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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Life Cycle Analysis of Thermoelectric Power Generation in the United States

In this article, the basics of performing a life cycle analysis of thermoelectric power generation are discussed, with three examples of life cycle greenhouse gas (GHG) balances for thermoelectric power generation forms: coal, natural gas, and nuclear. The final section compares multiple electricity generation methods in the United States. Results are presented on the basis of 1 megawatt-hour (MWh) of electricity delivered to the end user. Environmental life cycle results for greenhouse gas (GHG) emissions are presented as carbon dioxide equivalents, based on 100-year global warming potentials (GWPs) established by the 6th Assessment Report from the Intergovernmental Panel on Climate Change in 2021, commonly referred to as AR6 GWP values (IPCC, 2021). Additional details on other environmental life cycle impacts from non-GHG emissions to air, emissions to water, solid waste generation, and land use are available on the Department of Energy, National Energy Technology Laboratory’s Life Cycle Analysis website: www.netl.doe.gov/LCA.

Cutshaw, Ashley↗
DOE OSTI2022

Generation of localized reactor point kinetics parameters using coupled neutronic and thermal fluid models for pebble-bed reactor transient analysis

The systems analysis of anticipated operating occurrences and design basis accidents for pebble-bed reactor systems requires knowledge of neutron point kinetics equations (PKE) parameters. Typically, the generation of PKE parameters is performed in a global manner using standalone neutronics calculations, without the inclusion of thermal fluid distributions. We utilize Griffin and Pronghorn for generating global and local PKE parameters which includes the use of thermal fluid distributions to account for localized effects. This work establishes a methodology for calculating PKE parameters for a pebble bed reactor with a coupled neutronics/thermal fluids analysis. PKE parameters generated on a global and local basis are compared against a diffusion solve for a typical load-following transient. Locally-generated neutron kinetic parameters are able to reduce the maximum error in the transient power level from 5% to below 1.5%; along with this, bulk temperature errors were reduced from 12 K to 4 K.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Reconstructing porous media using generative flow networks

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.

58 GEOSCIENCES↗
DOE OSTI2026

Generation of random geological models using multi-randomization for machine learning

Generating high-fidelity geological models is essential for advancing machine learning (ML) methods in automated seismic interpretation. For instance, seismic images paired with corresponding fault labels are foundational for ML-based fault detection from seismic migration sections. While several open-access datasets of random geological models exist, open-source tools specifically designed to produce large volumes of such models for ML applications remain scarce. To address this gap, we present RGM (Random Geological Model), an open-source software package for efficiently generating 2D and 3D synthetic geological models tailored for ML workflows. RGM supports the creation of diverse model components, including medium property distributions (P-/S-wave velocities and density), seismic reflectivity images (i.e., synthetic migration sections), relative geological time, and discrete fault attributes such as probability, dip, strike, rake, and displacement. It also accommodates the creation of complex geological features such as salt bodies and unconformities. The model generation algorithm employs a multi-randomization strategy, yielding an effectively infinite-dimensional model space that encompasses a wide range of geological scenarios and associated seismic features. Furthermore, RGM incorporates a method to generate synthetic elastic migration images using analytical elastic reflection coefficients combined with frequency-dependent scaling. This functionality enables the creation of training datasets for ML models that leverage elastic seismic images. RGM is implemented in modern object-oriented Fortran, allowing users to flexibly control statistical parameters governing model variability. We demonstrate the capability, performance, and geological realism of the package through comprehensive 2D and 3D examples.

58 GEOSCIENCES↗
DOE OSTI2020

Rapid generation of optimal generalized Monkhorst-Pack grids

In this report computational modeling of the properties of crystalline materials has become an increasingly important aspect of materials research, consuming hundreds of millions of CPU-hours at scientific computing centers around the world each year, if not more. A routine operation in such calculations is the evaluation of integrals over the Brillouin zone. We have previously demonstrated that performing such integrals using generalized Monkhorst-Pack k-point grids can roughly double the speed of these calculations relative to the widely-used traditional Monkhorst-Pack grids. However the generation of optimal generalized Monkhorst-Pack grids is not implemented in most software packages due to the computational cost and difficulty of identifying the best grids. To address this problem, we present new algorithms that allow rapid generation of optimal generalized Monkhorst-Pack grids on the fly. We demonstrate that the grids generated by these algorithms are on average significantly more efficient than those generated using existing algorithms across a range of grid densities. For grids that correspond to a real-space supercell with at least 50 Å between lattice points, which is sufficient to converge density functional theory calculations within 1 meV/atom for nearly all materials, our algorithm finds optimized grids in an average of 0.19 s on a single processing core. To facilitate the widespread adoption of this approach, we present new open-source tools including a library designed for integration with third-party software packages.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Kinetic and equilibrium reactions on natural and laboratory generation of thermogenic gases from Type II marine shale

The phenomenon that laboratory pyrolysis experiments produce much wetter gases than those in natural reservoirs is a long-recognized and debated problem in the investigation of natural gases in sedimentary basins. In this study, we explore the discrepancy by pyrolyzing a type II kerogen from the Woodford Shale in Oklahoma, compared with the previous results on the produced natural gases from the Arkoma Basin generated from the same source rock (Liu et al., 2019) with the discussion of gas and isotopic compositions at bulk and position-specific (PS) levels. An improved GC-pyrolysis-GC IRMS method is applied for the determination of PS δ 13 C of propane produced in the pyrolysis of the Woodford Shale at Easy %R o from 0.76 to 3.27. Kinetic and thermodynamic considerations of the chemical and isotopic compositions of the natural and laboratory pyrolysis gases suggest that the generation of light hydrocarbons involves uni-directional cracking reactions, exchange reactions with water, and likely reversible reactions among light hydrocarbons and other H-containing volatiles. After the gas generation in the unconventional Woodford Shale reservoirs, the C 1 -C 4 gases might have approached close to chemical equilibrium of C 1 -C 3 and isotope equilibrium of C 2 -C 1 and C 3 -C 1 pairs at their peak temperatures. The capping H for the generation of C 1 -C 4 in the Woodford Shale gases appears to have experienced at least partial exchange with the water, while that in the pyrolysis gases is only originated from organic-bound compounds with large kinetic isotope effects (KIE). Our findings indicate that elevated compound-specific and PS δ 13 C values of propane in the wet-gas cracking stage are significantly influenced by the breakdown of the thermally stable compounds (e.g., remaining kerogen, residues). A first synthesis of PS δ 13 C and δ 2 H isotopic compositions of propane from this study and the literature data suggests relatively similar isotopic structures of propane precursors in kerogens. Finally, this study demonstrates that PS isotope analysis of propane can contribute to identifying various geological (e.g., maturation, wet-gas cracking, H exchange, diffusion) and biodegradation processes.

58 GEOSCIENCES↗
DOE OSTI2020

The effect of lignin degradation products on the generation of pseudo-lignin during dilute acid pretreatment

Pseudo-lignin is an insoluble material that tends to deposit on acidic pretreated fiber surface and negatively impacts the biological conversion of biomass to valued added products. Employing a laboratory generated acidic hydrolysate solution from dilute acid pretreatment of bamboo residues, we sought to seek an improved understanding of pseudo-lignin formation from fragmented carbohydrates. In addition, we also performed experiments to investigate how lignin model compounds interact with carbohydrates en route to pseudo-lignin’s formation. The yields of pseudo-lignin generated from xylose (CXPL) were higher than those generated from glucose (CGPL). Gas chromatography-Mass spectrometry and Foloin-Ciocalteau analysis suggested that there were more aromatic compounds in hydrolyzate during CGPL’s formation. The presence of lignin phenolic model compounds had both a positive and negative effect upon quantitative yields of pseudo-lignin. Gel permeation chromatography and NMR analysis ( 31 P and 2D-HSQC NMR) of the pseudo-lignin samples generated in the presence and absence of 4-hydroxybenzoic acid (4-HA), employed as a lignin model compound, revealed drastic differences in molecular weight, changes in hydroxyl group content, and hydrocarbon bonds, suggesting significant differences in the pseudo-lignin reaction pathways when certain lignin model compounds are present. Finally, our findings demonstrate the effects of both hemicellulose carbohydrate makeup and lignin model compound impact on the formation of pseudo-lignin and its chemical properties, suggesting a potential way to minimize pseudo-lignin formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2022

Projected network performance for next-generation xenon monitoring systems

Next-generation radioxenon monitoring systems are reaching maturity and are expected to improve certain aspects of performance in verifying the absence of nuclear tests. To predict the improvement in detecting and locating nuclear releases, thousands of releases all over the globe were simulated and the global detection probability was calculated. This was done for the International Monitoring System network of noble gas samplers as it currently exists (25 certified stations), and how it would be for potential future network sizes of 39 and 79 stations. The probability of detection was calculated for releases ranging from 10 10 Bq to 10 16 Bq of 133 Xe and presented as coverage maps and global integrals for both current and next-generation monitoring systems. Similarly, the number of detecting stations and the number of detecting samples were tabulated to elucidate the possibilities for enhanced location capability. Improvements in global detection coverage are maximized at different release sizes in a way that depends on the station density. For example, for releases of 3×10 14 Bq and 39 stations, the detection probability would rise from 60% to 70% with next-generation systems, while for releases of 10 13 Bq and 79 stations, it would rise from 37% to 52%. Achieving an average of two detecting stations requires a 1015 Bq release for a 39-station network and a 10 14 Bq release for a 79-station network. The largest impact of using next-generation systems may be the confidence, failure tolerance, and location capability that arise from obtaining multiple samples associated with a single release event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Comparison of forward and inverse cam generation methods for the design of cam-linkage mechanisms

This paper presents a comparison between the forward and inverse cam generation methods applied to the design of a multi-lobed cam-rocker slider mechanism for function generation. In the forward method, the cam profile is specified directly and then the linkage output is determined using kinematics. In the inverse method, the trajectory of the output link is specified first and then the cam profile is determined using inverse linkage kinematics. First, the cam-linkage mechanism is introduced, and relevant kinematic equations are presented. Then, the cam generation methods are thoroughly explained. Here, the two cam generation methods are applied to a case study of a hydraulic motor using a multi-lobed cam and multiple linkage mechanisms. While the forward method has direct control of the cam shape, the inverse method has direct control of the linkage output and offers an advantage in calculation speed. For the mechanism selected, the cam designed with the inverse method achieved a reduction of at least 50.97% in the torque ripple when compared to the cam designed with the forward method.

42 ENGINEERING↗
DOE OSTI2021

Methane generation from low-maturity coals and shale source rocks at low temperatures (80–120 °C) over 14–38 months

This study contributes long-term (14 to 38 months) experimental evidence for geocatalytically mediated methanogenesis in immature to early mature shale and coal source rocks at temperatures from 80 to 120 °C. Borosilicate glass tubes with pre-outgassed coal and shale source rock chips and water were sealed under vacuum, sterilized, heated isothermally, and finally opened in connection with a vacuum line where headspace gases methane (CH 4 ) and carbon dioxide (CO 2 ) were collected. The possibility of admitting pre-existing CH 4 from closed pores into product gases during heating experiments was evaluated by comparative ball milling of original and heated rock chips, followed by quantitation of released gases with a novel closed-circuit laser-based SARAD RTM2200 gas detector system with sub-µmol sensitivity. The yields of produced CH 4 from individual source rocks were corrected by subtracting the amounts of pre-existing CH 4 from formerly closed pores in original source rocks that had leaked during long-term heating. Different shales and coals express contrasting propensities to geocatalytically generate CH 4 and CO 2 , with CH 4 yields ranging from 0.1 to 5.5 µmol g-1 total organic carbon (TOC). CH 4 yields from two petrographically different samples of Springfield Coal with comparable thermal maturity suggest that liptinite expresses a far higher propensity for methanogenesis, but liberates less CO 2 than vitrinite. Shale from the Second White Specks Formation generated approximately 10 times more CH 4 than New Albany Shale per g of TOC, further suggesting complex controls on CH 4 generation during catalytic methanogenesis. Higher temperature can enhance the activities of catalytic methanogenesis. Here, the extrapolation of laboratory-based reaction rates to natural conditions in organic-rich buried sediments suggests that geocatalytic methanogenesis can be fast enough in some source rocks to generate economically sizeable gas plays from immature to early mature source rocks over geologic time.

03 NATURAL GAS↗
DOE OSTI2024

Enhancing power grid resilience to winter storms via generator winterization with equity considerations

Here we develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. As a result, the successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

42 ENGINEERING↗
DOE OSTI2022

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Theory of Plasmonic Hot-Carrier Generation and Relaxation

Hot-carrier (HC) generation from (localized) surface plasmon decay has recently attracted much attention due to its promising applications in physical, chemical, materials, and energy science. However, the detailed mechanisms of plasmonic HC generation, relaxation, and trapping are less studied. In this work, we developed and applied a quantum-mechanical model and coupled master equation method to study the generation of HCs from plasmon decay and their following relaxation processes with different mechanisms treated on equal footing. First, a quantum-mechanical model for HC generation is developed. Its connection to existing semiclassical models and time-dependent density functional theory (TDDFT) is discussed. Second, the relaxation and lifetimes of HCs are investigated in the presence of electron–electron and electron–phonon interactions. A GW-like approximation is introduced to account for the electron–electron scattering. The numerical simulations on the Jellium nanoparticles with a size up to 1.6 nm demonstrate the electron–electron scattering and electron–phonon scattering dominate different time scale in the relaxation dynamics. We also generalize the model to study the extraction of HCs to attached molecules. The quantum yield of extracting HCs for other applications is found to be size-dependent. In general, the smaller size of NP improves the quantum yield, which is in agreement with recent experimental measurements. Even though we demonstrate this newly developed theoretical formalism with Jellium model, the theory applies to any other atomistic models.

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

Plasmonic Hot-Carrier Generation and Catalysis in Ti 3 C 2 O 2 from Real-Time TDDFT Simulations

Photoinduced hot electrons are central to plasmon-driven catalysis. Atomically thin Ti 3 C 2 O 2 , with high carrier density and broad optical absorption, offers a promising platform for plasmon-driven reactions. However, comprehensive investigations of its plasmon resonance, hot-carrier generation, and plasmonic catalytic performance remain limited. In this work, real-time time-dependent density functional theory (rt-TDDFT) was employed to study Ti 3 C 2 O 2 ’s plasmon excitation and hot-carrier generation from nonradiative plasmon damping. The temporal evolution of the dipole moment reveals plasmon resonance in Ti 3 C 2 O 2 , followed by strong plasmon damping that redistributes the stored energy to generate hot carriers. Ti 3 C 2 O 2 with low oxygen vacancy concentration (O v -Ti 3 C 2 O 2 ) exhibits plasmonic behavior resembling the pristine surface, and the plasmon-generated hot electrons can markedly reduce the dissociation barrier of CO 2 at the oxygen vacancy. These findings provide fundamental insights into the plasmonic properties of Ti 3 C 2 O 2 and how they drive its catalytic performance in surface reactions, which is valuable for advancing plasmon-driven catalysis.

CO2 reduction↗
DOE OSTI2021

Simultaneous Energy Generation and Flow Enhancement (Electroslippage Effect) in Polyelectrolyte Brush Functionalized Nanochannels

Energy generation through nanofluidics is a topic of great nanotechnological relevance. In this work, we conduct all-atom molecular dynamics (MD) simulations of the transport of water and ions in a pressure-driven flow in nanochannels grafted with charged polyelectrolyte (PE) brushes and discover the possibility of simultaneous electrokinetic energy generation and flow enhancement (henceforth denoted as the electroslippage effect). Such PE-brush-functionalized nanochannels have been recently shown to demonstrate an overscreening (OS) effect (characterized by the presence of a greater number of screening counterions within the PE brush layer than needed to screen the PE brush charges), a consequent presence of excess coions within the PE brush-free bulk, and a coion-driven electroosmotic (EOS) transport in the presence of small to moderate applied axial electric fields. Here, however, we find that the streaming current, which represents the current generated by the flow-driven downstream advection of the charge imbalance present within the electric double layer (EDL) that screen the PE brush charges, is governed by the migration of the counterions. This stems from the fact that the highest contribution to the overall streaming current arises from the region near the PE brush-water interface (where there is an excess of counterions), while the brush-free bulk yields a hitherto unreported, but small, coion-dictated streaming current. This downstream advection of the charge imbalance (and the resultant counterion-driven streaming current) eventually leads to the development of an electric field (streaming electric field) in the direction that is opposite to the direction of the counterion-driven streaming current. The streaming current and the streaming electric field interact to generate the electrokinetic energy. Equally importantly, this streaming electric field induces an EOS transport, which becomes coion-driven, due to the presence of excess coions in the brush-free bulk. For the case of nanochannels grafted with negatively charged PE brushes, the streaming electric field will be in a direction that is opposite to the pressure-driven transport, and hence the coion (or anion) driven EOS flow, will be in the same direction as the pressure-driven transport. On the other hand, for the case of nanochannels grafted with positively charged PE brushes, the streaming electric field will be in the same direction as the pressure-driven flow, and hence the coion (or cation) driven EOS flow, will again be in the same direction as the pressure-driven flow. Therefore, whenever there occurs the presence of the OS and the resulting coion-driven EOS transport in PE brush grafted nanochannels, regardless of the sign of the charges of the PE brushes, this EOS transport will always aid the pressure-driven transport and will cause the most fascinating increase in the net volume flow rate across the nanochannel cross section, which is the electroslippage effect.

overscreening↗
DOE OSTI2025

Advanced Microstructured BaTiO 3 -Embedded PVDF–HFP/PEO Film for Enhanced Triboelectric Interface in Self-Sufficient Energy Generation and Sensing

The global reliance on fossil fuels and natural gas has largely dominated the energy production field, but due to finite resource depletion and escalating greenhouse gas emissions, the immediate exploration of sustainable energy alternatives to mitigate climate change and ensure resource security has been a major concern. There has been extensive research into other more renewable methods of energy production, such as wind and hydropower. Of these current energy generation types, there are many areas of untapped potential from the mechanical movements generated ambiently not only in the large scale of power generation but also on a smaller scale. The piezoelectric and triboelectric effects are phenomena where these ambient mechanical movements can generate electrical energy. Developing a hybrid system that leverages both mechanical stress and surface charges presents an ideal opportunity to exploit these untapped energy sources. Producing a hybrid PVDF–HFP/PEO film with perovskite BaTiO 3 (BTO) enables ambient power harvesting from both mechanical movement and surface charge. The optimized cell produced a potential of up to 15 V and a current of 200 nA with a 68 kΩ resistor, a substantial increase from a base system with an average of 2.1 V and 40 nA. These hybrid TENGs offer significant potential for energy harvesting in small-scale applications, such as health monitoring devices and indicators in electric circuits.

Ybarra, Rigobert [University of Texas Rio Grande V↗