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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Topological Control of Triply Periodic Minimal Surfaces for Thermal Design and Advanced Manufacturing: A Gyroid Case Study

Recently, there has been a heightened interest in using triply periodic minimal surfaces (TPMSs) in the design of compact process engineering components. The benefits of high surface area per unit volume, modular form, and inherent periodicity provide a holistic self-supporting network and flow-conducive features. Applications of importance include thermal power management, biomimetic scaffolds and structures, and feasibility of advanced manufacturing. This study presents a novel approach to the manipulation of the characteristic Schwarz-G, or gyroid TPMS, for thermal design in the context of advanced manufacturing. The study presents relationships between design parameters and resulting surface area as a target response using the characteristic equation of a gyroid. Through parametric control, the characteristic equation is manipulated to produce a 20-fold increase in achievable area over a baseline design characteristic of 25.4 mm through controlled combinations of design parameters. A second relationship is presented as a function of the maximum area achieved and manipulated design parameters. Through the analysis, the study presents a framework to identify and maximize the achievable area of TPMSs for advanced manufacturing and thermal management applications.

gyroid↗
DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Advanced Manufacturing of Alpha Double Prime Iron Nitride (ADPIN): An Innovative Rare Earth Element (REE) Free Ultra-High Performance Permanent Magnet for Clean Energy Applications

Iron nitride magnets offer the potential for large magnetic remanence magnets absent any rare earth materials [1]. This project was submitted in response to the funding opportunity announcement (FOA) from the Advanced Manufacturing Office (AMO) of the Office of Energy Efficiency & Renewable Energy (EERE) of the Department of Energy (DOE): DE-FOA-0001465: Advanced Manufacturing Projects for Emerging Research Exploration; Topic Area 1: Advanced Materials; Subtopic 1.1: Innovative Advanced Materials Manufacturing for Clean Energy to explore nitriding iron powder using a fluidized bed technique which if successful would accomplish nitriding the iron powder with reduce industrial energy intensity. Of particular concern when using metal powders at high temperatures in a fluidized bed reactor is the defluidization temperature of the bed, also known as the ‘bed collapse’ temperature. Above the defluidization temperature the metal powders can no longer fluidize and instead become an undesirable packed powder bed. Using the published results from the Institute of Process Engineering at the Chinese Academy of Sciences in Beijing, China, FeNix Magnetics was able to develop a spreadsheet calculation that allowed predictive guidelines for the defluidization temperature of iron powder based on the type of carrier gas, flow rate of the carrier gas, and the iron powder diameter. FeNix Magnetics was not able to conclude whether the temperatures to avoid bed defluidization that were achievable in a fluidized bed reactor actually resulted in nitriding the iron powder to the required level. In order to measure the amount of nitrogen in the iron powder, FeNix Magnetics contracted with the DOE sponsored Advanced Photon Source (APS) at Argonne National Laboratory to conduct X-Ray diffraction measurements. Unfortunately, due to COVID-19 restrictions, the DOE sponsored APS was closed and unable to provide X-Ray Diffraction measurements during this program.

36 MATERIALS SCIENCE↗
DOE OSTI2024

FENIX: An Open-Source Multiphysics Integrated Framework Enabling Collaborative Development of Plasma Facing Component Modeling Capabilities

Advanced modeling and simulation tools have a crucial role to play in accelerating fusion energy deployment as a sustainable power source. Multiphysics, high-fidelity computational tools can help understand, model, and quantify the complex interactions between materials performance, plasma and neutron exposure, and engineering processes. As a result, they accelerate the design, safety analysis, and performance evaluation of fusion systems. This webinar introduces the Fusion ENergy Integrated multiphys-X (FENIX) framework, an open-source multiphysics tool for plasma facing component modeling. FENIX leverages the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has been developed by the United States Department of Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. FENIX couples various MOOSE capabilities such as heat transfer, thermomechanics, thermal hydraulics, electromagnetics, and plasma kinetics with the MOOSE-based applications Cardinal (neutronics) and TMAP8 (tritium transport). During the webinar, we will present FENIX and discuss how its modularity, openness, software quality assurance processes, and licensing approach supports effective collaborations, including public-private partnerships.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Additive manufacturing of a metastable high entropy alloy: Metastability engineered microstructural control via process variable driven elemental segregation

Compositional paradigm shift in high entropy alloys (HEAs) provided new opportunities for microstructural engineering, whereas process control during laser powder bed fusion (LPBF) additive manufacturing (AM) enable fine microstructure tailoring. Metastability engineering in transformation induced plasticity (TRIP) HEAs by addition of minor alloying elements is an attractive strategy for fine microstructural tuning. This study explored in detail the microstructural evolution during LPBF AM of a metastable Fe 40 Mn 20 Co 20 Cr 15 Si 5 (at.%) dual phase HEA (CS-HEA). LPBF processing window for CS-HEA was established based on quantitative analysis and experiments. Based on melt pool overlap lack of fusion pores were observed at lower energy densities (J) of $\textit{J}$ ≤ 31.25 J/mm 3 and key-hole formation by melt pool destabilization in case of $\textit{J}$ ≥ 75 J/mm 3 . The microstructure of CS-HEA consists of metastable FCC-γ and HCP-ε phases; LPBF process parameters governed the final phase fraction in the alloy which has been correlated to metastability alteration of the high temperature γ phase. Final microstructural engineering was devised by LPBF process control which enabled cooling rate manipulation to guide Mn and Si segregation at the cell boundaries, thereby controlling the matrix metastability and final phase fraction. Additionally, high resolution transmission electron microscopy (TEM) revealed disparity in stacking fault morphology in CS-HEA with LPBF process variable alterations associated with local variations in chemical composition and stacking fault energy. Further, the phase evolution with process parameters also affected the nanomechanical behavior of the alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient turbine engine using integrated ammonia fuel processing

A gas turbine engine includes a core engine that includes a core flow path where air is compressed in a compressor section, communicated to a combustor section, mixed with an ammonia based fuel and ignited to generate a high energy combusted gas flow that is expanded through a turbine section. The turbine section is mechanically coupled to drive the compressor section. An ammonia flow path communicates an ammonia flow to the combustor section. A cracking device is disposed in the ammonia flow path. The cracking device is configured to decompose the ammonia flow into a fuel flow containing hydrogen (H2). At least one heat exchanger is upstream of the cracking device that provides thermal communication between the ammonia flow and a working fluid flow such that the ammonia fluid flow accepts thermal energy from the working fluid flow.

Smith, Lance L.↗
DOE OSTI2025

Process Systems Engineering-Informed Design and Scale-Up of Multi-stage Diafiltration Cascades for Lithium and Cobalt Recovery from Spent Lithium-Ion Batteries

These slides present work jointly completed by Tasks in PrOMMiS. The first half of the presentation motivates the importance of critical materials for national security and how the recovery of critical minerals via membrane separations can be more cost effective than currently used technology. The second half of the presentation presents cost-optimal results for the custom cost model for diafiltration using the superstructure flowsheet developed by CMU. These results highlight how PSE can inform process targets (i.e., product purity targets) and suitable design strategies for scaled-up membrane cascades.

critical materials↗
DOE OSTI2026

Molecular Additive Engineering for Process-Humidity Robustness and Reproducible Fabrication of Perovskite Solar Cells and Modules

The commercialization of perovskite solar cells (PSCs) faces significant challenges due to their sensitivity to environmental humidity, which compromises film crystallization and device stability. Here, we introduce diphenylvinylphosphine (DPVP) as a Lewis base additive that enhances the performance and reproducibility of PSCs fabricated under ambient-air conditions. DPVP suppresses moisture-induced defect formation and stabilizes crystallization within realistic process-humidity ranges (20–40% relative humidity) commonly encountered in laboratory and pilot-scale manufacturing environments. It improves film uniformity, reduces trap densities, and yields highly reproducible device performance, enabling champion PCEs of 24.2% in small-area devices and 20.5% in blade-coated 12 cm 2 mini-modules. Furthermore, DPVP-assisted modules exhibit enhanced stability, retaining over 85% of their initial efficiency after 900 h of maximum power point tracking (MPPT) at 65 °C. This study demonstrates a humidity-resilient and scalable additive strategy for ambient-air perovskite photovoltaic manufacturing.

defect passivation↗
DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2020

Identification of engine oil-derived ash nanoparticles and ash formation process for a gasoline direct-injection engine

Engine oil-derived ash particles emitted from internal combustion (IC) engines are unwanted by-products, after oil is involved in in-cylinder combustion process. Since they typically come out together with particulate emissions, no detail has been reported about their early-stage particles other than agglomerated particles loaded on aftertreatment catalysts and filters. To better understand ash formation process during the combustion process, here differently formulated engine oils were dosed into a fuel system of a gasoline direct injection (GDI) engine that produces low soot mass emissions at normal operating conditions to increase the chances to find stand-alone ash particles separated from soot aggregates in the sub-20-nm size range. In addition to them, ash/soot aggregates in the larger size range were examined using scanning transmission electron microscopy (STEM)-X-ray electron dispersive spectroscopy (XEDS) to present elemental information at different sizes of particles from various oil formulations. The STEM-XEDS results showed that regardless of formulated oil type and particle size, Ca, P and C were always contained, while Zn was occasionally found on relatively large particles, suggesting that these elements get together from an early stage of particle formation. The S, Ca and P K-edge X-ray absorption near edge structure (XANES) analyses were performed for bulk soot containing raw ash. The linear combination approach & cross-checking among XANES results proposed that Ca 5 (OH)(PO 4 ) 2 , Ca 3 (PO 4 ) 2 and Zn 3 (PO 4 ) 2 are potentially major chemical compounds in raw ash particles, when combined with the STEM-XEDS results. Despite many reports that CaSO 4 is a major ash chemical when ash found in DPF/GFP systems was examined, it was observed to be rarely present in raw ashes using the S K-edge XANES analysis, suggesting ash transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli.

Here, we report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. Deregulated networks are less sensitive to environmental conditions improving process robustness. Process robustness in turn leads to predictable scalability, minimizing the need for traditional process optimization. We validate process robustness and scalability of strains and bioprocesses synthesizing the important industrial chemicals alanine, citramalate and xylitol. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Mono-Ether and Alcohol Bioblendstocks to Reduce the Fuel Penalty of Mixing Controlled Compression Ignition (MCCI) Engine Aftertreatment

An integrated approach utilizing catalysis experiments, process systems engineering, fuel property modeling, and engine testing was utilized in this project to optimize the production process and composition of a #2 diesel bioblendstock produced from ethanol consisting primarily of long-chain mono-ethers. The primary objective of the project was to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. The desired bioblendstock needed to be blendable with #2 diesel fuel at > 5 vol. % while still meeting ASTM D975 diesel fuel specification properties and achieving improvements in fuel properties: increased cetane number, decreased sooting, and reduced pour point and cloud point temperatures. At the same time, it needed to reduce the fuel energy penalty associated with MCCI engine aftertreatment resulting in improved system efficiency. The findings of the current project demonstrate that primary objective of the work has been met, i.e., to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. Additionally, the results indicate that the property objectives (increased cetane number, reduced pour and cloud points, and reduced sooting propensity) for the designed bioblendstock composition have also been met. Engine testing performed has also confirmed that the increased reactivity of the bioblendstock can be used to improve catalyst heating operation and to reduce the fuel penalty associated with this operation mode. The fuel property results demonstrate that >5 vol.% blending is easily achieved while meeting the ASTM D975 #2 diesel fuel property specifications tested in this work, as this was achieved for a blend with 43 vol. % of the bioblendstock. The results provide a foundation for future work to scaleup the catalytic production process designed in this work, with many of the challenges and areas for improvement being identified in this work to enable economic production with low GHG lifecycle emissions.

09 BIOMASS FUELS↗