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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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178 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

RNAi-Mediated Manipulation of Cuticle Coloration Genes in Lygus hesperus Knight (Hemiptera: Miridae)

Cuticle coloration in insects is a consequence of the accumulation of pigments in a species-specific pattern. Numerous genes are involved in regulating the underlying processes of melanization and sclerotization, and their manipulation can be used to create externally visible markers of successful gene editing. To clarify the roles for many of these genes and examine their suitability as phenotypic markers in Lygus hesperus Knight (western tarnished plant bug), transcriptomic data were screened for sequences exhibiting homology with the Drosophila melanogaster proteins. Complete open reading frames encoding putative homologs for six genes (aaNAT, black, ebony, pale, tan, and yellow) were identified, with two variants for black. Sequence and phylogenetic analyses supported preliminary annotations as cuticle pigmentation genes. In accord with observable difference in color patterning, expression varied for each gene by developmental stage, adult age, body part, and sex. Knockdown by injection of dsRNA for each gene produced varied effects in adults, ranging from the non-detectable (black 1, yellow), to moderate decreases (pale, tan) and increases (black 2, ebony) in darkness, to extreme melanization (aaNAT). Based solely on its expression profile and highly visible phenotype, aaNAT appears to be the best marker for tracking transgenic L. hesperus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NDMAS System and Process Description

The U. S. Department of Energy (DOE) has made a significant investment in research to develop the next generation of reactor technologies as well as to improve the performance and lengthen the life cycle of existing nuclear reactors. Data collected to demonstrate new concepts may also be used in the future to support licensing of these technologies. Provenance of these data must be preserved. The Nuclear Data Management and Analysis System (NDMAS) was established to manage and preserve data collected by fuels and materials research conducted by the high-temperature, gas-cooled reactor program. The scope of NDMAS is expanding to include other nuclear research programs that have the shared need to preserve the provenance of research data. Nuclear research funded by DOE is conducted by Idaho National Laboratory (INL), universities, other national laboratories, foreign research partners, and private companies. This research will generate a large amount of data from a variety of sources over a period of many years. Managing the data generated by the research and development projects presents a significant challenge for retaining data integrity and availability.

+↗
DOE OSTI2020

Superconducting air core inductor systems and methods

Provided is a low-weight, high-efficiency inductor design for use with or in electrical power equipment, such as inverters. A toroidal power inductor includes a support structure comprising an outer shell, an inner shell, and one or more coolant channels formed therebetween, a plurality of conductors wrapped around and supported by an exterior surface of the outer shell, and an interior cavity substantially enclosed by the inner shell of the toroidal support structure. The plurality of conductors are configured to provide an inductance for the toroidal power inductor, and the one or more coolant channels are distributed beneath the exterior surface of the outer shell to cool the plurality of conductors. An air-core power inductor may implement the conductors using high-temperature superconducting (HTS) tapes cooled by cryogenic fluid flowing within the coolant channels.

42 ENGINEERING↗
DOE OSTI2020

Learnings from the Marcellus Shale Energy and Environmental Lab (MSEEL) Using Fiber Optic Tools and Geomechanical Modeling

The study focuses on the MIP and Boggess pads of the MSEEL (Marcellus Shale Energy and Environmental Laboratory), a public-private partnership with a mandate to publicly release data for scientists and engineers to engage with. Multiple diagnostic tools are used to characterize the formation and monitor fracture treatment and propagation. Geomechanical modeling is used to understand the insitu stresses, microseismic to describe half-lengths and heights, and fiber optics to characterize offset well. Fracture Driven Interactions (FDI’s) and interstage communication. Recent publications covering the MSEEL MIP-3H and MIP-5H wells are reviewed and discussed. A preview of the findings from the Boggess pad (first production in November 2019) is also shared here

03 NATURAL GAS↗
DOE OSTI2023

Nutrient Recovery from Algae Residues Using Mild Oxidative Treatment

Algal biorefining to fuels and chemicals frequently requires both fuel production and nutrient recycle pathways. Mild Oxidative Treatment (MOT) is a promising wet oxidation route that simultaneously converts nitrogen and carbon in algae hydrolysate and extracted residues into ammonia and carboxylic acids (up to C5), respectively. The latter can be converted to sustainable aviation fuel by ketonization, condensation, and hydrodeoxygenation. We show that for a Scenedesmus algae under certain oxidation conditions, the nitrogen in the residues can be converted quantitatively to ammonia and recovered by cation exchange, while almost half of the carbon can be converted to carboxylic acids, mainly acetic acid. At the same time, we also show that soluble phosphorus in the form of phosphate can be selectively recovered by anion exchange, leaving a clean aqueous carboxylic acid stream for upgrading to fuels.

algae↗
DOE OSTI2023

Lipids from High-Protein Algae Biomass

Biofuels from renewable and sustainable sources will be a critical component of reducing greenhouse gasses. To this end, in achieving maximum biomass productivity in outdoor ponds at a reasonable cost, algae must be grown at a maximal rate. This current algae cultivation strategy results in high-protein biomass because there is insufficient time for deplete-hold steps that increase lipids but also cost. Thus methods to convert high-protein (>50%) need to be developed for biofuels. The Consolidated Algae Processing (CAP) strategy has previously shown utility on high-carbohydrate algae biomass and has now been expanded to include methods aimed at converting the protein fraction to lipids. After acid-pretreatment, hydrolysate liquor is oxidatively treated to deaminate and convert soluble proteins and amino acids into four main carboxylate acids; formic, acetic, succinic, and propionic. These acids are readily and completely fermented into intracellular lipids using oleaginous yeast. Using a fed-batch fermentation strategy and no added nutrients, we achieved nearly 30% intracellular lipids and 1.3 g/L lipids demonstrating the potential of our expanded CAP process to generate additional lipids for conversion to biofuels as part of an algae-focused biorefinery.

AAD↗
DOE OSTI2023

Study of Microphonic Effects on the C100 Cryomodule for High Energy Electron Beam Accelerators

The Continuous Electron Beam Accelerator Facility (CEBAF) at Thomas Jefferson National Laboratory (JLab) is a particle accelerator which can accelerate an electron beam to relativistic speeds and apply the beam onto target samples. The C100 superconducting radio frequency (SRF) cavity is the primary accelerating structure of the C100 cryomodule, one of the many cryomodules which compose the CEBAF linear accelerator. SRF cavities are particularly sensitive to internal and external vibrations that can result in a phenomenon called microphonics which degrade the operational stability of a cryomodule. The purpose of this thesis is to investigate the significance of mechanical disturbances on the electromagnetic resonant frequency of a C100 SRF cavity. Knowledge of the mechanical resonance of the cavities and cryomodule sheds light into how these disturbances are most easily realized as deformation which causes radio frequency (RF) detuning. Three studies were conducted: the development and hammer test calibration of a Finite Element Analysis (FEA) model of a C100 cavity, the development and hammer test calibration of an FEA model of a C100 cavity string, and the hammer test of the C100-10R cryomodule at the Cryomodule Test Facility (CMTF). The cavity FEA model was found to accurately predict two modes found in two real cavities in a simply supported configuration. The cavity string FEA model leveraged the calibrated cavity FEA model but was not found to accurately predict the modal behavior of a real cavity string. Even so, the modal behavior of the cavity string inside the C100-10R cryomodule was captured during a hammer test while it was partially assembled. Finally, the C100-10R cryomodule was placed in the CMTF to study RF detuning. The RF detuning spectra during hammer hits and background noise was captured. The results of the hammer testing indicate two strong peaks at low frequencies (9-10 Hz and 22-23 Hz). These two frequencies were found to be nearly coincident to four instances of mechanical resonance found during the hammer testing done on the partially-assembled C100-10R. Because of this, these two modes are believed to contribute to RF detuning of the cryomodule. This test event also included the testing of the effectiveness of a configuration of BNNT canisters designed to act as dampers. While these tests show promising results, the lurking variables render these tests somewhat inconclusive.

Hull, Caleb James↗
DOE OSTI2024

Nuclear Data Management and Analysis System Plan

The United States Department of Energy Advanced Reactor Technologies Program was formed in Fiscal Year 2015 and encompasses the Next Generation Nuclear Plant Project and Very High Temperature Reactor (VHTR) Program as they were known previously. The VHTR Program was created to support design and licensing of the first VHTR nuclear plant. Data created for and used by the program must be qualified for use, stored in a readily accessible electronic form, categorized to assure the correct data are used, and controlled to prevent data corruption or inadvertent changes. The Nuclear Data Management and Analysis System was designed to support the data needs of the VHTR Program, at the time and now the Advanced Reactor Technologies Program. Since its inception, use of the Nuclear Data Management and Analysis System has expanded to support additional projects and programs with similar requirements for control, analysis, and availability of large data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Developing Microalgae-Based Non-Isocyanate Polyurethane Foam Materials

Polyurethane is a versatile material with a wide range of applications in the production of foam products such as car seats, mattresses, footwear, insulation foam, and more. The traditional process for producing polyurethane foam involves the fast reaction of isocyanate with water, which generates CO2 as a blowing agent. However, the use of isocyanate has raised concerns due to its potential for causing allergic reactions and aspiration symptoms. This has led to increased regulation of isocyanate, leading to a growing interest in developing alternatives. Non-isocyanate polyurethane (NIPU) has emerged as a promising alternative to traditional toxic PU production. The most promising approach for producing NIPU is the aminolysis of cyclic carbonate with diamine. The NIPU produced through this process has a polyhydroxyurethane structure, which is similar to traditional PU chemistry. We have demonstrated that microalgal oil is a favored feedstock, as it can provide high reactivity for industrial applications. However, the NIPU production process does not generate gas, and a blowing agent is required to achieve the desired foam structure. One of the challenges in producing NIPU foam is to match the bubble formation and curing rates. We have developed a novel approach to producing NIPU foam that utilizes carbamate as a bifunctional blowing and crosslinking agent. We have used vegetable oil and microalgal oil as feedstocks to produce various NIPU for characterization. The development of microalgal NIPU provides an exciting opportunity for reducing the environmental impact of foam production and improving the safety of the materials used in the production process. The production of value-added NIPU foam can also drive down the cost of microalgal biorefinery, advancing the commercialization of microalgal biofuel.

algae↗