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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
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

On the measurement of shape: With applications to lunar regolith

With the renewed commitment from NASA and other commercial entities for a presence on the Moon, the importance of understanding the characteristics of lunar regolith and how to utilize it have become the target of increasing scrutiny. Much of what is known about lunar regolith was collected during and immediately after the Apollo program, however, analytical techniques and instrumentation have advanced in leaps and bounds in the subsequent decades. Specifically, dynamic image analysis systems have advanced to the point that millions of particles can have morphological characteristics automatically determined in relatively short time frames. Particle morphological data was collected on several lunar samples and a pair of widely used regolith simulants to ascertain the accuracy of these simulants and to explore statistical analysis methods of these large datasets. It is found that these morphology datasets can vary widely depending on the particle size of the particles, and simple averaging of the data skews the results heavily towards the numerically abundant size ranges, the fines. Different reporting methods are suggested to ameliorate these problems. When applied to the lunar regolith, the particles are noted to be less morphologically complex than initially suspected. Compared to the lunar material, the simulants are found to contain some more morphological variability and angular grains. Such difference is likely due to the wildly different comminution processes that these different powder systems are subjected to.

2D shape↗
DOE OSTI2025

Microwave-Assisted Dehydroaromatization of Flare Gas: Reactor Modeling, Plant-Wide Simulation and Economic Feasibility Analysis

Flaring is widely practiced in the oil, gas, and petrochemical sectors to ensure safety during upsets and maintenance but emits large amounts of GHGs, causing energy and economic losses. In the U.S., about one-third of Bakken gas (~250 MMSCFD) and ~100 MMSCFD from Eagle Ford are flared. Recovering this gas is essential for sustainability. Existing recovery methods—compression and reinjection (EOR), conversion to NGL, LNG/CNG, GTL, and GTW—are often limited by flowrate, composition, and variability, especially in unconventional wells. This study develops a microwave-assisted dehydroaromatization (DHA) process to convert flare gas into benzene, toluene, ethylene, and naphthalene. A laboratory reactor model is scaled up into a modular plant-wide system. Techno-economic (TEA) and life-cycle (LCA) analyses evaluate performance and sustainability, with sensitivity studies on plant capacity, electricity cost, and catalyst price confirming strong economic potential.

dehydroaromatization↗
DOE OSTI2023

Modes and Mechanisms of Pacific Decadal-Scale Variability

The modes of Pacific decadal-scale variability (PDV), traditionally defined as statistical patterns of variance, reflect to first order the ocean's integration (i.e., reddening) of atmospheric forcing that arises from both a shift and a change in strength of the climatological (time-mean) atmospheric circulation. While these patterns concisely describe PDV, they do not distinguish among the key dynamical processes driving the evolution of PDV anomalies, including atmospheric and ocean teleconnections and coupled feedbacks with similar spatial structures that operate on different timescales. In this review, we synthesize past analysis using an empirical dynamical model constructed from monthly ocean surface anomalies drawn from several reanalysis products, showing that the PDV modes of variance result from two fundamental low-frequency dynamical eigenmodes: the North Pacific–central Pacific (NP-CP) and Kuroshio–Oyashio Extension (KOE) modes. Both eigenmodes highlight how two-way tropical–extratropical teleconnection dynamics are the primary mechanisms energizing and synchronizing the basin-scale footprint of PDV. While the NP-CP mode captures interannual- to decadal-scale variability, the KOE mode is linked to the basin-scale expression of PDV on decadal to multidecadal timescales, including contributions from the South Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Aerosol emissions from water-lean solvents for post-combustion CO 2 capture

Advanced water-lean solvents (WLS) for post-combustion CO 2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO 2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO 2 Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO 2 , 2.3–4.2 % H 2 O, and 0–6 ppm sulfite (SO 3 ) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO 3 reacts with water in the flue gas to create H 2 SO 4 , which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q 2 ) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.

42 ENGINEERING↗
DOE OSTI2021

Electrification Futures Study: Power Systems Operation with Newly Electrified and Flexible Loads

The Electrification Futures Study (EFS) is a multi-year study designed to analyze the impacts of widespread electrification in the U.S. energy system. In this webinar we present the detailed power systems operational analysis from the final report in the EFS series. For this analysis, we simulate multiple 2050 power systems for the conterminous United States to assess how variations in the magnitude and shape of electricity demand driven by electrification, and the extent of load participation to more-actively provide grid services, might impact the hourly operation, operational costs, and emissions of various power systems in 2050. The impacts of electrification and demand flexibility are overlayed across systems with significantly greater penetrations of variable renewable energy than today. Overall, we find that the high electrification scenarios envisioned in the EFS with significant VRE penetration (66% of annual national generation) can be operated to meet future increased levels of electrified demand. We also find that demand-side flexibility can enhance operational efficiency and reduce overall annual production costs by $5-$10 billion. The complementary relationship between flexible electric vehicle charging loads and solar generation is particularly pronounced, but in the absence of demand-side flexibility electrification can lead to increased wind and solar curtailment. By helping to reduce renewable curtailment, flexibility can also reduce power sector CO2 emissions. The analysis highlights the value of increased integration and coordination of demand- and supply-side resources in future electric system planning and operations—particularly under high electrification futures.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Spatial and Temporal Interpolation Analysis Process of Shock Loading

The following document serves to describe the current process that is utilized by W-13 analysts to approximate the pressure-time boundary condition seen by a test object from shock tube loading. The intent of this report is to capture the current capability and to encourage continued growth and development. Information on the required input variables, mapping process, and generated output information is covered in the following sections. Throughout the document, gaps and weaknesses of the mapping process are noted to encourage future development efforts.

42 ENGINEERING↗
DOE OSTI2023

Statistical analysis and degradation pathway modeling of photovoltaic minimodules with varied packaging strategies

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.

electrical measurements↗
DOE OSTI2021

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale-level design of particulate composites for an optimal macroscale-level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS-based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three-parameter isotropic damage law is used to model microstructure-level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress-strain response.

42 ENGINEERING↗
DOE OSTI2022

Soil Respiration Response to Simulated Precipitation Change Depends on Ecosystem Type and Study Duration

Soil respiration—the flow of biologically-generated CO 2 from the soil surface to the atmosphere—is a major component of global carbon cycling, but the long-term response of this flux to altered precipitation regimes remains uncertain, due to different responses of soil respiration in distinct ecosystems with varying degrees of water limitation. We conducted a meta-analysis to determine the role of soil characteristics (organic carbon stock and clay content), study duration, and ecosystem type (e.g., forest, grassland, etc) in modifying the response of soil respiration to precipitation change. In general, decreased precipitation (N = 128 effect size pairs) decreased soil respiration rates. In contrast, increased precipitation (N = 141) had a more variable and on-average weak positive effect on this flux; significantly increasing soil respiration only in desert ecosystems. The long-term response of ecosystems varied, with those that are less constrained by water-availability (e.g., forests) showing acclimation over time to altered precipitation. Soil organic carbon stock strongly modified the response of soil respiration to decreased precipitation (p < 0.0001), but only weakly modified the response to increased precipitation (p < 0.01). Our results suggest that ecosystems with limited soil water-holding capacity and strong inherent water-limitation will show long-term dynamic change in soil respiration regardless of the direction of precipitation change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Elastic functional changepoint detection of climate impacts from localized sources

Detecting changepoints in functional data has become an important problem as interest in monitoring of climate phenomenon has increased, where the data is functional in nature. Here, the observed data often contains both amplitude (y-axis) and phase (x-axis) variability. If not accounted for properly, true changepoints may be undetected, and the estimated underlying mean change functions will be incorrect. In this article, an elastic functional changepoint method is developed which properly accounts for these types of variability. The method can detect amplitude and phase changepoints which current methods in the literature do not, as they focus solely on the amplitude changepoint. This method can easily be implemented using the functions directly or can be computed via functional principal component analysis to ease the computational burden. We apply the method and its nonelastic competitors to both simulated data and observed data to show its efficiency in handling data with phase variation with both amplitude and phase changepoints. We use the method to evaluate potential changes in stratospheric temperature due to the eruption of Mt. Pinatubo in the Philippines in June 1991. Using an epidemic changepoint model, we find evidence of a increase in stratospheric temperature during a period that contains the immediate aftermath of Mt. Pinatubo, with most detected changepoints occurring in the tropics as expected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R 2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. In conclusion, the Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93% and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Analysis of the rotationally-resolved infrared spectrum of CD 3 79 Br and CD 3 81 Br between 900 and 1400 cm -1

We report the infrared spectrum of CD 3 79 Br and CD 3 81 Br between 900 cm -1 and 1400 cm -1 has been analyzed at high resolution. In this region, two fundamental vibrational states, v 2 = 1 and v 5 = 1, an overtone state, v 3 = 2, and a combination state, v 3 = v 6 = 1, have been analyzed for both isotopologues. As found in other halomethane molecules, strong Coriolis resonances couple the v 2 = 1 state to the v 5 = 1 state, and similar resonances were also used to describe the coupling found between the v 5 = 1 state to the v 3 = 2 state. A new determination of the K-dependent constant A 0 was performed for each isotopologue through the use of perturbation-allowed transitions. The values for A 0 determined from the analysis are 2.6001898(26) and 2.6001905(27) for CD 3 79 Br and CD 3 81 Br, respectively. Ab initio calculations were performed to examine the efficacy of high-level theoretical calculations to accurately predict spectroscopic constants. The ab initio calculations had variable success with the largest errors associated with the prediction of the ΔB rotational constants of the interacting states

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evaluation of data collection bias of third molar stages of mineralisation for age estimation in the living

Abstract Age assessment of the living is a fundamental procedure in the process of human identification, in order to guarantee fair treatment of individuals, which has ethical, civil, legal, and medical repercussions. The careful selection of the appropriate methods requires evaluation of several parameters: accuracy, precision of the method, as well as its reproducibility. The approach proposed by Mincer et al. adapted from Demirjian et al. exploring third molar mineralisation, is one of the most frequently considered for age estimation of the living. Thus, this work aims to assess potential bias in the data collection when applying the classification stages for dental mineralisation adapted by Mincer et al. A total of 102 orthopantomographs, of clinical origin, belonging to individuals aged between 12 and 25 years ($ \bar{\textit x} $ = 20.12 years, SD = 3.49 years; 65 females, 37 males, all of Portuguese nationality) were included and a retrospective analysis performed by five observers with different levels of experience (high, average, and basic). The performance and agreement between five observers were evaluated using Weighted Cohen’s Kappa and the Intraclass Correlation Coefficient. To access the influence of impaction on third molar classification, variables were tested using ordinal logistic regression Generalised Linear Model. It was observed that there were variations in the number of teeth identified among the observers, but the agreement levels ranged from moderate to substantial (0.4–0.8). Upon closer examination of the results, it was observed that although there were discernible differences between highly experienced observers and those with less experience, the gap was not as significant as initially hypothesised, and a greater disparity between the classifications of the upper (0.24–0.49) and lower third molars (>0.55) was observed. When bone superimposition is present, the classification process is not significantly influenced; however, variation in teeth angulation affects the assessment. The results suggest that with an efficient preparation, the level of experience as a factor can be overcome. Mincer and colleague's classification system can be replicated with ease and consistency, even though the classification of upper and lower third molars presents distinct challenges.

de Oliveira Santos, Inês (ORCID:0000000267324347)↗
DOE OSTI2021

Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning.

Focal Area(s): We will use unsupervised machine learning methods to identify and quantify the influence of large scale natural modes of climate variability to gain insight into the observed and simulated seasonal average and extreme precipitation changes. Science Challenge: A recent paper, led by co-PI Mark Risser, finds that although much of the variability in seasonal average and extreme precipitation over CONUS is unforced, the effect of large-scale modes of circulation variability (such as ENSO, AMO, PNA, etc.) can be detected and attributed. However, it is unclear whether or not unsupervised learning methods can (a) replicate this finding or (b) yield insight into possible nonlinear behavior that was not captured in the initial statistical analysis. Further work would entail extending this framework to other global land areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Structural Break and Causal Analyses of U.S. Corn Use for Ethanol and Other Corn Market Variables

The causal basis for many of the relationships in models used to estimate the indirect effects of U.S. biofuels on global agricultural markets has not been adequately established. This paper addresses this gap by examining causal interactions among corn market variables through which the indirect effects of U.S. corn use for ethanol would be transmitted. Specifically, structural break and causal analyses of U.S. corn supply, uses, trade, and price are performed using quarterly data for marketing years 1986 to 2017. The structural break analysis identifies three breaks in corn use for ethanol that reflect the policy-driven evolution of U.S. corn ethanol production and other market factors. The causality analysis finds that U.S. corn use for ethanol is not a driver of the corn price and net corn exports. Changes in corn supply and domestic corn use are found to be the key factors in accommodating the large increase in corn use for ethanol between 2003 and 2010. These results mean that common assumptions linking U.S. corn ethanol production to large reductions in corn availability and exports, and higher global corn prices merit reconsideration.

09 BIOMASS FUELS↗