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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 181 records · Page 10

Nearfield Summary and Analysis of the Third AIAA Sonic Boom Prediction Workshop C608 Low Boom Demonstrator

A summary and statistical analysis of the nearfield Computational Fluid Dynamics (CFD) submissions for the Third AIAA Sonic Boom Prediction Workshop is provided with a focus on the C608 Low Boom Flight Test Demonstrator. The C608 is more complex in terms of geometry and propulsion boundary conditions than previous workshops cases and is more representative of vehicles with lower ground loudness and the potential for lower annoyance. The nearfield signatures submitted by the participants are propagated to the ground to compute statistics of loudness measures over the vehicle sonic boom carpet. Principle component analysis is used to extract the primary variation modes. Context from previous sonic boom workshops indicates that this workshop has the lowest variation even though the case is more challenging because it is quieter and more complex. The international state-of-the-art results documented in this summary indicates that nearfield CFD variation is low enough for meaningful low-boom design and can contribute toward the discussion of replacing the prohibition of overland supersonic flight with a certification standard.

sonic boom↗

Nearfield Summary and Analysis of the Third AIAA Sonic Boom Prediction Workshop C608 Low Boom Demonstrator

A summary and statistical analysis of the nearfield Computational Fluid Dynamics (CFD)submissions for the Third AIAA Sonic Boom Prediction Workshop is provided with a focus onthe C608 Low Boom Flight Test Demonstrator. The C608 is more complex in terms of geometryand propulsion boundary conditions than previous workshop cases and is more representativeof vehicles with lower ground loudness and the potential for lower annoyance. The nearfieldsignatures submitted by the participants are propagated to the ground to compute statisticsof loudness measures over the vehicle sonic boom carpet. Principle component analysis isused to extract the primary variation modes. Context from previous sonic boom workshopsindicates that this workshop has the lowest variation even though the case is more challengingbecause it is quieter and more complex. The results documented in this summary indicate thatthe international state-of-the-art for nearfield CFD has a variation that is small enough formeaningful low-boom design and analysis. Low-boom configuration analysis methods with lowvariation are important tools to develop certification processes for addressing the prohibitionon overland supersonic commercial flight.

sonic boom CFD workshop c608↗

Types of Information Expected from a Photometric Search for Extra-Solar Planets

The current theory postulates that planets are a consequence of the formation of stars from viscous accretion disks. Condensation from the hotter, inner portion of the accretion disk favors the formation of small rocky planets in the inner portion and the formation of gas giants in the cuter, cooler part. Consequently, terrestrial-type planets in inner orbits must be commonplace (Wetheril 1991). From the geometry of the situation (Borucki and Summers 1984), it can be shown that 1% of those planetary systems that resemble our solar system should show transits for Earth-sized (or larger) planets. Thus a photometric satellite that uses a wide field of view telescope and a large CCD array to simultaneously monitor 5000 target stars should detect 50 planetary systems. To verify that regularly recurring transits are occurring rather than statistical fluctuations of the stellar flux, demands observations that extend over several orbital periods so that the constancy of the orbital period, signal amplitude, and duration can be measured. Therefore, to examine the region from Mercury's orbit to that of the Earth requires a duration of three years whereas a search out to the orbit of mars requires about six years. The results of the observations should provide estimates of the distributions of planetary size and orbital radius, and the frequency of planetary systems that have Earth-sized planets in inner orbits. Because approximately one half of the star systems observed will be binary systems, the frequency of planetary systems orbit ' ing either one or both of the stars can also be determined. Furthermore, the complexity of the photometric signature of a planet transiting a pair of stars provides enough information to estimate the eccentricities of the planetary orbits. In summary, the statistical evidence from a photometric search of solar-like stars should be able to either confirm or deny the applicability of the current theory of planet formation and provide new information about the stability of planetary orbits in binary star systems.

Borucki, William↗

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗

Reliability Estimation for One-Shot Devices (Rev. 1)

We present an engineering-oriented summary of statistical methods for estimation of the reliability (or equivalently, failure probability) of one-shot devices such as explosive detonators and other weapon components. Estimates may be given as single points or intervals, based on pass/fail tests, margin analysis, computational models, expert judgment, or a combination of these. We focus on highly reliable devices for which few or no failures are expected to occur in testing.

42 ENGINEERING↗

CoverM: read alignment statistics for metagenomics

SUMMARY: Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. AVAILABILITY AND IMPLEMENTATION: CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.

Aroney, Samuel T N↗

Cloud detection using satellite measurements of infrared and visible radiances for ISCCP

This paper, the first of three, describes the cloud detection part of the International Satellite Cloud Climatology Project (ISCCP) analysis. Key features of the cloud detection alogrithm are (1) use of space and time radiance variation tests over several different space and time domains to account for the global variety of cloudy and clear characteristics, (2) estimation of clear radiance values for every time and place, and, (3) use of radiance thresholds that vary with the type of surface and climate regime. Design of the detection algorithm was supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes. A summary of these statistical results is presented to illustrate how the cloud detection method works in a variety of circumstances. The sensitivity of the results to changing test parameter values is determined to provide a first estimate of the uncertainty of ISCCP cloud amounts. These test results (which exclude polar regions) suggest detection uncertainties of about 10% with possible negative biases of 5% (especially at night).

Rossow, Illiam B.↗

A summary of Nimbus-6 temperature retrieval accuracy statistics for DST-5 and DST-6

Statistics compiled from Nimbus-6 temperature retrievals processed during the 18 August-4 September 1975 DST-5, and the 1 February-4 March 1976 DST-6 are addressed. All statistics were compiled from samples of colocated satellite sounding and radiosonde measurements. The colocation window is 222 km in space and 6 hours in time.

Hayden, C. M.↗

Generating Emissions Inventory for Carbon Capture and Storage Analysis for Carbon-Intensive Industrial Sectors

Decarbonizing the industrial sector is critical to achieve carbon dioxide (CO2) emissions reductions goals of the Biden Administration. Currently available decarbonization options include electrification, fuel switching to zero carbon fuels like green hydrogen (H2) and carbon capture and storage (CCS). Application of post-combustion carbon capture (PCCC) technology in the power sector, as well as research at the U.S. Department of Energy's Fossil Energy and Carbon Management (FECM) Office has shown that its application in the industrial sector could have co-benefits in the form of emissions reductions of non-CO2 regulated pollutants. For example, solvent based PCCC systems require pre-conditioning of flue gas to remove sulfur and particulate matter (PM) upstream of the CO2 absorber. However, there is a lack of understanding about the type of non-CO2 pollutants which can be captured and the amount of reduction possible. PCCC application differs across industrial sectors as it depends on the availability of decarbonization options, characteristics of industrial processes and the amount and composition of pollutant flows. Certain facilities can also have multiple effluent flows with or without a CO2 stream. As such, understanding industrial processes and their effluent flows in detail is required to quantify the co-benefits opportunities presented by PCCC. Considering this requirement, the goal of this analysis is to develop a high-resolution inventory of effluent flows from facilities of 8 industrial sectors in the U.S. These industrial sectors - ethanol, ammonia, cement, steel, natural gas processing, hydrogen, petroleum refining and wood and pulp products - have carbon-intensive effluent flows, and thus are prime candidates for PCCC applications. In this study, we map the composition of pollutant flow from flue stacks across the identified facilities. Using data available in three Environmental Protection Agency (EPA) databases - the Green House Gas Reporting Program (GHGRP), the National Emissions Inventory (NEI) and the Toxic Release Inventory (TRI), we create a combined inventory which lists the type, amount, and concentration of pollutant flows. Using total weight of the pollutant flow back calculated from observed data for CO2 concentrations in flue gas for individual sectors, we calculate the concentration of each pollutant in the flue gas stream. Thus, the resultant emissions inventory includes the following details for each facility in the sector: facility-level and if possible, process-level pollutant flows, concentrations of pollutants in the flue gas, and geographical coordinates of the facilities. A detailed statistical analysis and summary allows us to search for erroneous data and remove them from the final inventory. The generation of the inventory is achieved using a python-based framework which can recreate this inventory for other industrial sectors as well as using newer releases of emission inventories from EPA. The statistical analysis performed on the inventory is also calibrated and automated to identify outliers efficiently.

air pollutants↗

Grid Reliability Statistics [SWR-25-45]

This codebase houses a suite of statistical and descriptive analyses of NERC GADS data of interest to grid modelers and planners. This repository is envisioned to house a collection of statistical and descriptive summaries of NERC GADS data, particularly summaries that on their own might be insufficient to warrant publication. In addition, it is intended to disseminate results rather than to enable reproduction as GADS is non-public.

Murphy, Sinnott [National Renewable Energy Laborat↗

Commercial Building Energy Code Field Study (Final Report)

What is the dollar value of increasing compliance with the energy code? Ultimately, this is the question that policy makers, funders, and program implementers care about. With this in mind, Pacific Northwest National Laboratory (PNNL) developed a new methodology capable of determining how much energy cost savings could potentially be gained through better compliance with the code (Rosenberg et al. 2016). On March 2, 2016 DOE issued Funding Opportunity Announcement (FOA) Number DE-FOA-0001532, requesting proposals to develop and test a methodology for identifying the commercial energy code requirements that can provide the maximum cost-effective energy and cost savings through increased compliance. Awarded to the Institute for Market Transformation, this report summarizes the development and testing of a commercial code compliance methodology, including a summary of results, statistical analysis of sample, and best practices for others implementing a commercial energy code compliance field study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High-Throughput Directed Evolution of Marine Microalgae and Phototrophic Consortia for Improved Biomass Yields (Final Report)

Primary project achievements include using selective pressures (O 2 , light, temperature) and developing culturing regimes for the diatom Nitzschia inconspicua str. hildebrandi to attain enrichments with an ~90% increase in areal biomass productivity relative to the parental strain under pond-mimicking conditions with high O 2 stress in laboratory bioreactors. The resulting strain (GAI-337) was tested further for dilution time, culture density, CO 2 supplementation, pH, temperature, and dissolved O 2 concentration under outdoor pond-mimicking conditions to improve areal productivities. These experiments yielded an optimum harvest and dilution time just after sunset, ~0.45 g AFDW L -1 initial culture density for maximal productivities, no requirement for CO 2 supplementation or pH control, maximal performance under a diel temperature curve going from 24 °C at night to 36 °C during the day, and benefits from some O 2 removal from the culture by bubbling with air. Using pond-mimicking laboratory bioreactors, N. inconspicua GAI-337 achieved ~42 g AFDW m -2 d -1 . Nutrient limitation experiments resulted in a biomass composition that equated to ~160 Gallons of Gasoline Equivalent energy per ton AFDW, highlighting the potential of GAI-337 as a promising renewable fuel feedstock strain. Genome resequencing has revealed genome alterations potentially contributing to the improved growth of GAI-337 in the laboratory. Based on the comparative analyses of the GAI-337 and GAI-229 (reference) strains, we identified 144 single nucleotide substitutions that resulted in amino acid change, 7 single nucleotide substitutions that resulted in protein truncation; 5 deletions; and 1 frameshift mutation. From the mutations that potentially affect expression of functionally annotated genes, particular interest was noted for an interferon-induced 6-16 family protein that may be involved in the host immune response against microbe invasion; the chaperone protein DnaK, which may function to protect the folding of proteins within the cell; and SPRY domain protein that is found in many eukaryotic proteins important in cell signaling pathways. Transcriptome analysis revealed over 1000 genes with increased transcript levels. Many of these and many of the genes with mutations are not yet functionally annotated and an increased bioinformatics effort is necessary to more completely analyze the Nitzschia inconspicua genome. Adaptive laboratory evolution (ALE) was performed for over 300 days using consecutive 0.5°C temperature increases in a constant temperature incubator to attain greater thermal tolerance in Nitzschia inconspicua. The adapted strain was able to grow at a constant temperature of 37.5°C; whereas this constant temperature was lethal to the parental control, which had an upper temperature boundary of 35.5°C prior to adaptive evolution. Several high-temperature clonal isolates were obtained from the evolved population following ALE, and increased temperature tolerance was observed in clonal adapted cultures. The final temperature adaptation was maintained through cryopreservation and was observed in multiple clonal isolates, including multiple clonal isolates with significantly increased cell size, indicating the potential occurrence of a sexual cycle during the clonal isolation process. A survey of Nannochloropsis strains was conducted for tolerances to high pH and high bicarbonate media. Nannochloropsis granulata showed promising growth in diel bioreactors and was successfully grown at the GAI Kauai farm site in long-term growth campaigns. Co-culturing using Nitzschia inconspicua, Nannochloropsis and a cyanobacterium were assembled in the laboratory to determine if productivity synergies could be attained. Although all strains grew well in the laboratory high-bicarbonate media individually, the cyanobacterium quickly outgrew the other strains in the laboratory consortium pushing the co-culture away from a diverse (and potentially synergistic assemblage) phototroph culture towards a monoculture dominated by the cyanobacterium. Several outdoor growth campaigns were conducted, with productivities ranging between 10-20 g/m 2 /d of biomass. The best performing strain in the laboratory (GAI-337) did not outperform reference strains at the Kauai farm under the conditions used. Addition growth campaigns are necessary under conditions that result in higher biomass (>20 g/m 2 /d) and that attain higher O 2 levels are likely necessary. Initial data indicate that the thermally adapted strain did slightly better than the control strain at higher temperatures; however, additional campaigns are necessary to establish statistical significance. In summary, Nitzschia inconspicua is able to attain exemplary biomass and lipid yields in the laboratory bioreactors. Strain evolution to both O 2 and temperature resulted in targeted strain improvements. Additional outdoor campaigns are necessary to determine if laboratory improvements translate to the field.

09 BIOMASS FUELS↗

Advanced probabilistic method of development

Advanced structural reliability methods are utilized on the Probabilistic Structural Analysis Methods (PSAM) project to provide a tool for analysis and design of space propulsion system hardware. The role of the effort at the University of Arizona is to provide reliability technology support to this project. PSAM computer programs will provide a design tool for analyzing uncertainty associated with thermal and mechanical loading, material behavior, geometry, and the analysis methods used. Specifically, reliability methods are employed to perform sensitivity analyses, to establish the distribution of a critical response variable (e.g., stress, deflection), to perform reliability assessment, and ultimately to produce a design which will minimize cost and/or weight. Uncertainties in the design factors of space propulsion hardware are described by probability models constructed using statistical analysis of data. Statistical methods are employed to produce a probability model, i.e., a statistical synthesis or summary of each design variable in a format suitable for reliability analysis and ultimately, design decisions.

Wirsching, P. H.↗

A Deterministic Annealing Approach to Clustering AIRS Data

We will examine the validity of means and standard deviations as a basis for climate data products. We will explore the conditions under which these two simple statistics are inadequate summaries of the underlying empirical probability distributions by contrasting them with a nonparametric, method called Deterministic Annealing technique

clustering↗