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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Analyzing Real-World Light Duty Vehicle Efficiency Benefits

Off-cycle technologies represent an important pathway to achieve real-world fuel savings, through which OEMs can potentially receive credit toward CAFE compliance. DOE national labs such as NREL are well positioned to provide objective input on these technologies using large, national data sets in conjunction with OEM- and technology-specific testing. This project demonstrates an approach that combines vehicle testing (dynamometer and on-road) with powertrain modeling and simulation over large, representative datasets to quantify real-world fuel economy. The approach can be applied to specific off-cycle technologies (engine encapsulation, start/stop, connected vehicle, etc.) in A/B comparisons to support calculation of realistic real-world impacts. Future work will focus on testing-based A/B technology comparisons that demonstrate the significance of this approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolution in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective

Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Advanced virtual energy simulation training and research: IGCC with CO2 capture power plant

In this presentation, we highlight the deployment of a real-time dynamic simulator of an integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture at the Department of Energy's (DOE) National Energy Technology Laboratory's (NETL) Advanced Virtual Energy Simulation Training and Research (AVESTARTM) Center. The Center was established as part of the DOE's accelerating initiative to advance new clean coal technology for power generation. IGCC systems are an attractive technology option, generating low-cost electricity by converting coal and/or other fuels into a clean synthesis gas mixture in a process that is efficient and environmentally superior to conventional power plants. The IGCC dynamic simulator builds on, and reaches beyond, conventional power plant simulators to merge, for the first time, a 'gasification with CO{sub 2} capture' process simulator with a 'combined-cycle' power simulator. Fueled with coal, petroleum coke, and/or biomass, the gasification island of the simulated IGCC plant consists of two oxygen-blown, downward-fired, entrained-flow, slagging gasifiers with radiant syngas coolers and two-stage sour shift reactors, followed by a dual-stage acid gas removal process for CO{sub 2} capture. The combined cycle island consists of two F-class gas turbines, steam turbine, and a heat recovery steam generator with three-pressure levels. The dynamic simulator can be used for normal base-load operation, as well as plant start-up and shut down. The real-time dynamic simulator also responds satisfactorily to process disturbances, feedstock blending and switchovers, fluctuations in ambient conditions, and power demand load shedding. In addition, the full-scope simulator handles a wide range of abnormal situations, including equipment malfunctions and failures, together with changes initiated through actions from plant field operators. By providing a comprehensive IGCC operator training system, the AVESTAR Center is poised to develop a workforce well-prepared to operate and control commercial-scale gasification-based power plants capable of 90% pre-combustion CO{sub 2} capture and compression, as well as low sulfur, mercury, and NOx emissions. With additional support from the NETL-Regional University Alliance (NETL-RUA), the Center will educate and train engineering students and researchers by providing hands-on 'learning by operating' experience The AVESTAR Center also offers unique collaborative R&D opportunities in high-fidelity dynamic modeling, advanced process control, real-time optimization, and virtual plant simulation. Objectives and goals are aimed at safe and effective management of power generation systems for optimal efficiency, while protecting the environment. To add another dimension of realism to the AVESTAR experience, NETL will introduce an immersive training system with innovative three-dimensional virtual reality technology. Wearing a stereoscopic headset or eyewear, trainees will enter an interactive virtual environment that will allow them to move freely throughout the simulated 3-D facility to study and learn various aspects of IGCC plant operation, control, and safety. Such combined operator and immersive training systems go beyond traditional simulation and include more realistic scenarios, improved communication, and collaboration among co-workers.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Development of high efficiency window air conditioner using propane under limited charge

The residential air conditioner market has a significant portion of window air conditioners (WAC). Due to the compact size and small refrigerant charge, WACs are most tolerant of flammable refrigerants. Hydrocarbon (HC) refrigerants are natural substances and have significantly better environmental impacts and lower costs than conventional refrigerants like R-410A and R-22. The paper aimed to develop a WAC prototype using propane (R-290) and verify the performance metrics in the laboratory. The key of developing the WAC using a flammable refrigerant is to control the refrigerant charge under the limit of building safety regulation with the performance meeting the Energy Star standard. To minimize the charge, compact heat exchangers were utilized. To achieve the target efficiency, we collaborated with a leading rotary compressor manufacturer to develop a compressor prototype specifically optimized for R-290, able to achieve superior energy efficiency. In addition, we conducted model-based investigations to identify further charge reduction potential. Finally, with using microchannel heat exchangers to replace the fin-and-tube condenser and evaporator, the system charge can be decreased to 150 g.

42 ENGINEERING↗
DOE OSTI2017

An Advanced One-Dimensional Finite Element Model for Incompressible Thermally Expandable Flow

Here, this paper provides an overview of a new one-dimensional finite element flow model for incompressible but thermally expandable flow. The flow model was developed for use in system analysis tools for whole-plant safety analysis of sodium fast reactors. Although the pressure-based formulation was implemented, the use of integral equations in the conservative form ensured the conservation laws of the fluid. A stabilization scheme based on streamline-upwind/Petrov-Galerkin and pressure-stabilizing/Petrov-Galerkin formulations is also introduced. The flow model and its implementation have been verified by many test problems, including density wave propagation, steep gradient problems, discharging between tanks, and the conjugate heat transfer in a heat exchanger.

42 ENGINEERING↗
DOE OSTI2018

Low-Cost Titanium Casting Technology

This is the final report of "Low-Cost Titanium Casting Technology" project. Titanium alloys have attracted increasing attentions as structural materials for high performance and weight-sensitive applications in aerospace and biomedical fields. However, in mass-production of civil applications like automotive components, titanium alloys are scarcely used due to the unaffordable costs in manufacturing compared with other metallic materials, such as aluminum, magnesium and steel. Therefore, there is a need to reduce the manufacturing cost of titanium alloys so that titanium alloys can be more competitive in terms of property and cost considerations. In this 4-year project, we started from exploring new low-cost titanium alloy systems with the guidance of Integrated Computational Materials Engineering – Calculation of Phase Diagram (ICME-CALPHAD) framework, and determined several promising alloy systems, alloying elements including Ti-Al-Fe-Mn-B-C, that can allow further development (lab-scale and industry-scale) in the future. During the new alloy system exploration, considerable amount of scientific work was completed regarding CALPHAD modeling, microstructure morphology, mechanical properties, and processing of the new alloy systems. The relevant ternary titanium alloy systems, Ti-Al-Mn and Ti-Al-Fe, were examined and reassessed both computationally and experimentally, leading to more comprehensive understanding of these alloy systems. Based on this above study, a new low-cost casting titanium alloy was developed, Ti-6Al-5Fe-0.05B-0.05C (designated as T65-0.05BC), which has competitive as-cast strength level with current commercial titanium alloys but relatively low ductility. However, in this project, since the target application, automotive engine connecting rod, is a fatigue-limited component and does not require high ductility, this alloy shall suffice as the deliverable alloy in this project. After designing and confirming the potent of the new alloy, we designed and built a lab-scale manufacturing equipment aided by computational tools at OSU labs to conceptually prove that this alloy can be manufactured with permanent mold casting, which is low-cost and potentially a mass production process. We produced a lab-scale prototype connecting rod demonstrating the capability of permanent mold casting of this new alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

Trigui, Ali [Qubit Engineering Inc.]↗
DOE OSTI2018

Product Portfolios within a Distributed Logistical System for Aviation Components

A structural model of a distributed logistical system based on fuzzy sets is proposed. This model permits the optimal organization of product portfolios. The model assumes a central organization that produces and distributes parts in the aviation industry; and peripheral organizations within the network of the central organization.

42 ENGINEERING↗
DOE OSTI2026

Feedback and Oscillations: Constructing Feedback Systems for Root Cause Analysis of Oscillations in Power Grids

Dynamic phenomena linked to inverter-based resources (IBRs) have gained global attention. Several IBR-induced dynamics have caused bulk power system-connected wind or solar power plants to trip, and some have even led to widespread outages. In addition, many oscillations have been observed involving IBR power plants. In 2023, the IEEE Power & Energy Society (PES) IBR Subsynchronous Oscillations (SSO) task force published a journal article, “Real-World Subsynchronous Oscillation Events in Power Grids With High Penetrations of Inverter-Based Resources,” in which 19 IBR oscillation events were examined for their causation. Earlier in 2020, another PES task force article, “Definition and Classification of Power System Stability-Revisited & Extended,” authored by prominent academics, introduced converter-driven stability as a new category of stability. The international power grid industry community also took action by publishing the CIGRE Green Book, Power System Dynamic Modelling and Analysis in Evolving Networks (led by Babak Badrzadeh and Zia Emin) in 2024. In August 2024, the Energy Systems Integration Group (ESIG) released a practical guide led by Nick Miller, “Diagnosis and Mitigation of Observed Oscillations in IBR-Dominant Power System: A Practical Guide.” The goal of the guide is to assist practicing engineers in making initial judgments and conducting detailed analyses about oscillations. Finally, when addressing the classification of stability and oscillations, the guide emphasizes a causality-based taxonomy for grouping, such as voltage control-induced oscillations, synchronization-induced oscillations, and frequency or active power control-induced oscillations.

Fan, Lingling [Univ. of South Florida, Tampa, FL (↗
DOE OSTI2023

Integrated membrane material design and system synthesis

In designing membrane systems, the synergy between membrane materials and the process design is often overlooked. In this paper, we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems while simultaneously designing the respective membrane materials for multicomponent gas separation. The approach considers superstructure representations for systems with: (1) same, (2) potentially different, and (3) property-targeting membrane materials. In the first two systems, the selection of membrane material is a decision, while in the final type, membrane permeances are subject to optimization. Physics-based surrogate models are used to describe permeation in crossflow and countercurrent flow permeators. We show that, through a case study of biogas upgrading, our approach obtains high quality solutions. Furthermore, we use the proposed approach while considering permeance-based production cost to find the optimal membrane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Altering DNA-Programmable Colloidal Crystallization Paths by Modulating Particle Repulsion

Colloidal crystal engineering with DNA can be used to realize precise control over nanoparticle (NP) arrangement. Here, we investigate a case of DNA-based assembly where the properties of DNA as a polyelectrolyte brush are employed to alter a hybridization-driven NP crystallization pathway. Using the co-assembly of DNA-conjugated proteins and spherical gold 2 nanoparticles (AuNPs) as a model system, we explore how steric repulsion between non-complementary, neighboring DNA-NPs due to overlapping DNA shells can influence their ligand-directed behavior. Specifically, our experimental data coupled with coarse-grained molecular dynamics (MD) simulations reveal that by changing factors related to NP repulsion, two structurally distinct outcomes can be achieved. When steric repulsion between DNA-AuNPs is significantly greater than that between DNA-proteins, a lower packing density crystal lattice is favored over the structure that is predicted by design rules based on DNA-hybridization considerations alone. This is enabled by the large difference in DNA density on AuNPs versus proteins and can be tuned by modulating the flexibility, and thus conformational entropy, of the DNA on the constituent particles. At intermediate ligand flexibility, the crystallization pathways are energetically similar and the structural outcome can be adjusted using the density of DNA duplexes on DNA-AuNPs and by screening the Coulomb potential between them. Such lattices are shown to undergo dynamic reorganization upon changing salt concentration. These data help elucidate the structural considerations necessary for understanding repulsive forces in DNA-assembly and lay the groundwork for using them to increase architectural diversity in engineering colloidal crystals.

colloidal crystals↗
DOE OSTI2023

Data Driven Approach to Dislocation-Based Plasticity Models of Face-Centered Cubic Metals

Dislocation dynamics controls plastic deformation, mechanical strength, and failure of crystalline materials. It also governs fatigue resistance under cyclic loading, creep resistance at elevated-temperature, and radiation resistance for reactor applications. There is a compelling need for understanding fundamental dislocation mechanisms for deformation because virtually all structural metals used in energy systems are fabricated to desired forms and shapes by deformation processes. To date, the most outstanding problem in a physics-based multiscale model of crystal plasticity is the lack of quantitative connections between continuum plasticity (CP) models with the lower scale dislocation models. As a result, existing CP models used in engineering applications are still phenomenological, while evidence continues to mount that they can make inaccurate predictions under realistically complex scenarios. This project takes advantage of the recent advances in high-performance discrete dislocation dynamics (DDD) simulations and data science approaches to establish the first fully connected multiscale plasticity model for pure face-centered cubic (FCC) single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway

Iron is a biochemically critical metal cofactor in enzymes involved in photosynthesis, cellular respiration, nitrate assimilation, nitrogen fixation, and reactive oxygen species defense. Marine microeukaryotes have evolved a phytotransferrin-based iron uptake system to cope with iron scarcity, a major factor limiting primary productivity in the global ocean. Diatom phytotransferrin is endocytosed; however, proteins downstream of this environmentally ubiquitous iron receptor are unknown. We applied engineered ascorbate peroxidase APEX2-based subcellular proteomics to catalog proximal proteins of phytotransferrin in the model marine diatom Phaeodactylum tricornutum . Proteins encoded by poorly characterized iron-sensitive genes were identified including three that are expressed from a chromosomal gene cluster. Two of them showed unambiguous colocalization with phytotransferrin adjacent to the chloroplast. Further phylogenetic, domain, and biochemical analyses suggest their involvement in intracellular iron processing. Proximity proteomics holds enormous potential to glean new insights into iron acquisition pathways and beyond in these evolutionarily, ecologically, and biotechnologically important microalgae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Particle Swarm Optimisation for group structure optimization for radiotherapy shielding

Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Energize: An Interactive Evaluation Tool for Engaging the General Public with Energy Decision Making - Final Report

The public has an ever-increasing interest in the economic, environmental, and social impacts of global energy production. To support informed decision making, the scientific community has a responsibility to communicate reliable and straight-forward information to the general public, in an engaging way, regarding energy systems and how choices made at different stages of an energy technology life cycle can impact the cost, amount of materials used, and waste produced. Our objective is to enhance public engagement via an interactive electrical energy game (Energ!ze) through which players interact with one another in their quest to develop an electrical energy portfolio that optimizes economic (e.g., company profit), environmental (e.g., reduced CO 2 emissions), and social (e.g., public opinion) impacts. The design and development of Energ!ze was an iterative process involving an interdisciplinary team of engineers, discipline-based scientists, computer scientists, and education experts. The development objectives were two-fold: (1) Accurate and quantifiable modeling of electrical energy systems and (2) Engaging and interactive user interface within the construct of the game narrative. The major game mechanics in the current playable Energ!ze version focus on economic metrics, and the database is scaffolded to add environmental and social metrics for future versions. This report details the outcome of the funding period (Section 2 and 3) and describes the development process, along with recommendations for future development (Section 4).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A cell-free system for production of 2,3-butanediol is robust to growth-toxic compounds

The need for sustainable, low-cost production of bioenergy and commodity chemicals is increasing. Unfortunately, the engineering potential of whole-cell catalysts to address this need can be hampered by cellular toxicity. When such bottlenecks limit the commercial feasibility of whole-cell fermentation, cell-free, or in vitro, based approaches may offer an alternative. Here, we assess the impact of three classes of growth toxic compounds on crude extract-based, cell-free chemical conversions. As a model system, we test a metabolic pathway for conversion of glucose to 2,3-butanediol (2,3-BDO) in lysates of Escherichia coli . First, we characterized 2,3-BDO production with different classes of antibiotics and found, as expected, that the system is uninhibited by compounds that prevent cell growth by means of cell wall replication and DNA, RNA, and protein synthesis. Second, we considered the impact of polar solvent addition (e.g., methanol, n-butanol). We observed that volumetric productivities (g/L/h) were slowed with increasing hydrophobicity of added alcohols. Finally, we investigated the effects of using pretreated biomass hydrolysate as a feed stock, observing a 25% reduction in 2,3-BDO production as a result of coumaroyl and feruloyl amides. Overall, we find the cell-free system to be robust to working concentrations of antibiotics and other compounds that are toxic to cell growth, but do not denature or inhibit relevant enzymes.

09 BIOMASS FUELS↗