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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identifying Energy Inefficiencies in Compressed Air Systems: A Systematic Approach with the Compressed Air Scoping Tool

This paper highlights the importance of improving compressed air systems, outlines the tool’s development and features, and underscores its value as a nonbiased resource for system evaluation and optimization. Additionally, the paper presents a case study based on a food manufacturing facility to demonstrate the practical application of the tool. Furthermore, the paper discusses potential future opportunities for enhancing the CA Scoping Tool.

compressed air benchmarking↗
DOE OSTI2020

Alternative Fuels Data Center and Related Technology Integration Tools

The U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy's Vehicle Technologies Office (VTO) facilitates national coordination of the Clean Cities coalitions through its Technology Integration Program. VTO's Technology Integration Program supports a broad technology portfolio that can reduce transportation energy costs for businesses and consumers. Fleets, cities, and regions can use the tools featured in this guide to implement alternative fuels and advanced vehicles. Also learn how Clean Cities coalitions and their stakeholders across the country have used the tools.

AFDC↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2017

Chapter 8: Planning Tools to Simulate and Optimize Neighborhood Energy Systems

This section introduces different energy modeling tools available in Europe and the USA for community energy master planning process varying from strategic Urban Energy Planning to more detailed Local Energy Planning. Two modeling tools used for Energy Master Planning of primarily residential communities, the 3D city model with CityGML, and the Net Zero Planner tool developed for the US Department of Defense installations are described in more details.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Economic Consequence Analysis of Disasters: The ECAT Software Tool

This study develops a methodology for rapidly obtaining approximate estimates of the economic consequences from numerous natural, man-made and technological threats. This software tool is intended for use by various decision makers and analysts to obtain estimates rapidly. It is programmed in Excel and Visual Basic for Applications (VBA) to facilitate its use. This tool is called E-CAT (Economic Consequence Analysis Tool) and accounts for the cumulative direct and indirect impacts (including resilience and behavioral factors that significantly affect base estimates) on the U.S. economy. E-CAT is intended to be a major step toward advancing the current state of economic consequence analysis (ECA) and also contributing to and developing interest in further research into complex but rapid turnaround approaches. The essence of the methodology involves running numerous simulations in a computable general equilibrium (CGE) model for each threat, yielding synthetic data for the estimation of a single regression equation based on the identification of key explanatory variables (threat characteristics and background conditions). This transforms the results of a complex model, which is beyond the reach of most users, into a "reduced form" model that is readily comprehensible. Functionality has been built into E-CAT so that its users can switch various consequence categories on and off in order to create customized profiles of economic consequences of numerous risk events. E-CAT incorporates uncertainty on both the input and output side in the course of the analysis.

Environmental Economics↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2018

Replacement of Nitrided 33CrMoV Steel with ESR Hot Work Tool Steels for Motorsport Applications: Microstructural and Fatigue Characterization

In this work tensile strength, fatigue resistance and fracture toughness of two electroslag remelted (ESR) tool steels and of 33CrMoV12 ESR steel (both in quenched and tempered condition, as well as nitrided condition) were evaluated. The role of hardness, residual stresses and inclusion sizes on the fatigue behavior was investigated. Tool steels have a tensile strength between about 1900 and 2300 MPa, fracture toughness between 35 and 33 MPa√m, while fatigue strength ranges between 725 and 992 MPa. The tensile strength and fracture toughness of the 33CrMoV12 ESR are, respectively, 1365 MPa and about 150 MPa√m. Nitriding induces a significant increase in fatigue strength from 560 to 980 MPa. These results highlight that appropriate ESR tool steels could replace nitrided steels.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development of Multilayer Sinter Cladding of Cold Work Tool Steel on Hadfield Steel Plates for Wear-Resistant Applications

Machinery components used for mining and mineral processing activities are often subjected to high impact loads and wear which have placed demands for the development of materials with high resistance to dynamic loads and aggressive wear conditions. In this study, a multilayered cladding of high alloyed cold work tool steel (X245VCrMo9-4), interlayered with Hadfield steel (X120Mn12) plates, which was also used as substrate using super-solidus liquid-phase sintering technique was investigated. A stack of the cold work tool steel powder was prepared with interlayered X120Mn12 steel plates in an alumina crucible at tap density with the substrate placed on it and was sintered in a vacuum furnace at 1250 °C at a heating rate of 10 K/min, held for 30 min under a nitrogen atmosphere at 0.08 MPa and furnace-cooled. Sample from the as-sintered cladding was subjected to austenization at 1000 °C, quenched in oil and tempered at 150 °C for 2 h. Samples were subjected to microstructural examination using optical and scanning electron microscopy. The microstructural investigations were supplemented by hardness and impact wear tests. Computational thermodynamics was used to support experimental findings. The results revealed that a near-net densification of the sintered X245 was achieved with 99.93 ± 0.01% density. The sintered X245 was characterized by a dispersion of vanadium carbonitride precipitates, especially at the grain boundaries. The heat-treated X245 sample had the highest hardness of 680 ± 7 HV30 due to the matrix of tempered martensitic microstructure when compared to as-sintered with hardness of 554 ± 2 HV30. The X245/X120 interface was characterized by diffusion of Cr, Mo, Mn and C, which resulted in metallurgical bonding between the cladded materials. The impact wear resistance of the sintered X245 was eight times that of the X120; hence, a tough and wear-resistant tool is anticipated when the X120 work hardened in service.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Role of Invasive and Non-invasive Imaging Tools in the Diagnosis and Optimal Treatment of Patients with Spontaneous Coronary Artery Dissection

Purpose of Review: Spontaneous coronary artery dissection (SCAD) is a serious non-atherosclerotic disease, most frequently presenting as an acute coronary syndrome and affecting female patients. Considering that diagnosis of SCAD is often elusive, and its interventional treatment is associated to a higher rate of complications than obstructive atherosclerotic disease, we aim to review all the imaging tools currently available for the optimal diagnosis and treatment of this condition. Recent Findings: The developments in both invasive and non-invasive imaging alternatives to coronary angiography, such as intravascular ultrasound, optical coherence tomography, and computed coronary angiography, have largely contributed to appraise the epidemiology of SCAD, understand its causative pathophysiological mechanisms, and improve our ability to confirm doubtful cases of SCAD. Intracoronary imaging is also a valuable in deciding the best therapeutic approach and in guiding interventions in those patients requiring percutaneous treatment. Furthermore, non-invasive imaging is a key tool in ruling out significant extracoronary vascular abnormalities which frequently occur in patients with underlying conditions like fibromuscular dysplasia who develop SCAD. Summary: Main imaging tools employed in SCAD cases could have advantages and drawbacks. Focusing on different types of SCAD, operators should be able to choose the best imaging technique for diagnosis, management, and follow-up.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Nucleus++ : a new tool bridging AME and NUBASE for advancing nuclear data analysis

The newly developed software, Nucleus++ , is an advanced tool for displaying basic nuclear physics properties from NUBASE and integrating comprehensive mass information for each nuclide from Atomic Mass Evaluation. Additionally, it allows users to compare experimental nuclear masses with predictions from different mass models. Building on the success and learning experiences of its predecessor, Nucleus , this enhanced tool introduces improved functionality and compatibility. With its user-friendly interface, Nucleus++ was designed as a valuable tool for scholars and practitioners in the field of nuclear science. Finally, this article offers an in-depth description of Nucleus++ , highlighting its main features and anticipated impacts on nuclear science research.

AME↗
DOE OSTI2021

Interactive web mapping tools and custom subsurface cross-sections for interdisciplinary geologic investigation

Using Python-based geospatial analytics, open-source web mapping technologies, geophysical data models, and subsurface stratigraphy models from the Regional Geology Geologic Framework Model database assembled by Los Alamos National Laboratory, we developed a suite of web-based geologic investigation tools to identify and understand subsurface structures and geophysical properties concerning salt and shale formations within the contiguous United States. Coupled with a web map interface, these tools allow for the interactive visualization of various geologic data and demonstrate the ability to quickly generate custom subsurface cross-sections, borehole charts, and diagrams for azimuthal orientation data. These capabilities were developed for stakeholder and researcher use to facilitate informed decision making for spent nuclear waste disposition. However, these capabilities provide a flexible model for a variety of subsurface investigation needs, and we have demonstrated this flexibility by adapting these tools to meet visualization needs for various subsurface models within a web-based platform.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Tool-workpiece stick-slip conditions and their effects on torque and heat generation rate in the friction stir welding

Friction stir welding (FSW) has found increased applications in automotive and aerospace industries due to its advantages of solid-state bonding, no fusion and melting, and versatility in various working conditions and material combinations. However, the relationship among processing parameters, material properties, and bonding extent and fidelity remains largely empirical, primarily because of the lack of the mechanistic understanding of the tool-workpiece frictional behavior that affects our subsequent understanding of microstructural evolution and interface bonding formation. While the tool-workpiece stick-slip condition is believed to dictate the resulting torque and heat generation rate during the welding process, it remains rare and elusive to conduct a quantitative experimental measurement of such interfacial field. On the other hand, numerical simulations based on Computational Fluid Dynamics (CFD) rely on ad hoc assumptions of interfacial pressure and shear-stress conditions, but predictions can only be validated via the medium- and far-range temperature field which is known to be insensitive to the interfacial frictional behavior. This work first presents a comparison among two CFD-based simulation methodologies and the Coupled Eulerian Lagrangian (CEL) model in finite element method, the last of which uses the Coulomb friction so that the stick-slip is naturally developed. Based on the Hill-Bower similarity relationship in the contact analysis, an analytical model is developed here to prove why a constant stick-slip fraction will be developed in the steady state, to correlate the stick-slip fraction to processing parameters such as the tool spin rate, and further to derive dimensionless functions for torque and heat-generation-rate predictions. Pros and cons of various numerical approaches in predicting stick-slip are discussed, and our analytical model has been found to agree well with our numerical simulation and literature experimental results. These analyses provide the critical strain-rate and temperature fields that are needed for the bonding analysis in our future work.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microstructure evolution during binder jet additive manufacturing of H13 tool steel

H13 is one of the most used tool steels for both hot and cold work tooling applications. Binder jet additive manufacturing offers the potential to deposit complex tools at scale due to larger powder bed sizes and faster deposition rates. However, to date there is no published literature on the sintering of H13 to full densification. Here, we discuss the pressureless sintering of binder jet AM H13 steel to full densification via supersolidus liquid phase sintering (SLPS) while presenting appropriate process windows (1360 °C – 1380 °C) for densification without distortion. The process windows have been rationalized based on thermodynamic calculations of liquid volume fractions with temperature. We show that higher binder saturation results in higher carbon retention and subsequently early liquid formation that can initiate the sintering process at lower temperatures. We report abnormal grain growth during sintering and found that the solidification phase transformations play a critical role on microstructural evolution and must be considered to accurately model the kinetics of SLPS.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Preliminary verification of the MCNP perturbation and fixed-source tally sensitivity tools

Integral benchmark experiments are vital in the adjustment and validation of the nuclear data that govern predictive simulations across the nuclear community. The nuclear data sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®) transport code are currently limited; however, expanding sensitivity capabilities will allow benchmark experiments to be designed to resolve compensating errors and adjust nuclear data where previously prohibitively difficult. This paper provides details of a preliminary verification for the use of (i.) the recently revised perturbation and (ii.) developmental fixed-source sensitivity tools within MCNP to calculate sensitivities of tallied responses (such as current integrated over a surface, F1, and flux averaged over a cell, F4) to nuclear data in fixed-source simulations. Energy-binned and energy-integrated sensitivities calculated with these tools are compared against sensitivities calculated using a central-difference approximation. Here, the verification is completed for four configurations of a benchmarked system using a 4.5-kg plutonium sphere surrounded by varying amounts of copper and/or polyethylene. The results show that sensitivities calculated with the perturbation and fixed-source sensitivity tools agree with the central-difference-based approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

MADHAT: Model-Agnostic Dark Halo Analysis Tool

We present the Model-Agnostic Dark Halo Analysis Tool (MADHAT), a numerical tool which implements a Fermi-LAT data-driven, model-independent analysis of gamma-ray emission from dwarf satellite galaxies and dwarf galaxy candidates due to dark matter annihilation, dark matter decay, or other nonstandard or unknown astrophysics. This tool efficiently provides statistical upper bounds on the number of observed photons in excess of the number expected, based on empirical determinations of foregrounds and backgrounds, using a stacked analysis of any selected set of dwarf targets. It also calculates the resulting bounds on the properties of dark matter under any assumptions the user makes regarding dark sector particle physics or astrophysics. As an application, we determine new bounds on Sommerfeld-enhanced dark matter annihilation in a set of eight dwarfs. MADHAT v1.0 includes 58 dwarfs and dwarf candidate targets, and we discuss future planned developments. MADHAT is available and will be maintained at https://github.com/MADHATdm.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Stakeholder perceptions of Environmental Management Plans as an environmental protection tool for major developments in the UK

Insufficient implementation and the lack of legislative requirements for follow-up measures following the approval of projects are consistently highlighted as major shortcomings of Environmental Impact Assessment (EIA). Although adopted over 15 years ago by the World Bank, Environmental Management Plans (EMPs) were only semi-formalised in the UK in 2008 and arguably provide a continuous link or ‘bridge’ between the EIA process pre-consent and an Environmental Management System (EMS) post-consent. Drawing on twenty-one semi-structured interviews with stakeholders and thematic analysis of their responses, and a broad-scale practitioner survey, this study aimed to assess the effectiveness of EMPs as an environmental protection tool across the project lifecycle for major developments. The findings revealed a mixed picture of EMP effectiveness in practice, with EMPs only partially fulfilling a bridging role between EIA and EMS. There is no ‘gold standard’ terminology for EMPs, all having slightly different uses, thus presenting different focuses to different stakeholders and further enhancing variation in practice. For many stakeholders, the effectiveness was simply not known, due to the lack of communication and follow-up that still exists. EMP–EMS linkages were shown to be effective from the developer's perspective when a single organisation has involvement across all project phases, though weaknesses occur when multiple parties are involved. Among other stakeholders, knowledge varied significantly; whilst some were in agreement that the linkages worked, many were unaware of the connections and thought of them as two quite separate tools. Stakeholders advocated for the need to make EMPs a legal requirement; for improved communication between stakeholders during EMP implementation and increased documentation of project outcomes; and for EMPs to be consistently written by environmental professionals. Furthermore, weak links in the current process may be improved by providing detailed guidance for organisations on the potential for EMP–EMS linkages, with the additional aim of encouraging stakeholders to broaden their current specialist knowledge on environmental protection tools. - Highlights: • We assessed the effectiveness of Environmental Management Plans (EMPs) for major developments, focusing on delivery of mitigation and monitoring. • EIA and Environmental Management Systems (EMS) post-consent are weakly linked. • The use of EMPs varies significantly in practise, partly due to the range of terminology used. • Linkages are hampered by poor communication and follow-up • More detailed guidance on how EMPs can successfully link with EMS is required.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Overcoming low EIA performance - A diagnostic tool for the deliberate development of EIA system capacities in low and middle income countries

Highlights: • A tool for rapid assessment of EIA performance is presented. • The tool focusses on EIA systems in low and middle income countries. • This tool aims to enhance the capacities of the main EIA organizations. • Mechanisms are identified to maintain and further develop the enhanced capacities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗