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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Structural modeling and dynamic analysis of condensate storage tanks in nuclear power plants

Condensate storage tanks (CSTs) in nuclear power plants (NPPs) are classified as critical equipment capable of surviving strong shaking in a design basis earthquake to assure the ability to subsequently provide cooling water. Structural modeling and dynamic analysis of CSTs are complicated due to fluid-structure interaction (FSI) and coupling issues, which could have significant effects on seismic response. The capability of a number of approximate models to capture seismic response is investigated using dynamic analysis results from detailed finite element models. Modal analyses and time history analyses are carried out using both 2D and 3D models to predict the dynamic behavior of CSTs as a function of ground motion intensity. Although 2D simplified models can be used to quickly evaluate the dynamic response of CSTs when there is substantial margin to failure, a detailed 3D model is required when it is necessary to examine a limit state associated with the failure mode of the tank. The findings show good agreement between the natural frequencies of convective modes determined from the 2D and 3D CST models. The difference between the frequencies of impulsive modes were found to be equal to or less than 25%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Study of alkaline carbonate cooling to mitigate Ex-Vessel molten corium accidents

To mitigate adverse effects from molten corium following a reactor pressure vessel failure (RPVF), some new reactor designs employ a core catcher and a sacrificial material (SM), such as ceramic or concrete, to stabilize the molten corium and avoid containment breach. Existing reactors cannot easily be modified to include these SMs but could be modified to allow injectable cooling materials. Current reactor designs are limited to using water to stabilize the corium, but this can create other issues such as reaction of water with the concrete forming hydrogen gas. Here the novel SM proposed is a granular carbonate mineral that can be used in existing light water reactor plants. The granular carbonate will decompose when exposed to heat, inducing an endothermic reaction to quickly solidify the corium in place and producing a mineral oxide and carbon dioxide. Corium spreading is a complex process strongly influenced by coupled chemical reactions, including decay heat from the corium, phase change, and reactions between the concrete containment and available water. A recently completed Sandia National Laboratories laboratory directed research and development (LDRD) project focused on two research areas: experiments to demonstrate the feasibility of the novel SM concept, and modeling activities to determine the potential applications of the concept to actual nuclear plants. Small-scale experiments using lead oxide (PbO) as a surrogate for molten corium demonstrate that the reaction of the SM with molten PbO results in a fast solidification of the melt due to the endothermic carbonate decomposition reaction and the formation of open pore structures in the solidified PbO from CO 2 released during the decomposition. A simplified carbonate decomposition model was developed to predict thermal decomposition of carbonate mineral in contact with corium. This model was incorporated into MELCOR, a severe accident nuclear reactor code. A full-plant MELCOR simulation suggests that by the introduction of SM to the reactor cavity prior to RPVF ex-vessel accident progression, e.g., core-concrete interaction and core spreading on the containment floor, could be delayed by at least 15 h; this may be enough for additional accident management to be implemented to alleviate the situation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Metal fuel relocation experiments with pressure injection

A pool-type sodium-cooled fast reactor (SFR) using metal fuels has a number of inherent safety features that can support benign consequences for design basis accidents (DBAs). Even in postulated severe accident conditions, the core is designed to remain under sub-critical condition in a passive coolable geometry. In case of the postulated severe accidents in SFRs, fuel relocation in the core region along the coolant channel is an important negative reactivity feedback factor that lowers the reactor power level and consequently eliminates the possibility of recriticality. Therefore, understanding the relocation behavior of fuels and the coolability of the relocated fuels in the postulated severe accident is one of the most important factors in the safety assessment of SFRs. In the present study, the relocation behavior of the metal fuel in a pin bundle geometry was investigated by injecting the metal fuels into the coolant channels with pressure. The first metal fuel relocation with pressure injection (RPI-1) experiment showed that many of the metal fuels levitated to upper plenum. In case of RPI-2 experiment, the Real Time X-ray Video System (RTXVS) was constructed and used to obtain a real time X-ray video of the experiment. This real time X-ray video clearly showed the relocation behavior of the pressure injected metallic uranium in the sodium coolant channel. Through this video, it was confirmed that part of the fuel was dispersed downward and part of the fuel was dispersed upward, and the relocation behavior of the injected fuel proceeded simultaneously in the downward and upward directions. So, it can be concluded that in case of pressure injection of the metallic fuel, there is a possibility that the metallic fuel can be quickly removed from the core region, resulting in a negative reactivity feedback effect.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Development of an accelerator-based neutron source to prototype Mo-99 production, Part II: A liquid LBE loop under a high vacuum

To provide US domestic supply of Mo-99 without using high-enriched uranium (HEU), a subcritical uranium target assembly (UTA) is irradiated by an accelerator-based neutron source to create Mo-99 through fission. Part I of this work discusses the design of a liquid lead–bismuth eutectic (LBE) windowless target for an accelerator-based neutron source development. Part II discusses how to couple this windowless target to an accelerator operating at an ultra-high vacuum and the subcritical UTA cooled by water at room temperature. Due to the windowless design of the target, the liquid LBE flow shares an ultra-high vacuum (<1.3 × 10 -7 Pa or 10 -9 Torr) space with the accelerator. As a result of this shared vacuum space, the LBE system must operate at a high vacuum (10 -3 ~10 -6 Pa or 10 -5 ~10 -8 Torr). A magnetic rotary motion feedthrough unit utilizes magnetic fluid to allow rotation of the pump while maintaining a high vacuum environment. Prior to testing the LBE system under vacuum, a pump curve measurement is performed to estimate flowrate in the system. This measurement also generates data on orifice loss coefficients, which are compared to correlations in literature. The second experiment investigates vacuum level in the LBE system during operation. High vacuum is maintained (10 -3 ~10 -5 Pa or 10 -5 ~10 -7 Torr) during system operation, and a residual gas analyzer (RGA) scan shows that partial pressures of residual gases in the LBE system lower over the duration of LBE system operations. The third experiment investigates the gravity driven liquid LBE flowing out of the target chamber in the return line, which is partially full. If the liquid LBE is not drained quickly enough, flooding in the target chamber could occur. The coefficient n in the Manning equation is found to be around 0.008 s/m 1/3 . The last experiment performed is a demonstration that a vacuum jacket could provide sufficient thermal insulation to allow coupling between 300 °C LBE loop and a water tank at room temperature. In conclusion, the results from these experiments have influenced the development of the neutron source for the future commercial scale Mo-99 production system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Power performance and loads characterization of laboratory-scale cross-flow rotors fabricated using additive manufacturing

Tidal energy conversion is a relatively new application for additive manufacturing (AM), where the focus has been on fabricating axial-flow turbine blades. AM techniques add material precisely where it is needed, creating more complex shapes with less waste. Cross-flow rotor geometry presents an opportunity for AM to improve rotor performance by fabricating features that cannot be created economically via conventional manufacturing. The challenges associated with using AM in cross-flow design include water resistance and degradation over time while retaining a level of quality equivalent to conventionally machined parts. In this work, AM materials were tested by environmentally conditioning samples in a seawater tank for 5 months, followed by performance and phase-resolved load testing of laboratory-scale rotors in a hydraulic flume. We found that while metals like titanium and Inconel have excellent performance in marine environments, achieving the desired geometry and performance is difficult. Thermoplastics degraded in seawater but were easier to form into desired geometries and could exceed the performance of an aluminum control rotor. Warping and surface finish were significant detractors from AM rotor performance. These results suggest that the primary benefits of using AM for cross-flow rotors is to quickly fabricate and test unconventional rotor geometries.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Machine-learning-based automatic small-angle measurement between planar surfaces in interferometer images: A 2D multilayer Laue lenses case

Here, we report a new machine-learning-based approach to automatically measure the small angle between multiple planar surfaces characterized by white light interferometers. By applying an unsupervised clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the multiple surfaces in an interferometer image are automatically identified as distinct surfaces. The angles between every two surfaces are then calculated through the surface fitting. This method can be applied to multiple surfaces regardless of their shapes and locations and significantly simplifies the angle measurement procedure. Using the developed method, we have demonstrated a quick and precise angle measurement for the alignment of 2D Multilayer Laue Lenses (MLLs) for the development of high-resolution x-ray microscopy. This automatic, accurate, and robust small-angle measurement method is compatible with widely used white light interferometers and can be further applied to other metrology applications of interferometer results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Synthetic proteins for COVID-19 diagnostics

There is an urgent need for inexpensive, rapid and specific antigen-based assays to test for vaccine efficacy and detect infection with SARS-CoV-2 and its variants. We have identified a small, synthetic protein (JS7), representing a region of maximum variability within the receptor binding domain (RBD), which binds antibodies in sera from nine patients with PCR-verified COVID-19 of varying severity. Antibodies binding to either JS7 or the SARS-CoV-2 recombinant RBD, as well as those that disrupt binding between a fragment of the ACE2 receptor and the RBD, are proportional to disease severity and clinical outcome. Binding to JS7 was inhibited by linear peptides from the RBD interface with ACE2. Variants of JS7, such as E484K or N501Y, can be quickly synthesized in pure form in large quantities by automated methods. JS7 and related synthetic antigens can provide a basis for specific diagnostics for SARS-CoV-2 infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗
DOE OSTI2021

Non-conformal attractor in boost-invariant plasmas

We study the dissipative evolution of (0+1)-dimensionally expanding media with Bjorken symmetry using the Boltzmann equation for massive particles in relaxation-time approximation. Breaking conformal symmetry by a mass induces a non-zero bulk viscous pressure in the medium. It is shown that even a small mass (in units of the local temperature) drastically modifies the well-known attractor for the shear Reynolds number previously observed in massless systems. For generic nonzero particle mass, neither the shear nor the bulk viscous pressure relax quickly to a non-equilibrium attractor; they approach the hydrodynamic limit only late, at small values of the inverse Reynolds numbers. Only the longitudinal pressure, which is a combination of thermal, shear and bulk viscous pressures, continues to show early approach to a far-off-equilibrium attractor, driven by the rapid longitudinal expansion at early times. Second-order dissipative hydrodynamics based on a gradient expansion around locally isotropic thermal equilibrium fails to reproduce this attractor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns

There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A review of neutronics and thermal hydraulics–based screening methods applied to accelerated nuclear fuel qualification

This paper reviews the state-of-the-art engineering approach for using thermal hydraulic (TH) and neutronics modeling and simulation (M&S) tools to perform rapid screening studies of novel nuclear fuel concepts within the context of accelerated fuel qualification. Global research efforts have introduced nuclear fuel and material concepts that mark a significant departure from traditional reactor materials. The number of new technologies being considered for development for light water reactors and advanced reactor types has created the need for an accelerated fuel qualification procedure. A key component of this procedure is the rapid identification of the most promising fuel concepts using computational screening studies. Advanced TH and neutronic M&S tools should be leveraged to efficiently determine whether the reactor performance and safety characteristics of a given concept warrant additional studies or whether the concept requires modification or elimination. This paper reviews best practices for performing these TH and neutronics screening studies at various stages during a fuel concept's progression through the qualification procedure. The motivation behind standardizing this approach is to minimize time and resources spent on qualification activities for fuel concepts that could be quickly refined or eliminated from consideration based on their reactor physics and TH characteristics. Adoption of this screening procedure—which focuses primarily on nuclear fuels but may be applicable to other reactor materials—will also help accelerate new material qualification by generating boundary conditions crucial to fuel performance evaluations and highlighting needed areas of separate effects experimentation. This article reviews the motivation behind the introduction of novel nuclear fuel concepts, provides incentive for utilizing TH- and neutronics-based screening studies, describes the screening approach and methodology, and includes discussion on how to interpret screening results to provide recommendations for the continued development of a given concept.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Opinion to address the personal protective equipment shortage in the global community during the COVID-19 outbreak

The current COVID-19 pandemic is stretching both the global supply for face masks and personal protective equipment (PPE). Production capacity is severely limited in many countries. This is a call for the R&D community, particularly to those in the polymer degradation and stability field. We have not only an opportunity but an obligation to engage and collaborate with virology and bio-medical experts. We require comparative R&D for extended, reuse and recyclability options. Additionally, there is urgent need for large scale institutional approaches and methods that can be quickly applied locally by non-experts with limited resources.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Influence of pilot-fuel mixing on the spatio-temporal progression of two-stage autoignition of diesel-sprays in low-reactivity ambient fuel-air mixture

The spatial and temporal locations of autoignition for direct-injection compression-ignition engines depend on fuel chemistry, temperature, pressure, and mixing trajectories in the fuel jets. Dual-fuel systems can provide insight into both fuel-chemistry and physical effects by varying fuel reactivities and engine operating conditions. In this context, the spatial and temporal progression of two-stage autoignition of a diesel-fuel surrogate, n-heptane, in a lean-premixed charge of synthetic natural-gas (NG) and air is imaged in an optically accessible heavy-duty diesel engine. The lean-premixed charge of NG is prepared by fumigation upstream of the engine intake manifold. Optical diagnostics include high-speed (15kfps) cool-flame chemiluminescence-imaging as an indicator of low-temperature heat-release (LTHR) and OH* chemiluminescence-imaging as an indicator high-temperature heat-release (HTHR). NG prolongs the ignition delay of the pilot fuel and increases the combustion duration. Zero-dimensional chemical-kinetics simulations provide further understanding by replicating a Lagrangian perspective for mixtures evolving along streamlines originating either at the fuel nozzle or in the ambient gas, for which the pilot-fuel concentration is either decreasing or increasing, respectively. The zero-dimensional simulations predict that LTHR initiates most likely on the air streamlines before transitioning to HTHR, either on fuel-streamlines or on air-streamlines in regions of near-constant φ. Due to the relatively short pilot-fuel injection-durations, the transient increase in entrainment near the end of injection (entrainment wave) is important for quickly creating auto-ignitable mixtures. To achieve desired combustion characteristics, e.g., multiple ignition-kernels and favorable combustion phasing and location (e.g., for reducing wall heat-transfer or optimizing charge stratification), adjusting injection parameters could tailor mixing trajectories to offset changes in fuel ignition chemistry.

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

DNS of ignition and flame stabilization in a simplified gas turbine premixer

With the increasing need for fuel flexibility, mitigation of auto-ignition (AI) inside gas turbine (GT) premixers becomes crucial. They must be designed to yield a sufficiently homogeneous fuel-air mixture to achieve low emissions while at the same time avoiding the occurrence of AI and subsequent flame stabilization. This challenge requires a detailed understanding of turbulent mixing and chemistry interactions. In the present work, a direct numerical simulation (DNS) of an array of jets in crossflow (JICF), representative of an industrial GT premixer, is reported to shed light on these complex phenomena. It is found that AI kernels form in the aft part of the premixer and coalesce into a flame front that then propagates upstream, mainly through the boundary layer, and successively engulfs the jets. This, therefore, suggests a significant role of the jet array pattern on the flame stabilization. It is noted that AI kernels continue to form independently during the whole time of the simulation. To clarify the contribution of AI and diffusion in the ignition kernels and the main flame, chemical explosive mode analysis (CEMA) is employed jointly with a kernel tracking algorithm. It is found that during the initial formation of the flame, many ignition kernels form in mixtures with low scalar dissipation rate and large contribution from AI mode. As they quickly grow, they merge into a single flame front that becomes increasingly more diffusion-assisted over time, balancing the AI mode. Turbulence is shown to have a significant enhancing effect in lean premixed flames, but further analysis is required to fully characterize it. These findings are relevant for the industrial premixer studied, and also for novel micromix concepts that may be used in the next generation of GT combustion systems.

ADVANCED PROPULSION SYSTEMS↗