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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

Patnaik, Surya N.↗
DOE OSTI2016

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome. In addition, the fact that coordinated regulation of cyanobacterial cellular machinery takes place with significantly fewer transcription factors, compared to other Eubacteria, suggests the involvement of post-transcriptional mechanisms and regulatory adaptations which are not fully understood. Global transcript abundance from model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using context-likelihood of relatedness. The resulting 903-gene network, which was organized into 11 modules, not only allowed classification of cyanobacterial responses to specific environmental variables but provided insight into the transcriptional network topology and led to the expansion of predicted regulons. When used in conjunction with genome sequence, the global transcript abundance allowed identification of putative post-transcriptional changes in expression as well as novel potential targets of both DNA binding proteins and asRNA regulators. The results offer a new perspective into the multi-level regulation that governs cellular adaptations of fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also extends a methodological knowledge-based framework for studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance evidence-driven genomic annotation, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis

The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.

42 ENGINEERING↗
DOE OSTI2023

Reactor Network Analysis with Various Reaction Mechanisms to Investigate Hydrogen vs. Methane Fuel at Varying Flame Temperatures with Experimental Data

Emissions data were evaluated for a set of test hardware that adapts Collins Aerospace’s aeroengine liquid fueled injector technology to a ground-based turbine. The hardware was specifically developed to operate on 100% hydrogen; however it has the ability to operate on both pure methane and hydrogen and mixtures in between. 16 injector configurations and seven factors (air split, fuel and air swirl, pressure drop, preheat temperature, fuel composition, and flame temperature) were investigated based on a Box Behnken statistical model. Of the 16, configuration 3 was selected for further discussion being in the middle of the statistical design space. A chemical reactor network was developed based on the experimental data obtained and was assessed with three different mechanisms: GRI Mech 3.0, UCSD, and Galway. All factors were held constant except the fuel composition to directly compare chemical pathways for methane and hydrogen. This study was conducted across different flame temperatures of 1500K, 1675K, and 1850K for both hydrogen and methane. Additional lower flame temperatures of 1100K and 1300K were evaluated for hydrogen. Moreover, results for fully premixed and non-premixed results were compared to obtain information on the implications of mixedness on emissions. These results were compared with the Leonard and Stegmaier plot for methane and a similar plot was constructed for hydrogen.

08 HYDROGEN↗
DOE OSTI2025

Improved heavy-ion PID using scintillation light detector with neural network analysis: a Monte Carlo simulation study

The photon collection efficiency of gaseous scintillator detectors varies according to the position of the impinging charged particles in the medium that generates scintillation light. Thus, when impinging particles are distributed over a large area, the intrinsic photon-number resolution of the system is affected by a large variation. This work presents and discusses a method for adjusting the total number of detected photons to account for variation in the photon collection efficiency as a function of the position of the light source within the scintillating medium. The method was developed and validated by processing data from systematic simulation studies based on GEANT4 that model the response of the Energy Loss Optical Scintillation System (ELOSS) detector. The position of the charged particle is calculated using a deep neural network algorithm. This is accomplished by analyzing the distribution of scintillation light recorded by the array of photosensors. The estimated particle position is then used to calculate the correction factor and adjust the amount of captured light to account for variations in the photon collection efficiency. The neural network algorithm provides excellent tracking capabilities, achieving sub-millimeter position resolution and an angular resolution of 12 mrad, approaching the performance of traditional tracking detectors (e.g., drift chambers). The present method can be generalized to any optical scintillation system where the photon collection efficiency depends on the position of the impinging particle.

Heavy-ion detectors↗
DOE OSTI2024

Discovering Innovations in Stress Tolerance through Comparative Gene Regulatory Network Analysis and Cell-Type Specific Expression Maps (Final Technical Report with Cover Page)

Through this grant, we developed a comparative framework to elucidate the mechanisms behind variations in environmental stress responses among a diverse group of species within the Brassicaceae family. Our focus was on the differences in physiological and transcriptomic responses to abscisic acid (ABA), a hormone associated with water stress. We examined the differential growth responses of four Brassicaceae species, finding that most exhibited reduced root growth correlated with smaller meristem size. In contrast, Schrenkiella parvula showed accelerated growth due to increased root cell elongation. We employed RNA sequencing to analyze the transcriptional responses to ABA across these species, and innovative bioinformatics techniques were used to pinpoint biological pathways with significant divergence. Additionally, we utilized DAP-seq to map the gene regulatory networks associated with ABAresponsive transcription factors, revealing that variations in the regulation of growth hormone biosynthesis play a critical role in the distinct ABA effects on root growth among the species. This research sets a new standard for comparative physiology by integrating comparative genomics and transcriptomics to uncover pathway divergences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike’s stability. Taken together, the network approach serves as a valuable tool to evaluate communication “hot-spots” in proteins to guide design of stable immunogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bioenergy sorghum nodal root bud development: morphometric, transcriptomic and gene regulatory network analysis

Bioenergy sorghum’s large and deep nodal root system and associated microbiome enables uptake of water and nutrients from and deposition of soil organic carbon into soil profiles, key contributors to the crop’s resilience and sustainability. The goal of this study was to increase our understanding of bioenergy sorghum nodal root bud development. Sorghum nodal root bud initiation was first observed on the stem node of the 7 th phytomer below the shoot apex. Buds were initiated near the upper end of the stem node pulvinus on the side of the stem opposite the tiller bud, then additional buds were added over the next 6-8 days forming a ring of 10-15 nascent nodal root buds around the stem. Later in plant development, a second ring of nodal root buds began forming on the 17 th stem node immediately above the first ring of buds. Overall, nodal root bud development can take ~40 days from initiation to onset of nodal root outgrowth. Nodal root buds were initiated in close association with vascular bundles in the rind of the pulvinus. Stem tissue forming nascent nodal root buds expressed sorghum homologs of genes associated with root initiation (WOX4), auxin transport (LAX2, PIN4), meristem activation (NGAL2), and genes involved in cell proliferation. Expression of WOX11 and WOX5, genes involved in root stem niche formation, increased early in nodal root bud development followed by genes encoding PLTs, LBDs (LBD29), LRP1, SMB, RGF1 and root cap LEAs later in development. A nodal root bud gene regulatory network module expressed during nodal root bud initiation predicted connections linking PFA5, SPL9 and WOX4 to genes involved in hormone signaling, meristem activation, and cell proliferation. A network module expressed later in development predicted connections among SOMBRERO, a gene involved in root cap formation, and GATA19, BBM, LBD29 and RITF1/RGF1 signaling. Overall, this study provides a detailed description of bioenergy sorghum nodal root bud development and transcriptome information useful for understanding the regulation of sorghum nodal root bud formation and development.

09 BIOMASS FUELS↗
DOE OSTI2025

Stage-resolved gene regulatory network analysis reveals developmental reprogramming and genes with robust stem-preferred expression in sorghum

Sorghum bicolor is a deep-rooted, heat- and drought-tolerant crop that thrives on marginal lands and is increasingly valued for its applications in biofuel, bioenergy, and biopolymer production. The sorghum stem, which can reach 4–5 m in length, serves as the primary reservoir of both lignocellulosic biomass and soluble sugars, making it a promising bioenergy feedstock. Although recent advances in genetic, genomic, and transcriptomic resources have improved our understanding of sorghum biology, comprehensive genome-wide analyses of functional dynamics across diverse organ types and developmental stages remain limited. In particular, candidate genes with stem preferred expression pattern or their associated cis-regulatory elements, which may program key stem-related functions and enable organ- or tissue-specific engineering, have not yet been identified.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions

In this work, we extract two nonsinglet nucleon Parton Distribution Functions from lattice QCD data for reduced Ioffe-time pseudodistributions. We perform such analysis within the NNPDF framework, considering data coming from different lattice ensembles and dis- cussing in detail the treatment of the different source of systematics involved in the fit. We introduce a recipe for taking care of systematics and use it to perform our extraction of light-cone PDFs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Relative Hausdorff distance for network analysis

Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs. In this work we study the effectiveness of RH distance as a tool for detecting anomalies in time-evolving graph sequences. We apply RH to cyber data with given red team events, as well to synthetically generated sequences of graphs with planted attacks. In our experiments, the performance of RH distance is at times comparable, and sometimes superior, to graph edit distance in detecting anomalous phenomena. Furthermore, our results suggest that in appropriate contexts, RH distance has advantages over more computationally intensive similarity measures.

97 MATHEMATICS AND COMPUTING↗