and engineering community is being driven to provide accurate assessments of engineering performance and risk for important issues with far-reaching consequences. As a result, the complexity and detail of subsurface processes, properties, and conditions that can be simulated have significantly expanded. This expansion was enabled, in part, by advances in measurement technology, computing technology, and numerical techniques.

benchmarking↗
DOE OSTI2022

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. Here this work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Stable-Cycling Sustainable Na-Ion Batteries with Olivine Iron Phosphate Cathode in an Ether Electrolyte

Sustainable batteries using nontoxic, earth-abundant, and low-cost materials are key to decarbonization. Olivine NaFePO 4 fulfills these criteria, is attractive for Na-ion batteries, and can be derived from LiFePO 4 recycled from Li-ion battery wastes. Critical knowledge is needed for transforming LiFePO 4 to NaFePO 4 to enable such a sustainable, green engineering path toward high-performance Na-ion batteries. Herein, we report on the development of a stable-cycling, sustainable olivine iron phosphate-based Na-ion battery empowered by an improved understanding of materials transformation and electrolyte chemistry. First, we found that the conventional carbonate electrolyte with fluoroethylene carbonate additive causes an additional plateau (~2.4 V) at the end of the discharge process of the FePO 4 ||Na metal cell, leading to lower initial discharge capacity and voltage. This result shows that the voltage profile is influenced by not only intrinsic materials phase transformation during battery cycling but also the electrolyte additives and interphases formed. With the 1 M NaPF 6 diglyme electrolyte, we achieved an excellent capacity retention of 96% and 98% after 500 cycles at 1 and 5 C, respectively. Second, we chemically sodiated FePO 4 to form single-phase Na 0.9 FePO 4 . Na 0.9 FePO 4 ||hard carbon full cells demonstrated a remarkable capacity retention of ~84% at 3 and 5 C after 1000 cycles. The successful implementation of hard carbon, which can be derived from biomass waste, will further improve the sustainability of energy storage technologies. Our research demonstrates that electrolyte chemistry influences the voltage profile of phase-changing electrodes and provides effective electrolyte and full-cell design solutions for stable-cycling NaFePO 4 .

36 MATERIALS SCIENCE↗
DOE OSTI2018

Twin Crystal Induced near Zero Thermal Expansion in SnO 2 Nanowires

Knowledge of controllable thermal expansion is a fundamental issue in the field of materials science and engineering. Direct blocking of the thermal expansions in positive thermal expansion materials is a challenging but fascinating task. We report a near zero thermal expansion (ZTE) of SnO 2 achieved from twin crystal nanowires, which is highly correlated to the twin boundaries. Local structural evolutions followed by pair distribution function revealed a remarkable thermal local distortion along the twin boundary. Lattice dynamics investigated by Raman scattering evidenced the hardening of phonon frequency induced by the twin crystal compressing, giving rise to the ZTE of SnO 2 nanowires. Further DFT calculation of Grüneisen parameters confirms the key role of compressive stress on ZTE. Finally, our results provide an insight into the thermal expansion behavior regarding to twin crystal boundaries, which could be beneficial to the applications.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Atomic arrangement at ZnTe/CdSe interfaces determined by high resolution scanning transmission electron microscopy and atom probe tomography

High resolution scanning transmission electron microscopy and atom probe tomography experiments reveal the presence of an intermediate layer at the interface between two binary compounds with no common atom, namely, ZnTe and CdSe for samples grown by Molecular Beam Epitaxy under standard conditions. This thin transition layer, of the order of 1 to 3 atomic planes, contains typically one monolayer of ZnSe. Even if it occurs at each interface, the direct interface, i.e., ZnTe on CdSe, is sharper than the reverse one, where the ZnSe layer is likely surrounded by alloyed layers. On the other hand, a CdTe-like interface was never observed. This interface knowledge is crucial to properly design superlattices for optoelectronic applications and to master band-gap engineering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Mycobacterium Phage Butters-Encoded Proteins Contribute to Host Defense against Viral Attack [plus supplemental information]

A diverse set of prophage-mediated mechanisms protecting bacterial hosts from infection has been recently uncovered within cluster N mycobacteriophages isolated on the host, Mycobacterium smegmatis mc 2 155. In that context, we unveil a novel defense mechanism in cluster N prophage Butters. By using bioinformatics analyses, phage plating efficiency experiments, microscopy, and immunoprecipitation assays, we show that Butters genes located in the central region of the genome play a key role in the defense against heterotypic viral attack. Our study suggests that a two-component system, articulated by interactions between protein products of genes 30 and 31, confers defense against heterotypic phage infection by PurpleHaze (cluster A/subcluster A3) or Alma (cluster A/subcluster A9) but is insufficient to confer defense against attack by the heterotypic phage Island3 (cluster I/subcluster I1). Therefore, based on heterotypic phage plating efficiencies on the Butters lysogen, additional prophage genes required for defense are implicated and further show specificity of prophage-encoded defense systems. IMPORTANCE: Many sequenced bacterial genomes, including those of pathogenic bacteria, contain prophages. Some prophages encode defense systems that protect their bacterial host against heterotypic viral attack. Understanding the mechanisms undergirding these defense systems is crucial to appreciate the scope of bacterial immunity against viral infections and will be critical for better implementation of phage therapy that would require evasion of these defenses. Furthermore, such knowledge of prophage-encoded defense mechanisms may be useful for developing novel genetic tools for engineering phage-resistant bacteria of industrial importance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

On Principles, Values, Metrics and Criteria for the Development of Magnetic Fusion Energy

Fusion energy holds the promise of providing abundant, safe, clean and environmentally sustainable power for the foreseeable future. Hence, fusion has enormous potential to benefit society if it can be developed into a commercial energy source in a timely manner. However, the task of developing fusion energy is a challenging endeavor and it is believed that it will take several decades in order to reach the final goal. Hence, it is imperative to develop a strategic plan and to periodically revise and update this plan. In 2017, the Department of Energy (DOE) Office of Fusion Energy Sciences (FES) commissioned the National Academy of Sciences (NAS) to provide a long term strategic plan for magnetic fusion energy (MFE). In turn, the MFE community mobilized to provide input to the NAS panel. This resulted in a series of US Magnetic Fusion Energy Research Strategic Directions (US MFRSD) Workshops.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗