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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Which Combinations of Environmental Conditions and Microphysical Parameter Values Produce a Given Orographic Precipitation Distribution?

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θ sfc ), and the snow fall speed coefficient (A s ). RH, U, and A s exhibit interdependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial subregions and given different environmental conditions. In particular, high θ sfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

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

Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering

We combine deep Gaussian processes (DGPs) with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian processes (GPs) with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems. In this article, we combine DGPs with multitask and transfer learning to allow for both an improved tuning of an application parameters on problems of interest but also the prediction of parameters on any potential problem the application might encounter.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Broadband spectral combining of three pulse-shaped fiber amplifiers with 42fs compressed pulse duration

We demonstrate ultra-broadband spectral combining of ultrashort pulses from Yb-doped fiber amplifiers, with coherently spectrally synthesized pulse shaping, to achieve tens-of-fs pulses. This method can fully compensate for gain narrowing and high order dispersion over broad bandwidth. We produce 42fs pulses by spectrally synthesizing three chirped-pulse fiber amplifiers and two programmable pulse shapers across an 80nm overall bandwidth. To the best of our knowledge, this is the shortest pulse duration achieved from a spectrally combined fiber system at one-micron wavelength. This work provides a path toward high-energy, tens-of-fs fiber chirped-pulse amplification systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Numerical Modeling Experiments of Combined Effects of Ice and Permafrost on Delta Morphodynamics

This dataset contains output from numerical model experiments to test the combined effects of ice thickness and permafrost erodibility on delta dynamics. All experiments were conducted using DeltaRCM, a reduced complexity cellular model. Details of the model setup and experiments are described in full in Piliouras et al., (in revision). This dataset contains results from a total of 48 experiments, representing 16 different experimental conditions, each of which was run in triplicate. There is a control run (3 total experiments) with no ice and no permafrost. We used permafrost erodibilities (here described as erodibility relative to that of non-permafrost, and therefore < 1) of 0.65, 0.8, and 0.95, where 0.95 represents permafrost being only 5% less erodible than non-permafrost. We used ice thicknesses of 0.5 m, 3 m, and 4.5 m in a 5 m deep basin. We conducted experiments for each possible combination of permafrost erodibility and ice thickness (9 conditions, 27 experiments). We also provide results for experiments with only ice or permafrost (6 conditions, 18 experiments). The excel table (csv file) provides the ice and permafrost conditions for each run ID. Zip files contain model outputs and other variables generated during analysis (see ReadMe file) as numpy arrays (.npy files), which can be accessed using python.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate

In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest (QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate r. We characterized two input data uncertainty sources from the experiment (the distortion from the camera U c and the non-zero-angle fuel placement U Y ), the prediction and model form uncertainty from the deep neural network (U m ), as well as the variability from the manually segmented images used for training it (U s ). Here, we conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.

42 ENGINEERING↗
DOE OSTI2020

Enhancing Heavy-Oil-Recovery Efficiency by Combining Low-Salinity-Water and Polymer Flooding

Summary Combining low-salinity-water (LSW) and polymer flooding was proposed to unlock the tremendous heavy-oil resources on the Alaska North Slope (ANS). The synergy of LSW and polymer flooding was demonstrated through coreflooding experiments at various conditions. The results indicate that the high-salinity polymer (HSP) (salinity = 27,500 ppm) requires nearly two-thirds more polymer than the low-salinity polymer (LSP) (salinity = 2,500 ppm) to achieve the target viscosity at the condition of this study. Additional oil was recovered from LSW flooding after extensive high-salinity-water (HSW) flooding [3 to 9% of original oil in place (OOIP)]. LSW flooding performed in secondary mode achieved higher recovery than that in tertiary mode. Also, the occurrence of water breakthrough can be delayed in the LSW flooding compared with the HSW flooding. Strikingly, after extensive LSW flooding and HSP flooding, incremental oil recovery (approximately 8% of OOIP) was still achieved by LSP flooding with the same viscosity as the HSP. The pH increase of the effluent during LSW/LSP flooding was significantly greater than that during HSW/HSP flooding, indicating the presence of the low-salinity effect (LSE). The residual-oil-saturation (Sor) reduction induced by the LSE in the area unswept during the LSW flooding (mainly smaller pores) would contribute to the increased oil recovery. LSP flooding performed directly after waterflooding recovered more incremental oil (approximately 10% of OOIP) compared with HSP flooding performed in the same scheme. Apart from the improved sweep efficiency by polymer, the low-salinity-induced Sor reduction also would contribute to the increased oil recovery by the LSP. A nearly 2-year pilot test in the Milne Point Field on the ANS has shown impressive success of the proposed hybrid enhanced-oil-recovery (EOR) process: water-cut reduction (70 to less than 15%), increasing oil rate, and no polymer breakthrough so far. This work has demonstrated the remarkable economical and technical benefits of combining LSW and polymer flooding in enhancing heavy-oil recovery.

Engineering↗
DOE OSTI2020

Turbine Aero-Thermal Technologies for 65% Combined Cycle Efficiency (Phase I Final Report)

GE Power (GEP), a division of General Electric Company, performed a gas turbine technology development program that integrated Aerodynamic and Heat Transfer advances as an important and innovative step towards the DOE goal of 65% combined cycle efficiency. Improved efficiency will also contribute towards the reduction in the cost of electricity identified by the DOE for the US through reduced fuel consumption, and through lowering the capital cost of the generating equipment. It is currently predicted that the result of this project will enable 0.40~0.65 pt in combined cycle efficiency improvement. This technology will be directly applicable to gas turbines utilizing coal gasification processes where hydrogen-rich coal-derived synthesis gas is produced with natural gas being the developmental fuel.

03 NATURAL GAS↗
DOE OSTI2020

Hybrid Ceramic-CMC Vane with EBC for Future Coal Derived Syngas Fired 65% Efficient Turbine Combined Cycle

The efficiency of both simple cycle and combined cycle power generation systems scale with the peak temperature at which the gas exits the combustor to drive the turbine. In conventional systems, a substantial fraction of the total turbine core flow exiting the compressor is diverted downstream to cool metallic turbine hardware rather than power the turbine, much of which is used to cool the first-stage turbine vane. The use of coal derived syngas fuels provides an additional challenge to the lifetime of materials utilized in the turbine, as particulate byproducts created in the coal gasification process melt in the combustion gas, and can subsequently deposit and interact with the turbine hardware. The development of durable hot-section materials capable of operating at temperature well above that of single crystal superalloy airfoil/zirconia based thermal barrier coatings is critical to realizing 65% efficient coal derived syngas fired gas turbine based power systems. To enable higher turbine inlet temperatures while lowering cooling air requirements, United Technologies Research Center (UTRC), the central R&D laboratory supporting UT Pratt & Whitney, led the conceptual design of a new type of ceramic composite turbine hot section materials system. The design focused on a novel hybrid monolithic ceramic-fiber reinforced ceramic matrix composite (CMC) first stage turbine vane having an environmental barrier coating. By utilizing ceramic construction in the turbine hot-section, the core flow normally used to cool metallic components will be substantially reduced, increasing efficiency and reducing emissions. To provide the framework for future demonstration testing, UTRC partnered with University of North Dakota Energy and Environmental Research Center (UNDEERC) to provide a conceptual design for a gasified coal fed high-pressure turbine combustor system designed to mimic the conditions expected in a future 65% fuel to busbar efficient syngas fueled gas turbine based combined cycle. The UNDEERC and UTRC collaborated on characterizing dusts from coal gasifier filtration systems.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Combining artificial intelligence, Earth observations, and climate models to improve predictability of ice-biogeochemistry interactions

Focal Area: Predictive Modeling. We describe how artificial intelligence (AI) can be combined with state-of-the-science Earth system models to better predict future regional climate responses. To demonstrate, we describe a case study in biogeochemical interactions with sea ice. Science Challenge: Biogeochemical models are poorly constrained for high latitude systems. Machine learning methods and edge computing can be combined with Earth system models, such as the Energy Exascale Earth System Model (E3SM), to gain insight into ice-biogeochemical interactions and improve sea ice extent prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Combining information from multiple cosmological surveys: inference and modeling challenges

The tightest and most robust cosmological results of the next decade will be achieved by bringing together multiple surveys of the Universe. This endeavor has to happen across multiple layers of the data processing and analysis, e.g., enhancements are expected from combining Euclid, Rubin, and Roman (as well as other surveys) not only at the level of joint processing and catalog combination, but also during the post-catalog parts of the analysis such as the cosmological inference process. While every experiment builds their own analysis and inference framework and creates their own set of simulations, cross-survey work that homogenizes these efforts, exchanges information from numerical simulations, and coordinates details in the modeling of astrophysical and observational systematics of the corresponding datasets is crucial.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A combined biological and chemical flue gas utilization system towards carbon dioxide capture from coal-fired power plants (Final Report)

Photosynthetic algal cultivation has been intensively studied for CO 2 capture and utilization for several decades. The footprint for using algae to capture CO 2 emitted from carbon-intensive industrial processes (power plants, cement plants, and fermentation processes) is extremely large, which creates serious technical and economic hurdles that must be cleared if algal technologies are to be commercially implemented. Fortunately, algal biomass is rich in proteins, carbohydrates, and lipids, providing a good chemical source for organic absorbents and other value-added chemical feedstocks. In particular, amino acids from algal protein can be used to generate amino acid salt solutions that have been proven to be effective for capturing CO 2 . In order to take advantage of both algal cultivation and biomass utilization, the goal of the proposed project is to develop a combined biological and chemical system for coal-fired power plants for sequestering CO 2 in biological absorbents and generating value-added products. This approach significantly reduces the land and energy footprint of CO 2 capture, and minimizes capital and operational expenses. Three specific objectives are targeted: 1) optimizing the growth of the selected algal strain to maximize biomass accumulation from the coal-fired flue gas; 2) developing a cascade biomass utilization to produce amino acid absorbents, polyurethanes, biodiesel, and methane; and 3) conducting techno-economic analysis (TEA) and life cycle assessment (LCA) of the proposed process. Three key technical outcomes were achieved: (1) With the selected robust algal strain and unique photobioreactor design, long-term culture stability can be extended, and algal biomass productivity reached 0.5 g dry biomass/L/day year-round at a biomass concentration of 1.2 g/L in the pilot photobioreactor; (2) The biomass utilization process led to complete utilization of the algal biomass to produce amino acid salt absorbent, polyurethanes, and methane; and (3) The combined biological and chemical flue gas utilization process concluded a technically and economically feasible commercial-scale system that completely captures CO 2 in coal-fired flue gas with greatly reduced energy consumption.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Proof that Combining the Forced-collision and DXTRAN Monte Carlo Variance-reduction Techniques is Fair

This report provides mathematical proof that the MCNP DXTRAN (also known in other codes as forced-flight) and forced-collision variance-reduction techniques do not bias the expected value of Monte Carlo simulation estimates when combined. Proof is also provided that the techniques are unbiased when used independently. To prove that the techniques are unbiased, this report derives the first History Score Moment Equation (HSME) for non-multiplying media employing only forced collisions, only DXTRAN, and then both the variance-reduction techniques combined, as defined next. The HSMEs are found by forming the History Score Probability Density Functions (HSPDFs) and then taking the first score moment. Following its derivation, each HSME with a variance-reduction technique employed is reduced to the HSME for an analog simulation. Because the HSME represents the expected contribution to estimators in the simulation, reducing the HSME with variance reduction to the analog HSME shows that the simulation is unbiased despite the variance-reduction technique considered in the HSME. Analysis regarding higher-score moments of each technique is the subject of prior work and is not addressed herein. Throughout this work, the phase space p is defined to be the particle position x, direction-of-flight unit vector $\hat{Ω}$, energy E, and statistical weight w, $$p ≡ (x; \hat{Ω}; E; w).$$ A reduced phase-space excluding the statistical weight of the particle, $$r ≡ (x; \hat{Ω}; E),$$ is also used.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Verification of Combined VR Techniques, Derivation of Future Time Equation, and Integration of LLNL Pulsed Sphere V&V Suite [Slides]

To determine whether the combination of forced-collision and DXTRAN variance-reduction (VR) techniques is unbiased, and to gain insight into the operation of these techniques, proof that first-moment estimates from Monte Carlo simulations employing both techniques are unbiased is developed. A general background on the forced-collision and DXTRAN VR techniques and their combination is given. Proof of an unbiased simulation is outlined by showing the equivalence of the history score moment equations of simulations with these techniques in use. A report with detailed proof of this equivalence is available upon request. The derivation of the future time equation using a similar approach, as well as a summary of the addition of the LLNL Pulsed Sphere experiments to the MCNP verification and validation suite, is also briefly discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational Fluid Dynamics Modeling to Simulate a Combined Reforming Process for Syngas and Hydrogen Production

An Oxygen Transport Membrane (OTM) combined reforming technology for producing syngas and hydrogen integrates the advantages of multiple processes—steam methane reforming (SMR), autothermal reforming (ATR), an air separation unit (ASU)—into a single integrated technology. The OTM consists of a primary reforming tube, in which desulfurized natural gas is partially reformed by steam at high pressure in the presence of a metal catalyst. This process is followed in series by a ceramic OTM with a secondary reformer, in which residual methane reforms and O 2 - ions react with a portion of the CO and H 2 fuel to provide the heat to support both primary and secondary reforming. Although the OTM combined reformer technology for syngas and H 2 production has been substantially developed in the last decade, several challenges that affect the overall production efficiency and reliability are yet to be fully understood, addressed, and resolved. Therefore, developing Computational Fluid Dynamics (CFD) models that incorporate fluid dynamics, mass transport, kinetics, heat transport, and structural mechanics is critical to understanding and minimizing the probability of tube failures during the startup and operation. In this report, an exhaustive literature review was performed to survey the current state of technology for producing syngas and H 2 using either conventional or renewable energy sources. The feedstocks reviewed include natural gas and coal for the conventional technologies, whereas biomass, solar, wind, and nuclear energy for the renewable technologies. The existing industry-grade COMSOL multiphysics models of OTM were upgraded for the latest software release. In addition, they were improved to help achieve grid and solver independence and were successfully ported on the ORNL high-performance computing clusters to speed up their run times. A 42% reduction in the simulation run time was achieved. A new higher-fidelity CFD model of an OTM tube was developed in the StarCCM+ simulation platform. This new model was designed to simulate various physics using first principles, e.g., turbulent flow, heat transfer, and chemical reactions while avoiding unnecessary simplifications. The resulting predictions were qualitatively assessed and provided useful insights into the multiphysics complexity of an OTM tube.

08 HYDROGEN↗
DOE OSTI2022

Towards a combination of LHC and TeVatron W-boson mass measurements

In this note methodological and modelling considerations towards a combination of the ATLAS, CDF and D0 measurements of the W -boson mass are discussed. As they were performed at different moments in time, each measurement employed different assumptions for the modelling of W -boson production and decay, as well as different fits of the parton distribution functions of the proton (PDFs). Methods are presented to accurately evaluate the effect of PDFs and other modelling variations on existing measurements, allowing to extrapolate them to any PDF set and to evaluate the corresponding uncertainties. Based on this approach, the measurements can be corrected to a common modelling reference and to the same PDFs, and subsequently combined accounting for PDF correlations in a quantitative way.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Identification of Material Type and Thickness Using Combined X-ray and Fast Neutron Radiography

Material identification (ID) using radiography is a problem that has implicitly existed since the discovery of the X-ray. Since 9-11, this problem has received renewed attention for homeland security applications of the generic, “what is in the box?” type1. Examples of items potentially in the box include: roadside bombs, nuclear weapons, contraband shipments and the like. In these cases, the “what” can include: explosives, projectiles, fissile material, or drugs. Here we investigate the ability of 60Co gamma-rays, 14MeV neutrons, and their combination to tease apart information about density, thickness and atomic number of items in the box. That information subset can be combined with other types of independent information (e.g. neutron and gammaspectroscopy, intelligence, photographs etc.) to assert a more complete picture of what, exactly, is in the box? That more complete information set can form the basis for actions including: remote detonation, evacuations, disablement, detention, search, arrest etc.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space (Final Report)

This is the final report for the project "Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space". The central aim of this project was to develop modeling tools that vastly increase the diversity of molecules for which reliable adsorption isotherms in nanoporous materials are available. Within the project, molecular simulation data for a diverse set of examples was combined with machine-learning (ML) methods to rapidly predict adsorption equilibria in crystalline nanoporous materials (primarily MOFs).

36 MATERIALS SCIENCE↗