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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chapter 16: Connecting Microbial Genotype with Phenotype in the Omics Era

Although rational design-based metabolic engineering has been applied widely to obtain promising microbial biocatalysts, conventional strategies such as adaptive laboratory evolution (ALE) and mutagenesis are still efficient approaches to improve microorganisms for exceptional features such as a broad spectrum of substrate utilization, robustness of cell growth, as well as high titer, yield, and productivity of the target products. In this chapter, we describe the procedure to generate mutant strains with desired phenotypes using ALE and a new mutagenesis approach of Atmosphere and Room Temperature Plasma (ARTP). In addition, we discuss the methodology to combine next-generation sequencing (NGS)-based genome-resequencing and RNA-Seq transcriptomics approaches to characterize the mutant strains and connect the phenotypes with their corresponding genotypic changes.

adaptive laboratory evolution (ALE)↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Large intrinsic anomalous Hall effect in SrIrO 3 induced by magnetic proximity effect

The anomalous Hall effect (AHE) is an intriguing transport phenomenon occurring typically in ferromagnets as a consequence of broken time reversal symmetry and spin-orbit interaction. It can be caused by two microscopically distinct mechanisms, namely, by skew or side-jump scattering due to chiral features of the disorder scattering, or by an intrinsic contribution directly linked to the topological properties of the Bloch states. Here we show that the AHE can be artificially engineered in materials in which it is originally absent by combining the effects of symmetry breaking, spin orbit interaction and proximity-induced magnetism. In particular, we find a strikingly large AHE that emerges at the interface between a ferromagnetic manganite (La 0.7 Sr 0.3 MnO 3 ) and a semimetallic iridate (SrIrO 3 ). It is intrinsic and originates in the proximity-induced magnetism present in the narrow bands of strong spin-orbit coupling material SrIrO 3 , which yields values of anomalous Hall conductivity and Hall angle as high as those observed in bulk transition-metal ferromagnets. These results demonstrate the interplay between correlated electron physics and topological phenomena at interfaces between 3d ferromagnets and strong spin-orbit coupling 5d oxides and trace an exciting path towards future topological spintronics at oxide interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defect tolerance in halide perovskites: A first-principles perspective

In recent years, the impressive photovoltaic performance of halide perovskites has been commonly attributed to their defect tolerance. This attribution is seemingly intuitive and has been widely promoted in the field, though it has not been rigorously assessed. In this Perspective, we critically discuss the proposition of defect tolerance in halide perovskites based on first-principles calculations. We show that halide perovskites actually do suffer from defect-assisted nonradiative recombination, i.e., they are not defect tolerant. The nonradiative recombination rates in halide perovskites are comparable to or even greater than those in more conventional semiconductors. We note that to obtain accurate defect properties in halide perovskites, the level of theory and computational details are highly important, which was previously not sufficiently recognized. A distinctive feature of halide perovskites is that they can be grown with moderate defect densities using low-cost deposition techniques. But, similar to the case of conventional semiconductors, defect engineering is still key to improving the efficiency of perovskite solar cells.

14 SOLAR ENERGY↗
DOE OSTI2021

Microbial community composition controls carbon flux across litter types in early phase of litter decomposition

Leaf litter decomposition is a major carbon input to soil, making it a target for increasing soil carbon storage through microbiome engineering. We expand upon previous findings to show with multiple leaf litter types that microbial composition can drive variation in carbon flow from litter decomposition and specific microbial community features are associated with synonymous patterns of carbon flow among litter types. Although plant litter type selects for different decomposer communities, within a litter type, microbial composition drives variation in the quantity of dissolved organic carbon (DOC) measured at the end of the decomposition period. Bacterial richness was negatively correlated with DOC quantity, supporting our hypothesis that across multiple litter types there are common microbial traits linked to carbon flow patterns. Variation in DOC abundance (i.e. high versus low DOC) driven by microbial composition is tentatively due to differences in bacterial metabolism of labile compounds, rather than catabolism of non-labile substrates such as lignin. The temporal asynchrony of metabolic processes across litter types may be a substantial impediment to discovering more microbial features common to synonymous patterns of carbon flow among litters. Overall, our findings support the concept that carbon flow may be programmed by manipulating microbial community composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Energy Storage Sizing, Placement and Valuation for Distribution Networks

SAND2023-05682O The Energy Storage Sizing, Placement, and Valuation for Distribution software allows a user to simulate sizing, placement, and operation of one or more storage assets on a distribution grid. Among its features are measuring differences to determine the best combinations, and options for configuring and comparing results. This new simulation capability leverages the Open Distribution System Simulator and the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tamrakar, Ujjwol↗
DOE OSTI2024

AI Design Assistant

SAND2025-01930O The AI Design Assistant uses ChatGPT to provide a natural language interface to airfoil analysis tools (XFOIL). Most of the code base is glue code, connecting XFOIL (a tool for analyzing airfoils) to the OpenAI interface. Among the more novel features are an airfoil geometry class and methods on how to extract detailed data from XFOIL. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Karcher, Cody↗
DOE OSTI2025

On-chip terahertz emission from Floquet-Bloch states [Invited]

Floquet engineering uses time-periodic electromagnetic fields to modify the electronic properties of quantum materials via the creation of Floquet-Bloch states. These photon-dressed states inherit features from both the material and the driving field, enabling the exploration and control of quantum phenomena in light-matter hybrid systems. In non-centrosymmetric materials, shift currents can arise from the quantum geometric properties of electronic wavefunctions. However, shift currents from Floquet-Bloch states remain experimentally unexplored. Here, we employ an on-chip optoelectronic circuit to detect intrinsic terahertz emission from Floquet-Bloch states in T d -WTe 2 under intense optical driving. We observe strong edge-localized terahertz emission that scales linearly with the driving field, consistent with the theoretical prediction for shift currents generated by Floquet-Bloch states. The results advance our understanding of strongly driven quantum materials and provide insights for developing efficient, bias-free terahertz sources for future optoelectronic technologies.

Li, Xinyu [Max Planck Institute for the Structure ↗
DOE OSTI2021

A multiresolution adaptive wavelet method for nonlinear partial differential equations

We report the multiscale complexity of modern problems in computational science and engineering can prohibit the use of traditional numerical methods in multi-dimensional simulations. Therefore, novel algorithms are required in these situations to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. To meet these challenges, we present a multiresolution wavelet algorithm to solve PDEs with significant data compression and explicit error control. We discretize in space by projecting fields and spatial derivative operators onto wavelet basis functions. We provide error estimates for the wavelet representation of fields and their derivatives. Then, our estimates are used to construct a sparse multiresolution discretization which guarantees the prescribed accuracy. Additionally, we embed a predictor-corrector procedure within the temporal integration to dynamically adapt the computational grid and maintain the accuracy of the solution of the PDE as it evolves. We present examples to highlight the accuracy and adaptivity of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Power of Priors: Improved Enrichment Safeguards

International safeguards currently rely on material accountancy to verify that declared nuclear material is present and unmodified. Although effective, material accountancy for large bulk facilities can be expensive to implement due to the high precision instrumentation required to meet regulatory targets. Process monitoring has long been considered to improve material accountancy. However, effective integration of process monitoring has been met with mixed results. Given the large successes in other domains, machine learning may present a solution for process monitoring integration. Past work has shown that unsupervised approaches struggle due to measurement error. Although not studied in depth for a safeguards context, supervised approaches often have poor generalization for unseen classes of data (e.g., unseen material loss patterns). This work shows that engineered datasets, when used for training, can improve the generalization of supervised approaches. Further, the underlying models needed to generate these datasets need only accurately model certain high importance features.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

High Precision, High Frequency Printed Antennas

An emerging trend in advanced manufacturing is printed electronics and sensors. The ability to print customized electronics and sensors integrated into functional packages is a growing need within a variety of growing markets such as smart manufacturing, internet of things (IoT), and the small satellite industry. Both Oak Ridge National Laboratory (ORNL) and the MITRE Corporation have seedling research efforts evaluating the potential for future printed electronic systems. High frequency, wide-bandwidth phased array antennas (i.e. >45 GHz) open the door to new applications. However, such sensors require currently prohibitively small feature sizes for commercial 3D printing technologies along with increasing challenges with connecting the driving electronics to such features. An additional finding with related advanced manufacturing challenges is the rapid production of 3D additive connectors for integration with commercial printed circuit boards (PCBs), primarily for advanced in-circuit inspection techniques. This work is developing additive manufacturing processes for producing connected and conductive fine scale 3D features. The primary focus was on aerosol-jet printing (AJP), which has a small minimum resolution (<50 µm) but is traditionally printed flat with small height/width aspect ratios <<1, and developing controls to enable fully 3D, high aspect ratio, and unsupported features. In Phase 1 of this effort, baselines of process performance were characterized, and test coupons produced for both ultra-high frequency antennas and microstructures to support reverse engineering of PCBs. In Phase 2, these efforts will be extended for system demonstration of ultra-high frequency antenna arrays, as well as reverse engineering circuitry for dense PCBs.

42 ENGINEERING↗
DOE OSTI2026

Teleseismic Network Association with GENIE

In this report we investigate adapting the Graph Neural Interpretation Engine (GENIE), an associator developed for three-component dense monitoring networks, to regional to teleseismic association using a sparse network of array stations. We expand GENIE’s input features to include first-P detection time, azimuth, and slowness estimates. Additionally, we include a probability of detection (PDET) term which measures a station’s likelihood of detecting an event. To assess each feature’s relative importance, we train four models, each using an increasing set of node features and find that the PDET models perform the best. We define two measures of event complexity which demonstrate that all GENIE model versions perform better than the standard backprojection stack.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

2022 Prototype Installation and Testing Awardee: Windward Engineering

The 60-kilowatt (kW) Zephyr 21/60 wind turbine from Windward Engineering uses a low-cost, full-span independent pitch system that can act as a redundant aerodynamic braking system. This design uses a relatively simple pneumatic rotary vane actuator, allowing each of the turbine's three blades to be pitched independently and any single blade to activate emergency braking for the entire turbine. A previous Competitiveness Improvement Project (CIP) funding award enabled Windward Engineering to design and develop the Zephyr 21/60 wind turbine's aeroelastic model and pitch system. At the end of this current Competitiveness Improvement Project award, a fully validated aeroelastic model will make it possible to certify Windward Engineering's Zephyr 21/60 design and quantify the technology's levelized cost of energy in preparation for certification and entry into the distributed wind market. The Zephyr 21/60 will feature an attractive levelized cost of energy, improved reliability, and a full-span pitch system for safe and redundant protection against rotor overspeed (which occurs when the rotor turns beyond its design limit).

CIP↗
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
DOE OSTI2021

Simulations of Multi-Mode Combustion Regimes Realizable In a Gasoline Direct Injection Engine

Lean and dilute gasoline compression ignition (GCI) operation in spark ignition (SI) engines are an attractive strategy to attain high fuel efficiency and low NOx levels. However, this combustion mode is often limited to low-load engine conditions due to the challenges associated with autoignition controllability. In order to overcome this constrain, multi-mode (MM) operating strategies, consisting of advanced compression ignition (ACI) at low load and conventional SI at high load, have been proposed. In this three-dimensional computational fluid dynamics study, the concept of multi-mode combustion using two RON98 gasoline fuel blends (Co-Optima Alkylate and E30) in a gasoline direct injection (GDI) engine were explored. To this end, a new reduced mechanism for simulating the kinetics of E30 fuel blend is introduced in this study. To cover the varying engine load demands for multi-mode engines, primary combustion dynamics observed in ACI and SI combustion modes was characterized and validated against experimental measurements. In order to implement part-load conditions, a strategy of mode transition between SI and ACI combustion (i.e., mixed-mode combustion) was then explored numerically by creating a virtual test condition. The results obtained from the mixed-mode simulations highlight an important feature that deflagrative flame propagation regime coexists with ignition-assisted end-gas autoignition. This study also identifies a role of turbulent flow property adjacent to premixed flame front in characterizing the mixed-mode combustion. The employed hybrid combustion model was verified to perform simulations aiming at suitable range of multi-mode engine operations.

Kim, Sayop↗
DOE OSTI2025

Heterostructure engineering for wurtzite LaN

Wurtzite LaN (wz-LaN) is a semiconducting nitride with favorable piezoelectric and ferroelectric properties, making it promising for applications in electronics. Here, we use first-principles density functional theory with a hybrid functional to investigate several features that are key for its use in heterostructures. First, for the purposes of growing wz-LaN on a substrate or designing a heterostructure, we show that it can be lattice-matched with a number of cubic materials along their [111] axes. We also evaluate the bound charge at such interfaces, taking into account both the polarization discontinuity and the piezoelectric polarization due to pseudomorphic strain. Second, we investigate band alignments and assess the results for interfaces with zincblende-, rocksalt-, and perovskite-structure compounds, along with chemically similar wurtzite and rocksalt nitrides. Our results provide guidance for the development of electronic devices based on wz-LaN.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Anisotropic moiré optical transitions in twisted monolayer/bilayer phosphorene heterostructures

Moiré superlattices of van der Waals heterostructures provide a powerful way to engineer electronic structures of two-dimensional materials. Many novel quantum phenomena have emerged in graphene and transition metal dichalcogenide moiré systems. Twisted phosphorene offers another attractive system to explore moiré physics because phosphorene features an anisotropic rectangular lattice, different from isotropic hexagonal lattices previously reported. Here we report emerging anisotropic moiré optical transitions in twisted monolayer/bilayer phosphorenes. The optical resonances in phosphorene moiré superlattice depend sensitively on twist angle and are completely different from those in the constitute monolayer and bilayer phosphorene even for a twist angle as large as 19°. Our calculations reveal that the Γ-point direct bandgap and the rectangular lattice of phosphorene give rise to the remarkably strong moiré physics in large-twist-angle phosphorene heterostructures. This work highlights fresh opportunities to explore moiré physics in phosphorene and other van der Waals heterostructures with different lattice configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗