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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An automated integrated web-based smart tool for open stope design

The Stability Graph is a widely used tool for the design of open stopes in underground mining. Many users of the Stability Graph still apply this design method manually. Although the manual approach has benefits, using multiple graphs and stability number computation charts for each stope surface is time-consuming, even for the experienced mining engineer. Current practice in the use of the method also limits data sharing. This paper presents a StopeSoft web-based tool for open stope stability prediction that is developed on the basis of the Stability Graph method and is available at openstope.com. StopeSoft incorporates flexibility in terms of Stability Graph options and incorporates additional critical factors often overlooked. As a web-based tool, StopeSoft encourages and makes data sharing possible globally, focused on expanding the database and improving the current limitations of the Stability Graph to provide practical, reliable solutions for mining engineers, consultants, and academics. The StopeSoft automated process facilitates the process of open stope stability prediction, saving time and minimizing potential human errors. Statistical treatment of the data accounts for the variability of input parameters to emphasize the probabilistic nature of the Stability Graph method. The probabilistic interpretation of the stability states of stope surfaces eliminates the false feeling of absolute stope performance based on its location on the Stability Graph , as implied by the deterministic approach.

58 GEOSCIENCES↗
DOE OSTI2022

Molecular mechanism of active Cas7-11 in processing CRISPR RNA and interfering target RNA

Cas7-11 is a Type III-E CRISPR Cas effector that confers programmable RNA cleavage and has potential applications in RNA interference. Cas7-11 encodes a single polypeptide containing four Cas7- and one Cas11-like segments that obscures the distinction between the multi-subunit Class 1 and the single-subunit Class-2 CRISPR Cas systems. We report a cryo-EM (cryo-electron microscopy) structure of the active Cas7-11 from Desulfonema ishimotonii (DiCas7-11) that reveals the molecular basis for RNA processing and interference activities. DiCas7-11 arranges its Cas7- and Cas11-like domains in an extended form that resembles the backbone made up by four Cas7 and one Cas11 subunits in the multi-subunit enzymes. Unlike the multi-subunit enzymes, however, the backbone of DiCas7-11 contains evolutionarily different Cas7 and Cas11 domains, giving rise to their unique functionality. The first Cas7-like domain nearly engulfs the last 15 direct repeat nucleotides in processing and recognition of the CRISPR RNA, and its free-standing fragment retains most of the activity. Both the second and the third Cas7-like domains mediate target RNA cleavage in a metal-dependent manner. The structure and mutational data indicate that the long variable insertion to the fourth Cas7 domain has little impact on RNA processing or targeting, suggesting the possibility for engineering a compact and programmable RNA interference tool.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Biomass Logistics

Equipment manufacturers have made rapid improvements in biomass harvesting and handling equipment. These improvements have increased transportation and handling efficiencies due to higher biomass densities and reduced losses. Improvements in grinder efficiencies and capacity have reduced biomass grinding costs. Biomass collection efficiencies (the ratio of biomass collected to the amount available in the field) as high as 75% for crop residues and greater than 90% for perennial energy crops have also been demonstrated. However, as collection rates increase, the fraction of entrained soil in the biomass increases, and high biomass residue removal rates can violate agronomic sustainability limits. Advancements in quantifying multi-factor sustainability limits to increase removal rate as guided by sustainable residue removal plans, and mitigating soil contamination through targeted removal rates based on soil type and residue type/fraction is allowing the use of new high efficiency harvesting equipment and methods. As another consideration, single pass harvesting and other technologies that improve harvesting costs cause biomass storage moisture management challenges, which challenges are further perturbed by annual variability in biomass moisture content. Monitoring, sampling, simulation, and analysis provide basis for moisture, time, and quality relationships in storage, which has allowed the development of moisture tolerant storage systems and best management processes that combine moisture content and time to accommodate baled storage of wet material based upon “shelf-life.” The key to improving biomass supply logistics costs has been developing the associated agronomic sustainability and biomass quality technologies and processes that allow the implementation of equipment engineering solutions.

09 BIOMASS FUELS↗
DOE OSTI2015

New Whole-House Solutions Case Study: Northwest Energy Efficient Manufactured Housing Program High-Performance Test Homes - Pacific Northwest

This project represents the third phase of a multi-year effort to develop and bring to market a High Performance Manufactured Home (HPMH). In this project, the Northwest Energy Efficient Manufactured Housing Program worked with Building America Partnership for Improved Residential Construction and Bonneville Power Administration to help four factory homebuilders build prototype zero energy ready manufactured homes, resulting in what is expected to be a 30% savings relative to the Building America Benchmark. (The actual % savings varies depending on choice of heating equipment and climate zone). Previous phases of this project created a HPMH specification and prototyped individual measures from the package to obtain engineering approvals and develop preliminary factory construction processes. This case study describes the project team's work during 2014 to build prototype homes to the HPMH specifications and to monitor the homes for energy performance and durability. Monitoring is expected to continue into 2016.

BA-PIRC↗
DOE OSTI2016

Lessons Learned from FUSRAP

The US DOE Office of Legacy Management (LM) is the long-term steward for 90 sites remediated under numerous regulatory regimes including the Formerly Utilized Sites Remedial Action Program (FUSRAP) sites. In addition, LM holds considerable historical information, gathered in the 1970s, to determine site eligibility for remediation under FUSRAP. To date, 29 FUSRAP sites are in LM’s inventory of sites for long-term surveillance and maintenance (LTS&M), and 25 are with the US Army Corps of Engineers (USACE) for remediation or in the process of being transitioned to LM. It is forecasted that 13 FUSRAP sites will transfer from the USACE to LM over the next 10 years; however, the timing of the transfers is strongly dependent upon federal funding of the ongoing remedial actions. Historically, FUSRAP sites were generally cleaned up for “unrestricted” industrial use or remediated to the “cleanup standards” at that time, and their use remained unchanged. Today, these sites as well as the adjacent properties are now changing or envisioned to have changes in land use, typically from industrial to commercial or residential uses. The implication of land-use change affects DOE’s LTS&M responsibility for the sites under LM stewardship as well as the planning for the additional sites scheduled to transition in time. Coinciding with land-use changes at or near FUSRAP sites is an increased community awareness of these sites. As property development increases near FUSRAP sites, the general public and interested stakeholders regularly inquire about the sufficiency of cleanups that impact their neighborhoods and communities. LM has used this experience to address a series of lessons learned to improve our program management in light of the changing conditions of our sites. We describe these lessons learned as (1) improved stakeholder relations, (2) enhanced LTS&M requirements for the sites, and (3) greater involvement in the transition process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

3D printed materials, stress-strain behavior, mult↗
DOE OSTI2017

Effect of Socio-Political Factors in Nuclear Power Development in Multi-National Framework

Nuclear fuel cycle is a complex process with numerous steps influenced by both engineering and socio-economic factors. A number of studies have been done in this area providing a comprehensive view of nuclear fuel cycle in term of the energy scenarios, material flows, or pricing mechanism. However, the effect of other socio-economic aspects like public acceptance, proliferation risks, or the trans-boundary nature of the nuclear fuel cycle have not been investigated thoroughly. This research is an attempt to develop such a hybrid model of multinational nuclear fuel cycle using system dynamics and the quantifiable effects of political and social impacts on the development of nuclear fuel cycle through multi-attribute analysis of historical data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

On a Thermodynamic Approach to Material Selection for Service in Aggressive Multi-Component Gaseous and/or Vapor Environments

This report fulfills the M4 milestone, M4FT-15IN08020110 UNF Analysis Support, under Work Package Number FT-15IN080201. The issue of materials selection for many engineering applications represents an important problem, particularly in cases where material failure is possible as a result of corrosive environments. For example, 304 dual purpose or 316 stainless steel is used in the construction of many used nuclear fuel storage canisters. Deployed all over the world, these canisters are housed inside shielded enclosures and cooled passively by convective airflow. When located along seaboards or particular industrial areas, salt, other corrosive chemicals, and moisture can become entrained in the air that cools the canisters. It is important to develop an understanding of what impact, if any, that chemical environment will have on those canisters. In many cases of corrosion in aggressive gaseous environments, the material selection process is based on some general recommendations, anecdotal evidence, and/or the past experience of that particular project’s participants. For gaseous mixtures, the theoretical basis is practically limited to the construction of the so-called “Ellingham diagrams” for pure metals. These plots predict the equilibrium temperature between different individual metals, their respective oxides, and oxygen gas. Similar diagrams can be constructed for the reactions with sulfur, nitrogen, carbon, etc. In the generalization of this approach by Richardson and Jeffes, additional scales can be superimposed upon an Ellingham diagram that would correspond to different gaseous mixtures, e.g. CO/CO 2 , or H 2 /H 2 O. However, while the general approach to predicting the stability of a multi-component heterogeneous alloy (e.g., steel or a superalloy) in a multi-component aggressive gaseous environment was developed in very general form, actual examples of its applications to concrete real-life problems are practically absent. This is related to alloy design, corrosion protection, and material selection for different applications. In this work, an effort was made to advance in that direction using modern computational thermodynamics methodology, software, and databases by Thermo-Calc Inc. The developed methodology is illustrated by the case study – a process of nuclear waste immobilization using a chemical engineering approach described below. The developed methodology can be considered a practical illustration of the Ellingham approach generalization and could be used for obtaining thermodynamic guidance on a given process’ feasibility using equipment/sensors made of a particular multicomponent heterogeneous metallic alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2018

Engineering Design with Digital Thread

Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. Though Digital Thread is gaining traction as a digital communication framework to streamline design, manufacturing, and operation processes in order to more efficiently design, build, and maintain engineering products, a principled mathematical formulation describing the manner in which Digital Thread can be used for critical design decisions remains absent. The contribution of this paper is to present such a formulation from the context of a data-driven design and decision problem under uncertainty. This formulation accounts for the fact that the design process is highly iterative and not all information is available at once. Output design decisions are made not only on what data to collect but also on the costs and benefits involved in experimentation and sensor instrumentation to collect that data. The mathematical formulation is illustrated through an example design of a structural fiber-steered composite component. In this example, the methodology highlights how different sequencing of small-scale experimentation with manufacturing and deployment lead to different designs and different associated costs.

42 ENGINEERING↗
DOE OSTI2016

Experimental study on the effect of nozzle hole-to-hole angle on the near-field spray of diesel injector using fast X-ray phase-contrast imaging

Fuel atomization and vaporization process play a critical role in determining the engine combustion and emission. The primary near-nozzle breakup is the vital link between the fuel emerging from the nozzle and the fully atomized spray. In this study, the near-nozzle spray characteristics of diesel injector with different umbrella angle (UA) were investigated using high-speed X-ray phase-contrast imaging and quantitative image processing. A classic ‘dumbbell’ profile of spray width (SW) composed of three stages: opening stage, semisteady stage and closing stage. The SW peak of two-hole injectors was more than twice of that of single-hole injector at the opening and closing stages, corresponding to the hollow-cone spray. This indicated the vortex flow was formed with the increase of the UA. The higher injection pressure had little influence on the SW while led to earlier breakup closer to the nozzle. Significant fuel effect on the SW at higher needle lift was found. However, this effect could be neglect at lower needle lift due to the leading role of internal flow and cavitation on the near-field spray characteristics. In addition, the morphology-based breakup process was observed, which highlighted the important effect of internal flow on the spray development. The possibility of using hollow-cone spray in diesel injector was also discussed.

biodiesel↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2014

U.S. Wind Energy Manufacturing and Supply Chain: A Competitiveness Analysis

The goal of the project was to develop a greater understanding of the key factors determining wind energy component manufacturing costs and pricing on a global basis in order to enhance the competitiveness of U.S. manufacturers, and to reduce installed systems cost. Multiple stakeholders including DOE, turbine OEMs, and large component manufactures will all benefit by better understanding the factors determining domestic competitiveness in the emerging offshore and next generation land-based wind industries. Major objectives of this project were to: 1. Carry out global cost and process comparisons for 5MW jacket foundations, blades, towers, and permanent magnet generators; 2. Assess U.S. manufacturers’ competitiveness and potential for cost reduction; 3. Facilitate informed decision-making on investments in U.S. manufacturing; 4. Develop an industry scorecard representing the readiness of the U.S. manufacturers’ to produce components for the next generations of wind turbines, nominally 3MW land-based and 5MW offshore; 5. Disseminate results through the GLWN Wind Supply Chain GIS Map, a free website that is the most comprehensive public database of U.S. wind energy suppliers; 6. Identify areas and develop recommendations to DOE on potential R&D areas to target for increasing domestic manufacturing competitiveness, per DOE’s Clean Energy Manufacturing Initiative (CEMI). Lists of Deliverables 1. Cost Breakdown Competitive Analyses of four product categories: tower, jacket foundation, blade, and permanent magnet (PM) generator. The cost breakdown for each component includes a complete Bill of Materials with net weights; general process steps for labor; and burden adjusted by each manufacturer for their process categories of SGA (sales general and administrative), engineering, logistics cost to a common U.S. port, and profit. 2. Value Stream Map Competitiveness Analysis: A tool that illustrates both information and material flow from the point of getting a customer order at the manufacturing plant; to the orders being forwarded by the manufacturing plant to the material suppliers; to the material being received at the manufacturing plant and processed through the system; to the final product being shipped to the Customer. 3. Competitiveness Scorecard: GLWN developed a Wind Industry Supply Chain Scorecard that reflects U.S. component manufacturers’ readiness to supply the next generation wind turbines, 3MW and 5MW, for land-based and offshore applications. 4. Wind Supply Chain Database & Map: Expand the current GLWN GIS Wind Supply Chain Map to include offshore elements. This is an on-line, free access, wind supply chain map that provides a platform for identifying active and emerging suppliers for the land-based and offshore wind industry, including turbine component manufacturers and wind farm construction service suppliers.

17 WIND ENERGY↗
DOE OSTI2020

Gasoline Compression Ignition (GCI) on a Light-Duty Multi-Cylinder Engine Using a Wide Range of Fuel Reactivities and Heavy Fuel Stratification

Many research studies have focused on utilizing gasoline in modern compression ignition engines to reduce emissions and improve efficiency. Collectively, this combustion mode has become known as gasoline compression ignition (GCI). One of the biggest challenges with GCI operation is maintaining control over the combustion process through the fuel injection strategy, such that the engine can be controlled on a cycle-by-cycle basis. Research studies have investigated a wide variety of GCI injection strategies (i.e., fuel stratification levels) to maintain control over the heat release rate while achieving low temperature combustion (LTC). This work shows that at loads relevant to light-duty engines, partial fuel stratification (PFS) with gasoline provides very little controllability over the timing of combustion. On the contrary, heavy fuel stratification (HFS) provides very linear and pronounced control over the timing of combustion. However, the HFS strategy has challenges achieving LTC operation due to the air handling burdens associated with the high EGR rates that are required to reduce NOx emissions to near zero levels. In this work, a wide variety of gasoline fuel reactivities (octane numbers ranging from < 40 to 87) were investigated to understand the engine performance and emissions of HFS-GCI operation on a multi-cylinder light-duty engine. The results indicate that over an EGR sweep at 4 bar BMEP, the gasoline fuels can achieve LTC operation with ultra-low NOx and soot emissions, while conventional diesel combustion (CDC) is unable to simultaneously achieve low NOx and soot. At 10 bar BMEP, all the gasoline fuels were compared to diesel, but using mixing controlled combustion and not LTC.

Dempsey, Adam↗
DOE OSTI2016

Mesoscale to microscale wind farm flow modeling and evaluation

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so-called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to direct future research in this area.

17 WIND ENERGY↗
DOE OSTI2024

The Chalkboard: An Introduction to Electrochemical Separations

Chemical separations are a cornerstone of industrial manufacturing processes that generate products and services that have improved the standard of living for humans across the globe. To give some context as to how ubiquitous separations are in the modern world, they are involved in the production of fuels, medicines, clean water, fertilizers, materials used in semiconductor chip manufacturing, and other goods. A 2019 report by the National Academies of Sciences, Engineering, and Medicine highlights that chemical separations account for about 10 to 15% of energy use in the United States. Of the four broad sectors (residential, transportation, industry, and commerce) that use energy in the United States, industry has the largest use at 32% and about half of the energy use in industry hails from separations. It is likely that the transportation and residential sectors will experience significant decarbonization in the next 25 years with the proliferation of wind and solar energy sources coupled with electrochemical energy storage and electrification of vehicles. Finally, industrial decarbonization, on the other hand, is far more complex and challenging and it is imperative that future engineers and scientists work hard to devise alternative processes that can be powered on renewable electrons while generating little waste to produce the goods and services that make up our modern lives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗