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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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The agency’s Strategic Integrated Road Map illustrates the path to achieving goals now and into the future. NNSA’s mission priorities include maintaining the stockpile, countering proliferation threats, keeping the nuclear Navy moving, applying world-class scientific and technological discoveries to real-world problems, and modernizing the infrastructure of the enterprise.
The Saltstone facility at Savannah River Site (SRS) has a grout hopper tank to provide agitator stirring of the Saltstone feed materials. The tank has about 300 gallon capacity to provide a larger working volume for the grout nuclear waste slurry to be held in case of a process upset, and it is equipped with a mechanical agitator, which is intended to keep the grout in motion and agitated so that it won't start to set up. The primary objective of the work was to evaluate the flow performance for mechanical agitators to keep an adequate stirring of the feed materials and to estimate an agitator speed which provides acceptable flow performance with a 45 deg. pitched four-blade agitator. In addition, the power consumption required for the agitator operation was estimated. The modeling calculations were performed by taking two steps of the Computational Fluid Dynamics (CFD) modeling approach. A series of sensitivity calculations for different designs of agitators and operating conditions have been performed to investigate the impact of key parameters on the grout hydraulic performance in a 300-gallon hopper tank. Recommended operational guidance was developed by using the basic concept that local shear rates and flow patterns can be used as a measure of hydraulic performance and spatial stirring. Flow patterns and fluid residence time were estimated by a Lagrangian integration technique along the flow paths from the material feed inlet. (authors)
Abstract The integration of a high-performance core and a dissipative divertor, or the so-called ‘core–edge integration,’ has been widely identified as a critical gap in the design of future fusion reactors. In this letter, we report, for the first time, direct experimental evidence of electron turbulence at the DIII-D H-mode pedestal that correlates with the broadening of the pedestal and thus facilitates core–edge integration. In agreement with gyrokinetic simulations, this electron turbulence is enhanced by high η e ( η e = L n / L T e , where L n is the density scale length and L T e is the electron temperature scale length), which is due to a strong shift between the density and temperature pedestal profiles associated with a closed divertor. The modeled turbulence drives significant heat transport with a lower pressure gradient that may broaden the pedestal to a greater degree than the empirical and theoretically predicted pedestal width scalings. Such a wide pedestal, coupled with a closed divertor, enables us to achieve a good core–edge scenario that integrates a high-temperature low-collisionality pedestal (pedestal top temperature T e,ped > 0.8 keV and a pedestal top collisionality ν * ped < 1) under detached divertor conditions. This paves a new path toward solving the core–edge integration issue in future fusion reactors.
The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source to receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.
Hydrogen (H2) has been identified as a leading sustainable contender to replace fossil fuels in transportation and electricity generation. H2 production can be achieved by concentrating solar thermal power (CSP) systems collecting thermal energy from the sun to various chemical processes for fuel production. Fuel production via solar thermal chemical processes integrated with CSP uses the full spectrum of sunlight compared with photovoltaic power conversion and stores solar energy directly and efficiently [1]. The solar fuel production can be realized by thermochemical processes (e.g., water splitting for H2 production, carbon dioxide reduction, or methane reforming) or thermal electrochemical methods (e.g., integration with solid oxide electrolysis cell). Technology development for CSP-integrated solar fuel production requires broad technological bases from solar energy collection to chemical energy conversion. H2 generated from renewable sources can be an energy carrier for a carbon-free economy. Integrating CSP with high temperature electrolysis (HTE) using solid oxide electrolysis cells (SOEC) provides a renewable path for H2 generation. The CSP-HTE integration approach provides the benefit of thermal energy storage (TES) for continuous operation, improved capacity, and SOEC life. H2 gas has low energy density for transportation, pipeline networks are expensive, and H2 liquefaction is energy intensive. However, an alternative method for H2 distribution is to use carbon dioxide (CO2) capture and liquid hydrocarbon synthesis to convert solar energy into liquid fuels that are compatible with the existing fossil fuel infrastructure.
A tunable diode laser absorption spectroscopy-based methane sensor, employing a dense-pattern multi-pass gas cell and a 3.3 µm, CW, DFB, room temperature interband cascade laser (ICL), is reported. The optical integration based on an advanced folded optical path design and an efficient ICL control system with appropriate electrical power management resulted in a CH 4 sensor with a small footprint (32 x 20 x 17 cm 3 ) and low-power consumption (6 W). Polynomial and least-squares fit algorithms are employed to remove the baseline of the spectral scan and retrieve CH 4 concentrations, respectively. An Allan-Werle deviation analysis shows that the measurement precision can reach 1.4 ppb for a 60 s averaging time. Continuous measurements covering a seven-day period were performed to demonstrate the stability and robustness of the reported CH 4 sensor system.
Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.
The presentation was given at the November 21-23, 2018 'CEM Days - Integration of Renewables in the Electric Sector: Paths and Challenges to Energy Planning' in Rio de Janeiro, Brazil. It provides a summary of NREL's current modeling approaches, datasets, and considerations for integrating renewables into the power system. It also covers current model method improvement efforts, including our vision for a truly integrated modeling approach.
The report documents the conceptual basis for a new potential Combined Algal Processing design strategy which may allow more flexibility in accommodating different algal biomass feedstock compositions, by enabling upgrading of both protein and carbohydrates in a single step, without a strict requirement for either component to be in soluble or monomeric form, while maintaining effective wet lipid extraction techniques to enable high lipid recoveries. In light of previously-established constraints around algal biomass costs (which are significantly higher than lignocellulosic terrestrial biomass), the present CAP processing strategy reflects an integrated biorefinery concept producing both fuels and value-added chemical coproducts as a means to improve profitability and generate coproduct revenues to help drive down the minimum fuel selling price (MFSP) towards economically viable levels. Namely, this report highlights an integrated CAP biorefinery process and associated technical targets that would be required to achieve U.S. Department of Energy target MFSP goals of $2.5/gallon gasoline equivalent by 2030. This is accomplished by a process involving low-cost seasonal storage of algal biomass during high-growth seasons, rapid flash hydrolysis pretreatment of the biomass, solvent extraction of pretreated biomass, cleanup and fractionation of lipids into triglyceride and free fatty acid fractions, and a series of thermochemical conversion steps to upgrade carbohydrates and protein to hydrocarbon fuels. These steps include mild oxidative treatment (MOT), a process originally investigated at NREL for upgrading lignin, followed by catalytic ketonization and hydrotreating of MOT products to fuels. Isolated triglycerides are sent to a coproduct train, with the base case focused on upgrading to polyurethane foams as a high-value, high-market-volume coproduct.
We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method’s precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE’s encoder and decoder networks capture the localization process and the source’s influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbor approach based on the closest latent representation of a source. Validation with pencil-lead-break tests on a metallic pipe confirmed our method’s effectiveness, achieving an averaged 3D localization accuracy of 0.84.
This work describes the integrated experimental and modeling effort at PNNL to enhance the room-temperature formability of aluminum alloys by taking advantage of formability improvements generally associated with high-strain-rate forming. Al alloy AA5182-O sheets were deformed in near plane-strain conditions at strain-rates exceeding 1000 /s using the electrohydraulic forming (EHF) technique, and at quasi-static strain-rates via a bulge test. A novel capability, combining high-speed imaging with digital image correlation technique, was developed to quantify the deformation history during high-rate forming. Sheet deformation under high rates was modeled in Abaqus and validated with experimentally determined deformation data. The experimental results show a ~2.5x increase in formability at high rates, relative to quasi-static rates, under a proportional loading path that was verified by the experimental data. The model shows good correlation with the experimentally determined strain path. It is anticipated that such integrated experimental and modeling work will enable room-temperature forming of Al and industrial implementation of high-rate forming processes.
The design, fabrication and performance of InGaAs and InGaP/GaAs microcells are presented. These cells are integrated with a Si wafer providing a path for insertion in hybrid concentrated photovoltaic modules. Comparisons are made between bonded cells and cells fabricated on their native wafer. The bonded cells showed no evidence of degradation in spite of the integration process which involved significant processing including the removal of the III-V substrate. Results from a number of hybrid cell configurations were reported. These cells employed integration techniques including wafer level bonding of processed cells and solder bonding of the cells. Lastly, the cells themselves showed evidence of degradation in spite of the integration process, which involved significant processing including the removal of the III-V substrate.
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CO 2 capture, utilization, and storage (CCUS) technology has yet to be widely deployed at a commercial scale despite multiple high-profile demonstration projects. We suggest that developing a large-scale, visible, and financially viable CCUS network could potentially overcome many barriers to deployment and jumpstart commercial-scale CCUS. To date, substantial effort has focused on technology development to reduce the costs of CO 2 capture from coal-fired power plants. Here, we propose that near-term investment could focus on implementing CO 2 capture on facilities that produce high-value chemicals/products. These facilities can absorb the expected impact of the marginal increase in the cost of production on the price of their product, due to the addition of CO 2 capture, more than coal-fired power plants. A financially viable demonstration of a large-scale CCUS network requires offsetting the costs of CO 2 capture by using the CO 2 as an input to the production of market-viable products. As a result, we demonstrate this alternative development path with the example of an integrated CCUS system where CO 2 is captured from ethylene producers and used for enhanced oil recovery in the U.S. Gulf Coast region.
Here, in this work, the solid–liquid coexistence curves of classical fully flexible atomistic models of α-RDX and β-HMX were calculated using thermodynamically rigorous methodologies that identify where the free energy difference between the phases is zero. The free energy difference between each phase at a given state point was computed using the pseudosupercritical path (PSCP) method, and Gibbs–Helmholtz integration was used to evaluate the solid–liquid free energy difference as a function of temperature. This procedure was repeated for several pressures to determine points along the coexistence curve, which were then fit to the Simon–Glatzel functional form. While effective, this method is computationally expensive. An alternative approach is to compute the melting point at a single pressure via the PSCP method, and then use the Gibbs–Duhem integration technique to trace out the coexistence curve in a more computationally economical manner. Both approaches were used to determine the coexistence curve of α-RDX. The Gibbs–Duhem integration method was shown to generate a melt curve that is in good agreement with the PSCP-derived melt curve, while only costing ~10 % of the computational resources used for the PSCP method. For α-RDX, the predicted melting temperature increases significantly more for a given increase in pressure when compared to available experimental data.
Phosphoproteomics greatly augments proteomics and holds tremendous potential for insights into the modulation of biological systems for various disease states, etc. However, numerous challenges hinder conventional methods in terms of measurement sensitivity, throughput, and capabilities for confident phosphopeptide and phosphosite identification. In this work, we report the first example of integrating structures for lossless ion manipulations ion mobility (SLIM) IM-MS with online reversed-phase liquid chromatography (LC) to evaluate its potential for addressing the aforementioned challenges. A mixture of 51 heavy-labeled phosphopeptides was analyzed with a SLIM IM module having integrated ion accumulation and long-path separation regions. The SLIM IM-MS provided improved sensitivity over an established LC-SRM method, with limits of detection as low as 50 pM (50 amol/µl) for several peptides. Conventionally problematic phosphopeptide isomers could be resolved following an 18 m SLIM IM separation. The 2-D peak capacity was estimated as ~9000 for 90 min LC separation coupled to an 18 m SLIM IM separation, considerably higher than LC alone and providing a basis for both improved identification and quantification, with additional gains projected with the future use of longer path SLIM IM separations. Thus, LC-SLIM IM-MS offers great potential for improving the sensitivity, separation, and throughput of phosphoproteomics analyses.
We report that versatile polyelectrolytes with tunable physical properties have the potential to be transformative in applications such as energy storage, fuel cells and various electronic devices. Among the types of materials available for these applications, nanostructured cationic block copolyelectrolytes offer mechanical integrity and well-defined conducting paths for ionic transport. To date, most cationic polyelectrolytes bear charge formally localized on heteroatoms and lack broad modularity to tune their physical properties. To overcome these challenges, we describe herein the development of a new class of functional polyelectrolytes based on the aromatic cyclopropenium ion.We demonstrate the facile synthesis of a series of polymers and nanoparticles based on monomeric cyclopropenium building blocks incorporating various functional groups that affect physical properties. In conclusion, the materials exhibit high ionic conductivity and thermal stability due to the nature of the cationic moieties, thus rendering this class of new materials as an attractive alternative to develop ion-conducting membranes.
Two-dimensional heterostructures combined with vertical geometries are candidates to probe and utilize the physical properties of atomically-thin materials. The vertical configuration enables a unique form of hot-carrier spectroscopy as well as atomic-scale devices. Here, we present the room-temperature evolution of heteroepitaxial perovskite hot-electron transistors using a SrRuO 3 base down to the monolayer limit ( ~ 4 Å). As a fundamental electronic probe, we observe an abrupt transition in the hot-electron mean free path as a function of base thickness, coinciding with the thickness-dependent resistive transition. As a path towards devices, we demonstrate the integrated synthesis of perovskite one-dimensional electrical edge contacts using water-soluble and growth-compatible Sr 3 Al 2 O 6 hard masks. Edge-contacted monolayer-base transistors exhibit on/off ratios reaching ~ 10 8 , complete electrostatic screening by the base manifesting pure hot-electron injection, and excellent scaling of the output current density with device dimensions. These results open new avenues for incorporating emergent phenomena at oxide interfaces and in heterostructures.