Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data quality analysis”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Statistical Evaluation of Combining Human Productivity Metrics in the Indoor Environment

The potential of improving human productivity by providing healthy indoor environments has been a consistent interest in the building field for decades. This research field's long-standing challenge is to measure human productivity given the complex nature of office work. Previous studies have diversified productivity metrics, allowing greater flexibility in collecting human data; however, this diversity complicates the ability to combine productivity metrics from disparate studies within a meta-analysis. This study aims to categorize existing productivity metrics and statistically assess which categories show similar behavior when used to measure the impacts of indoor environmental quality. The 106 productivity metrics compiled were grouped into six productivity metric categories: neurobehavioral speed, accuracy, neurobehavioral response time, call handling time, self-reported productivity, and performance score. Then, this study set neurobehavioral speed as the baseline category given its fitness to the efficiency-based definition of productivity (i.e., output versus input) and conducted three statistical analyses with the other categories to evaluate their similarity. As results, the categories of neurobehavioral response time, self-reported productivity, and call handling time were found to have statistical similarity with neurobehavioral speed. This study contributes to creating a constructive research environment for future meta-analyses to understand which human productivity metrics can be combined with each other.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modular automated bottom-up proteomic sample preparation for high-throughput applications

Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Augmented Human Analysis (AHA)

Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.

42 ENGINEERING↗
DOE OSTI2024

Metagenome-assembled genomes provide insight into the metabolic potential during early production of Hydraulic Fracturing Test Site 2 in the Delaware Basin

Demand for natural gas continues to climb in the United States, having reached a record monthly high of 104.9 billion cubic feet per day (Bcf/d) in November 2023. Hydraulic fracturing, a technique used to extract natural gas and oil from deep underground reservoirs, involves injecting large volumes of fluid, proppant, and chemical additives into shale units. This is followed by a “shut-in” period, during which the fracture fluid remains pressurized in the well for several weeks. The microbial processes that occur within the reservoir during this shut-in period are not well understood; yet, these reactions may significantly impact the structural integrity and overall recovery of oil and gas from the well. To shed light on this critical phase, we conducted an analysis of both pre-shut-in material alongside production fluid collected throughout the initial production phase at the Hydraulic Fracturing Test Site 2 (HFTS 2) located in the prolific Wolfcamp formation within the Permian Delaware Basin of west Texas, USA. Specifically, we aimed to assess the microbial ecology and functional potential of the microbial community during this crucial time frame. Prior analysis of 16S rRNA sequencing data through the first 35 days of production revealed a strong selection for a Clostridia species corresponding to a significant decrease in microbial diversity. Here, we performed a metagenomic analysis of produced water sampled on Day 33 of production. This analysis yielded three high-quality metagenome-assembled genomes (MAGs), one of which was a Clostridia draft genome closely related to the recently classified Petromonas tenebris. This draft genome likely represents the dominant Clostridia species observed in our 16S rRNA profile. Annotation of the MAGs revealed the presence of genes involved in critical metabolic processes, including thiosulfate reduction, mixed acid fermentation, and biofilm formation. These findings suggest that this microbial community has the potential to contribute to well souring, biocorrosion, and biofouling within the reservoir. Our research provides unique insights into the early stages of production in one of the most prolific unconventional plays in the United States, with important implications for well management and energy recovery.

natural gas↗
DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

How data science methods can improve the quality and efficiency of ICF and HEDP research

Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part I - Analysis with a Small Sample Size

Computer simulations, especially of complex phenomena, can be expensive, requiring high-performance computing resources. Often, to understand a phenomenon, multiple simulations are run, each with a different set of simulation input parameters. These data are then used to create an interpolant, or surrogate, relating the simulation outputs to the corresponding inputs. When the inputs and outputs are scalars, a simple machine learning model can suffice. However, when the simulation outputs are vector valued, available at locations in two or three spatial dimensions, often with a temporal component, creating a surrogate is more challenging. In this report, we use a two-dimensional problem of a jet interacting with high explosives to understand how we can build high-quality surrogates. The characteristics of our data set are unique - the vector-valued outputs from each simulation are available at over two million spatial locations; each simulation is run for a relatively small number of time steps; the size of the computational domain varies with each simulation; and resource constraints limit the number of simulations we can run. We show how we analyze these extremely large data-sets, set the parameters for the algorithms used in the analysis, and use simple ways to improve the accuracy of the spatio-temporal surrogates without substantially increasing the number of simulations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Measurement of the angular dependence of the spontaneous Raman scattering in anisotropic crystalline materials using spherical samples: Potassium dihydrogen phosphate as a case example

A specialized experimental configuration was created to allow for more-accurate characterization of the spontaneous Raman scattering properties in anisotropic materials. This need stems from the challenges, arising from the complexity of light propagation, in obtaining accurate measurements of the angular dependence of the Raman scattering cross section in birefringent materials. The nonlinear optical material KH 2 PO 4 (KDP) is used as a model medium. Our report is motivated by the need to improve our understanding and management of transverse stimulated Raman scattering in KDP crystals and its deuterated analog, DKDP, typically used for frequency conversion and polarization control in large-aperture laser systems. Key to this experimental platform is the use of high-quality spherical samples that enable one to measure the Raman scattering cross section in a wide range of geometries using only a single sample. The effect of polarization rotation of both the pump light and the collected Raman signal must be carefully considered in data analysis and can give rise to artifacts, which can, in part, be mitigated by reducing the input and collection cone angles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Determining the Hubble constant through cross-correlation of galaxies and gravitational waves

Gravitational-wave (GW) standard sirens have the potential to measure the Hubble constant 𝐻 0 in the local Universe independent of the distance ladder and thus offer unique insights into the Hubble tension. A key challenge with standard sirens is detecting their electromagnetic counterparts and therefore assigning redshifts to the measured distances. One promising way to proceed is to utilize GW dark sirens—events without an identified electromagnetic counterpart—and cross-correlate their angular distribution with that of galaxies. We present a quantitative study of how precisely the Hubble constant can be measured using tomographic cross-correlation between galaxies and GW sources. Altogether, we find that the constraints on 𝐻 0 will be limited by the quality and quantity of GW data. We find that percent-level constraints on 𝐻 0 will primarily depend on achieving small distance uncertainties (𝜎 𝑑𝐿 = 0.1⁢𝑑 𝐿 ), obtaining a large number of GW dark sirens (≳ 5⁢000) and accurate sky localization in the tomographic analysis.

Cosmological parameters↗
DOE OSTI2023

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterize, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalizes a previous measure of the information gained by making the cryo-EM reconstruction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Lifelong cytomegalovirus and early‐LIFE irradiation synergistically potentiate age‐related defects in response to vaccination and infection

Abstract While whole‐body irradiation (WBI) can induce some hallmarks of immune aging, (re)activation of persistent microbial infection also occurs following WBI and may contribute to immune effects of WBI over the lifespan. To test this hypothesis in a model relevant to human immune aging, we examined separate and joint effects of lifelong latent murine cytomegalovirus (MCMV) and of early‐life WBI over the course of the lifespan. In late life, we then measured the response to a West Nile virus (WNV) live attenuated vaccine, and lethal WNV challenge subsequent to vaccination. We recently published that a single dose of non‐lethal WBI in youth, on its own, was not sufficient to accelerate aging of the murine immune system, despite widespread DNA damage and repopulation stress in hematopoietic cells. However, 4Gy sub‐lethal WBI caused manifest reactivation of MCMV. Following vaccination and challenge with WNV in the old age, MCMV‐infected animals experiencing 4Gy, but not lower, dose of sub‐lethal WBI in youth had reduced survival. By contrast, old irradiated mice lacking MCMV and MCMV‐infected, but not irradiated, mice were both protected to the same high level as the old non‐irradiated, uninfected controls. Analysis of the quality and quantity of anti‐WNV immunity showed that higher mortality in MCMV‐positive WBI mice correlated with increased levels of MCMV‐specific immune activation during WNV challenge. Moreover, we demonstrate that infection, including that by WNV, led to MCMV reactivation. Our data suggest that MCMV reactivation may be an important determinant of increased late‐life mortality following early‐life irradiation and late‐life acute infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantifying Nitrogen Bioextraction by Seaweed Farms – A Real-time Modeling-Monitoring Case Study in Hood Canal, WA

Anthropogenic nitrogen input is the leading cause of increasing marine eutrophication. Cultivating seaweeds at large scales may provide a viable solution for mitigating eutrophication especially when nitrogen extraction is achieved as an added environmental benefit of biofuel and food production. As the first step to promoting and implementing this solution, it is critical to quantify and assess the effectiveness of nitrogen removal by existing seaweed farms. Pacific Northwest National Laboratory and Hood Canal Mariculture Inc. conducted a case study by applying a coupled modeling-monitoring approach to quantify nitrogen bioextraction by a one-hectare commercial sugar kelp farm in Hood Canal, WA. Through this two-year effort, the project team conducted intensive field monitoring of water quality parameters at the farm site and subsequent laboratory analysis. Meanwhile, the team applied a coupled hydrodynamic-macroalgal growth model to simulate kelp growth and nitrogen bioextraction over the two kelp growing seasons. The model results showed an overall good comparison with field monitoring data and showed the effectiveness of nitrogen removal by kelp farms. The established modeling-monitoring approach can also be transferable for supporting broader research activities in macroalgal farming and eutrophication mitigation.

09 BIOMASS FUELS↗
DOE OSTI2023

TRACER-Coastal Urban Boundary-Layer Interactions with Convection (TRACER-CUBIC) Field Campaign Report

To better understand the complicated web of processes governing convective cloud life cycle and aerosol-convection interactions, the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility supported deployment of a variety of advanced atmospheric measurement systems to the greater Houston, Texas, area from 1 October 2021 to 30 September 2022 as part of the Tracking Aerosol Convection Interactions Experiment (TRACER). Houston was selected as a study area because isolated convection and a variety of aerosol conditions are common in this region. This one-year ARM Mobile Facility (AMF) deployment featured a four-month intensive operational period (IOP) during summer 2022 (1 June–30 September). The ARM instrumentation was deployed at three sites along an east-west transect from La Porte, Texas to an ancillary site in a less-polluted rural region southwest of downtown Houston (Figure 1). At the La Porte Site, which is located near the Houston ship channel in an area that experiences significant pollution, the first ARM Mobile Facility (AMF1) was deployed. During the IOP, the ARM tethered balloon system (TBS) operated at the ancillary site. The second-generation C-Band Scanning ARM Precipitation Radar (CSAPR) operated near Pearland, Texas, roughly halfway between the Laporte and ancillary sites. As part of the TRACER- Coastal Urban Boundary-Layer Interactions with Convection (CUBIC) project, three boundary-layer profiling systems) were deployed along a north-south transect spanning from the University of Houston Coastal Center to the Aldine site north of downtown Houston (also blue dot in Figure 1) during the TRACER IOP. These systems included the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory CLAMPS2 (C2), which was deployed at the UHCC, the University of Wisconsin SPARC, which was deployed at the ARM CSAPR site near Pearland (orange diamond in middle of map in Figure 1), and the University of Oklahoma CLAMPS1 (C1), which was deployed at Aldine. These three systems have been successfully operated in various field campaigns, providing data sets that collectively offer new insights into atmospheric-boundary-layer (ABL) processes, sea-breeze (SB) circulations, and convection initiation (CI). For the TRACER IOP window, these systems ran continuously between 1 June and 26 September, 2022. Due to commitments to NOAA projects, the Doppler lidar at the UHCC site was not available until 24 June 2022. The CLAMPS and SPARC profiling systems are self-contained platforms that have benefited from several years of development and deployment. Instruments and data processing were maintained remotely, which made the 4-month deployment for the TRACER-CUBIC IOP period possible. The same basic instrument configuration comprises each system: a scanning Doppler wind lidar for flow characterization and passive profiler(s) for characterizing planetary-boundary-layer (PBL) thermodynamic properties. Each platform includes a Halo Streamline Doppler wind lidar, an Atmospheric Emitted Radiance Interferometer (AERI), and a surface meteorology station. CLAMPS1 and CLAMPS2 each also include a microwave radiometer (MWR, Figure 1d). The TRACER-CUBIC hypotheses included (i) Interactions of SB and urban circulations and how they affect the PBL structure in the Houston environment, causing spatially (horizontally and vertically) and temporally highly variable flow patterns, (ii) heat, moisture, and aerosol transport and mixing depend on these flow dynamics, and (iii) an improved understanding of the flow patterns and PBL structure are critical for investigating the processes leading to CI. To test these hypotheses, the project aimed at (i) characterizing SB circulations and their impacts on the diurnal evolution of the structure of the ABL, (ii) studying the evolution of Houston’s complex urban boundary layer, and (iii) identifying effects of urban-induced circulations on pre-convective environments. TRACER-CUBIC observations generally provide good coverage during the summer IOP. Initial screening of the data indicates a good number of cases with bay-breeze (BB) and/or SB signatures, local CI, and interesting boundary-layer features such as strong nocturnal low-level jets (LLJs, Table 1). The numbers listed in rows 3-5 in this table will be further updated as part of ongoing in-depth analyses and systematic identification of local circulations and CI events. More detailed information about the data availability and quality for each instrument is provided in the “readme” files that were submitted to the ARM Data Center along with each archived data sets. These “readme” files also provide instrument descriptions, information about the data collection and processing procedures, data formats, and any additional information relevant for further data analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)

A critical element in efective wildlife management is monitoring the status of wildlife populations; however, resources to monitor wildlife populations are typically limited. We compared cost efectiveness of three common population estimation methods (i.e. non-invasive DNA sampling, camera sampling, and sampling from trapping) by applying them to wild pigs (Sus scrofa) across three habitats in South Carolina, U.S.A where they are invasive. We used mark-recapture analyses for fecal DNA sampling data, spatially-explicit capture-recapture analyses for camera sampling data, and a removal analysis for removal sampling from trap data. Density estimates were similar across methods. Camera sampling was the least expensive, but had large variances. Fecal DNA sampling was the most expensive, although this technique generally performed well. We examined how reductions in efort by method related to increases in relative bias or imprecision. For removal sampling, the largest cost savings while maintaining unbiased density estimates was from reducing the number of traps. For fecal DNA sampling, a reduction in efort only minimally reduced costs due to the need for increased lab replicates while maintaining high quality estimates. For camera sampling, efort could only be marginally reduced before inducing bias. We provide a decision tree for researchers to help make monitoring decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

HopBox: An image analysis pipeline to characterize hop cone morphology

Abstract Hop cone morphology can influence picking and drying ability, and color can impact consumer preference and may be indicative of quality. However, these characteristics are not generally evaluated in hop breeding programs due to the tedious nature of trait quantification and the extensive variation among cones within a genotype. We developed the HopBox, which is a simply constructed light box with a camera mount, and a publicly available image processing pipeline that identifies hop cones within color‐corrected images, reads a QR code within the image, and outputs data on hop cone length, width, area, perimeter, openness, weight, color, and density. The trained model was applied to images of 500 cones each from 15 replicated advanced hop genotypes from the USDA‐ARS breeding program in Prosser, Washington. Analysis of variance revealed significant ( p < 0.001) differences between genotypes for all traits measured, enabling breeders to discriminate between genotypes for selection purposes. Broad sense heritability for all traits ranged from 0.23 to 0.59. A random sampling of hop cones from the complete dataset revealed that imaging only 5–10 cones adequately captured genotypic variation and provided acceptable rank correlations ( r s > 0.75); however, increasing the sample size to 30 provided optimal precision. Instructions for constructing a HopBox and the code for the analysis pipeline are publicly available online and have wide applicability for hop breeding and research.

Altendorf, Kayla R.↗
DOE OSTI2026

The U.S. Agrivoltaic Shading Tool: A National-Scale Interface for Modeling Light and Shade Patterns in Ten Common Agrivoltaic Configurations

Agrivoltaic systems are dual-use configurations that co-locate agriculture and photovoltaic (PV) infrastructure and require careful design to balance crop performance and energy generation. A critical element of agrivoltaic design is the spatial and temporal distribution of irradiance and shade within and around PV arrays. To support research, planning, and stakeholder decision-making, we introduce the U.S. Agrivoltaic Shading Tool, a novel web-based application that delivers high-resolution irradiance and photosynthetically active radiation (PAR) modeling for ten standardized PV configurations across the conterminous United States. The tool leverages the National Laboratory of the Rockies (NLR) System Advisor Model (SAM) to perform detailed irradiance simulations, using meteorological data from the National Solar Radiation Database (NSRDB). Outputs include seasonal, monthly, weekly, and diurnal patterns of available sunlight, amount of shade, irradiance, and PAR at ground level within agrivoltaic system footprints. For a user's selected location, these results are visualized through interactive visualizations, heatmaps, and time-series plots, designed to be accessible to both technical and non-technical users. In addition to facilitating rapid spatial exploration of agrivoltaic light environments, the tool will offer seamless integration with the InSPIRE Agrivoltaics Design and Analysis Model (ADAM). This optional workflow will allow users to port selected site and configuration parameters into a more advanced modeling environment for further customization of structural layouts, crop-system compatibility, power generation, and technoeconomic performance. Finally, to promote open science, the entire dataset will be hosted and available for open access through the OpenEI platform. By standardizing and disseminating high-quality irradiance data and design tools, the U.S. Agrivoltaic Shading Tool supports a wide range of users, including researchers, landowners, energy developers, and policymakers, in evaluating the agronomic and energetic feasibility of agrivoltaic systems across the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weak lensing with HSC and SDSS using the emulator based halo model

We present cosmology results from a blinded joint analysis of cosmic shear, galaxy-galaxy weak lensing, and projected galaxy clustering, measured from the Hyper Suprime-Cam three-year (HSC-Y3) shape catalog and the Sloan Digital Sky Survey (SDSS) DR11 spectroscopic galaxy catalog—a cosmology analysis. We define luminosity-cut, and therefore nearly volume-limited, samples of SDSS galaxies to serve as the tracers of w p and as the lens samples for Δ⁢Σ in three spectroscopic redshift bins spanning the range 0.15± and Δ⁢Σ measurements, we use a single sample of about seven million source galaxies over 416 deg 2 , selected from HSC-Y3 based on having photometric redshifts (photo-z) greater than 0.75. The deep, high-quality HSC-Y3 data enable significant detections of the Δ⁢Σ signals, with integrated signal-to-noise ratio S/N~24 in the range 3≤R/[ℎ –1 Mpc]≤30 over the three lens samples. ξ ± has S/N ~19 in the range 8' ≤$\theta$ ≤5⁢0' and 30' ≤$\theta$ ≤15⁢0' for ξ + and ξ – , respectively. For cosmological parameter inference, we use the dark emulator package, combined with a halo occupation distribution prescription for the relation between galaxies and halos, to model w p and Δ⁢Σ down to quasinonlinear scales, and we estimate cosmological parameters after marginalizing over nuisance parameters. In our baseline analysis we employ an uninformative flat prior of the residual photo-z error, given by Π⁡(Δz ph )=$U$⁡(–1,1), to model a residual bias in the mean redshift of HSC source galaxies. Comparing the relative lensing amplitudes for Δ⁢Σ in the three redshift bins and for ξ ± with the single HSC source galaxy sample allows us to calibrate the photo-z parameter Δ⁢z ph to the precision of σ⁡(Δ⁢z ph )≃0.09. With these methods, we obtain a robust constraint on the cosmological parameters for the flat Λ⁢CDM model: S 8 =σ 8 ⁢(Ω m /0.3) 0.5 =$0.763^{+0.040}_{–0.036}$, or the best-constrained parameter given by $S^{'}_{8}$ = σ 8 ⁢(Ω m /0.3) 0.22 =0.721±0.028, determined with about 4% fractional precision. Based on multidimensional tension metrics, HSC-Y3 data exhibits about 2.5σ tension with the cosmological constraint inferred by Planck for the Λ⁢CDM model, and hints at a nonzero residual photo-z bias implying that the true mean redshift of the HSC galaxies at z ≳0.75 is higher than that implied by the original photo-z estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗