Search NASA⌕ Search

SEARCH · Search NASA

Results for “Variable selection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Representative Period Selection for Robust Capacity Expansion Planning in Low-carbon Grids

With the increasing urgency to decarbonize power systems, while mitigating extreme events, capacity expansion models can play a vital role in reliably planning the expansion of power systems and facilitating the integration of renewable energy sources. Optimizing capacity expansion generally involves selecting surrogate representative days from forecasts of load and the generation profiles of variable renewable energy resources. To properly select those representative days, we propose a novel input-based approach in combination with the k-means clustering algorithm that utilize three unique operational inputs: load shedding, renewable curtailment, and transmission congestion. The proposed method allows for more robust and cost-effective capacity planning. The method is validated using a capacity expansion model and a production cost model based on California Independent System Operator (CAISO)'s decarbonization goals, and results in reduced costs and drastically lower load shedding.

Anderson, Osten P.↗
DOE OSTI2020

NMDQi Thermal-hydraulic Optimization, Analysis, and Scoping Tool (TOAST)

The Thermal-hydraulic Optimization, Analysis, and Scoping Tool (TOAST) is developed to support the thermal-hydraulic aspects for the design of optimal irradiation vehicles for the Nuclear Materials Discovery and Qualification initiative (NMDQi). TOAST is currently a MATLAB-based tool that utilizes a simplified steady-state analytical model where a radial thermal resistance circuit is utilized to account for conduction through up to 2 capsules, a gas gap, a thermal bond, a sample cladding, and a sample, as well as the convection from a water coolant outside the capsules. The geometry is discretized axially along the user-defined height of a basket with a user-defined number of points/nodes, essentially turning TOAST into a semi-2D heat transfer analysis utility for cylindrical irradiation vehicles with practically any configuration, solving for temperatures radially at multiple axial locations. TOAST utilizes a Graphical User Interface (GUI) in-which a user can define the geometrical layout, the materials utilized for each component, the coolant characteristics, and the required solution. The user can define the geometry by inputting the diameters, thicknesses, and heights for each component, whereas the materials are defined via the user-provided constant or variable thermal conductivities. The user can select the coolant's inlet temperature, pressure, and the pressure drop across the height of the problem (which is used to calculate the velocity of the flow). Finally, the user can choose to solve for a sample heat generation rate limit by inputting temperature limits for the sample and capsules or can choose to purely solve for the axial temperature distributions of each component by inputting a pre-selected heat generation rate. Either way, TOAST provides the user with axial temperature distributions of all the components including the coolant, and the heat generation rate limit as well as the maximum outlet coolant temperature. The user can also choose to do one of 6 sensitivity analyses in TOAST. The sensitivity analyses yield plots of the sensitivity of the heat generation rate limit, the maximum coolant temperature, and the pressure limit for the annular components due to perturbations in 1-2 unknown variables based on the selected sensitivity analysis. Benchmarks between computations in TOAST and equivalent 2D axis symmetric ABAQUS models are presented, showing that TOAST results are within less than 3% of ABAQUS results in most cases, and a maximum of 8% difference in some cases. The benchmarks also revealed that this uncertainty is tied to the selection of an appropriate Nusselt number correlation and appropriate thermal conductivities, which the user can do from the GUI. Regardless, TOAST is demonstrated as a computationally efficient, highly accessible, and accurate utility for optimization and scoping s of studies different irradiation vehicles.

42 ENGINEERING↗
DOE OSTI2022

Variable Active Galactic Nuclei in the Galaxy Evolution Explorer Time Domain Survey

We searched the northern hemisphere fields of the Galaxy Evolution Explorer Time Domain Survey for galaxies with UV variability indicative of active galactic nuclei (AGNs). We identified 48 high-probability candidate AGNs from a parent sample of 1819 galaxies in the NASA Sloan Atlas catalog. We further characterized these systems using optical spectroscopic diagnostics, Wide-field Infrared Survey Explorer IR color selection criteria, and spectral energy distribution modeling. Of the 48 candidates, eight were identified as AGNs from optical emission lines, two were identified by their IR colors, and 28 were identified through spectral energy decomposition. Observational biases of each selection method are discussed in connecting these AGN subsamples to another. By selecting AGNs based on UV variability, we also identified six low-mass AGN candidates, all of which would have been missed by spectroscopic selection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Nine lensed quasars and quasar pairs discovered through spatially extended variability in Pan-STARRS

We present the proof of concept of a method for finding strongly lensed quasars using their spatially extended photometric variability through difference imaging in cadenced imaging survey data. We applied the method to Pan-STARRS, starting with an initial selection of 14 107Gaiamultiplets with quasar-like infrared colours from WISE. We identified 229 candidates showing notable spatially extended variability during the Pan-STARRS survey period. These include 20 known lenses and an additional 12 promising candidates for which we obtained long-slit spectroscopy follow-up. This process resulted in the confirmation of four doubly lensed quasars, four unclassified quasar pairs, and one projected quasar pair. Only three are pairs of stars or quasar+star projections. The false-positive rate accordingly is 25%. The lens separations are between 0.81″ and 1.24″, and the source redshifts lie betweenz = 1.47 andz = 2.46. Three of the unclassified quasar pairs are promising dual-quasar candidates with separations ranging from 6.6 to 9.3 kpc. We expect that this technique is a particularly efficient way to select lensed variables in the upcomingRubin-LSST, which will be crucial given the expected limitations for spectroscopic follow-up.

Astronomy & Astrophysics↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2021

Texture and microstructure evolution in thermomechanically processed Mg-Ca and Mg-Zn-Ca alloys

Mg-Zn-Ca alloys have the potential for producing Mg alloy sheets with weaker basal textures and therefore improved formability. Thermomechanical processing (TMP) also plays a substantial role in determining the final sheet texture. The interplay of these variables, TMP and alloying, complicates comparisons of alloys across the literature. This work systematically explores the texture evolution and recrystallization behavior in Mg-Ca and Mg-Zn-Ca alloys during plane strain compression (PSC) using a Gleeble thermomechanical simulator. It is demonstrated that the basal texture intensity and texture characteristics change significantly during post-deformation annealing, particularly in the ternary alloys. It is also shown that careful selection of the TMP processing variables used during PSC is essential to producing weak textures. In particular, it is important to limit the recrystallization that occurs between compressive hits. This is achieved by adjusting solute content, strain rate, and the duration of the soak between passes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Abstract. Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

58 GEOSCIENCES↗
DOE OSTI2021

Very Large Array imaging rules out precessing radio jets in three DES–SDSS-selected candidate periodic quasars

ABSTRACT Periodic quasars have been suggested as candidates for hosting binary supermassive black holes (SMBHs), although alternative scenarios remain possible to explain the optical light-curve periodicity. To test the alternative hypothesis of precessing radio jet, we present deep 6 GHz radio imaging conducted with NSF’s Karl G. Jansky Very Large Array (VLA) in its C configuration for the three candidate periodic quasars, DES J024703.24−010032.0, DES J024944.66−000036.8, and DES J025214.67−002813.7. Our targets were selected based on their optical variability using 20 yr long multicolour light curves from the Dark Energy Survey (DES) and the Sloan Digital Sky Survey (SDSS). The new VLA observations show that all three periodic quasars are radio-quiet with the radio loudness parameters measured to be $R\equiv f_{6\, {\rm cm}}/f_{{\rm 2500}}$ of ≲1.0–1.5 and the k-corrected luminosities νLν[6 GHz] of ≲5–21 × 1039 erg s−1. They are in stark contrast to previously known periodic quasars proposed as binary SMBH candidates such as the blazar OJ 287 and PG1302−102. Our results rule out optical emission contributed from precessing radio jets as the origin of the optical periodicity in the three DES–SDSS-selected candidate periodic quasars. Future continued optical monitoring and complementary multiwavelength observations are still needed to further test the binary SMBH hypothesis as well as other competing scenarios to explain the optical periodicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However, for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

09 BIOMASS FUELS↗
DOE OSTI2026

Public water supply infrastructure extensification and diversification in surface waters is insufficient to meet future demands in Texas

The data were developed to evaluate the capacity of existing and potential new surface water supply infrastructure to meet projected public water demands across districts in Texas under multiple future socioeconomic and climate scenarios. The database integrates hydrologic, water quality, infrastructure, energy, cost, demographic, and demand-projection information for candidate surface water supply locations. Candidate sites include stream reaches, waterbodies, reservoir surplus locations, and potential new reservoir sites. Water availability is characterized using historical and projected flow conditions, while site suitability is evaluated using five indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets include statewide candidate-site information, district-level demand projections under Shared Socioeconomic Pathways (SSPs), runoff-based allocation constraints, climate-stress metrics, and optimization outputs evaluating alternative infrastructure planning strategies. Optimization results compare Business-as-Usual (BAU) and All Surface Water (AllSW) demand-management approaches under both scaled and fixed cost-cap strategies. Associated validation datasets provide district-level feasibility assessments, infrastructure selection outcomes, cost-cap utilization, demand satisfaction metrics, and constraint diagnostics. Additional datasets quantify projected changes in storage and flow conditions as well as water availability stress for both existing and newly selected intake locations under the SSP5 scenario for mid-century and late-century climate conditions. Together, these datasets support assessment of the extent to which surface-water infrastructure expansion and diversification strategies can satisfy future public water demands while accounting for hydrologic, economic, and planning constraints across Texas. Dataset(s) Description Dataset_preoptimization.xlsx Comprehensive pre-optimization dataset containing candidate water-supply sites and associated hydrologic, water-quality, infrastructure, climate, demographic, runoff, and demand-projection variables used as inputs to the optimization analyses. Includes variable descriptions and the full statewide candidate-site database. District_level_site_selection.zip - Compressed archive containing all SSP-specific district-level optimization result files MESIO_ssp1_results.xlsx District-level site selection results for SSP1 (MESIO). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. MESID_ssp2_results.xlsx District-level site selection results for SSP2 (MESID). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. LCMRD_ssp3_results.xlsx District-level site selection results for SSP3 (LCMRD). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-Low_ssp4l_results.xlsx District-level site selection results for SSP4-Low (IRDev-Low). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-High_ssp4h_results.xlsx District-level site selection results for SSP4-High (IRDev-High). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. RSIM_ssp5_results.xlsx District-level site selection results for SSP5 (RSIM). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. tx_hydrological_stress.xlsx Hydrological stress dataset for existing and newly selected intake locations. Includes projected mid-century and late-century changes, gain/loss classifications, planning strategy information, and accompanying variable descriptions. Also includes water-stress metrics derived from historical and projected low-flow conditions.

Okoye, Perpetua I. (ORCID:0000000215545033)↗
DOE OSTI2022

Strong parallel evidence of selection during switchgrass sward establishment in hybrid and lowland ecotypes

Switchgrass sward establishment results in up to 90% seedling mortality. The degree of selection during sward establishment has not been reported using modern genetic methods. Pooled leaf samples were sequenced from replicated swards of 46 half-sib families from two breeding groups (lowland and hybrid) before and through 3 years of stand establishment. Pooled allele frequencies were then assessed using fixation indices (Fst) and an independent data set was used to predict the polygenic impact of establishment selection on two traits (heading date and winter survivorship). Last, the DNA pools were assigned survival rankings to predict the sward survival genomically estimated breeding values within the training data set. Strong and parallel selection occured in both breeding groups. Five genomic regions exceeded the significant threshold of 99.9% in >10 families, indicating consistent selection across families and breeding groups. Polygenic trait predictions determined that establishment selection was partially associated with winter survivorship but resulted in variable heading date alterations. The genomewide variation is consistent with selection for a small number of related parental lines. This study observed strong selection for a small number of hybrid and coastal ecotype individuals which are promising germplasm sources for improved sward survival. This confirms prior reports of sward selection during grassland establishment and highlights the strength of pooled DNA sequencing for survival traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Production of neo acids from biomass-derived monomers

Neo acids are highly branched carboxylic acids currently produced from fossil fuels. In this work, we produce renewable neo acids from lignocellulosic biomass-derived furan and keto acids via C–C coupling through hydroxyalkylation/alkylation (HAA), followed by ring-opening of furans through hydrodeoxygenation (HDO). Here, we show effective C–C coupling over acid catalysts. Catalyst screening and multi-parameter optimization using machine learning optimize the yield and elucidate the correlation between variables and outcomes. We demonstrate selective furan ring-opening without affecting the carboxylic acid to make neo acids using a co-catalyst involving Pd supported on carbon and metal triflate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Investigating trends in actinide covalency and magnetism with 35/37 Cl SSNMR spectroscopy and first-principles calculations

Solid-state NMR (SSNMR) spectroscopy is a powerful technique for studying actinide chemistry but has been significantly limited due to the complex paramagnetism, and radiological hazards presented by these materials. Lanthanide and actinide salts often feature magnetic ordering and can be paramagnetic, ferromagnetic, or antiferromagnetic depending on temperature and electronic structure. Paramagnetic interactions can manifest in SSNMR both as secular spectral shifts and/or couplings as well as contributions from non-secular relaxation. Both effects can be directly measured with NMR and used to extrapolate rich chemical information such as coordination environments, bonding characteristics, local molecular dynamics, and correlation times. Typically, these studies are carried out on high-γ and highly abundant NMR-active isotopes (e.g., 1 H, 6/7 Li, 19 F, 23 Na, etc.) or on enriched rare isotopes (e.g., 2 H and 17 O), which can be expensive. Herein, we present a facile methodology to measure the 35/37 Cl electric-field gradient (EFG) and paramagnetic shift anisotropy (SA) tensor components using static wideline SSNMR measurements of LaCl 3 , NdCl 3 , UCl 3 , and UCl 4 . The static powder spectra were measured with both 35 Cl and 37 Cl SSNMR to increase the fidelity of the extracted tensor parameters. Variable temperature NMR of a select case confirms the Curie–Weiss paramagnetism. Relaxation measurements of both nuclei further corroborate observations owing to the paramagnetic relaxation enhancement and reveal simultaneous quadrupolar relaxation mechanisms. Density functional theory (DFT) calculations using Hubbard U corrections to the uranium valence orbitals show excellent agreement with experimental EFG tensor parameters and help describe the bonding characteristics in these lanthanide and actinide systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Data for Influence of Particle Size on NIR Spectroscopic Characterization of Sorghum Biomass for the Biofuel Industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum ( Sorghum bicolor ), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

Biomass Analytics↗
DOE OSTI2024

Innovative Joining Method for Hybrid Composites with Tailored Performance

In various industries, such as renewables and aerospace, the demand for carbon fiber is rapidly growing to meet the requirements of high-performance applications with optimized designs. Traditionally, these optimized designs have been limited to single-material composites due to process constraints. However, this study aims to explore the feasibility of transitioning between two different fiber types for pultrusion and filament winding processes, thereby enhancing design flexibility, and optimizing performance. This research introduces a novel method of hybrid reinforcement through splicing techniques. By employing a splicing method, fibers are merged prior to infusion, resulting in a transition of higher strength compared to traditional joints. Moreover, this technique enables the creation of composites with variable compositions, allowing for the selective placement of properties in a process previously characterized by static properties. The objective of this paper is to evaluate the mechanical properties of dry fiber splicing and its impact on the resulting composite material. Through comprehensive analysis, we aim to provide insights into the feasibility and advantages of employing this innovative splicing technique in composite manufacturing processes.

Guzorek, Steven↗