Efficient Sampling of Climate Models using Bayesian Optimal Experimental Design
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Sustainable aviation fuels (SAFs) offer an effective pathway to decarbonize the aviation sector, which accounts for about 5% of the global net effective radiative forcing, and is expected to double in the next two decades. A primary objective of the SAF GC Roadmap is to develop sustainable fuels that avoid “sooting, aerosols, and other contributors to vapor trail emissions." Another parts of this project is motivated by ongoing efforts to develop aromatic-free SAFs by using cycloalkanes to match seal swell characteristics of current fossil-based jet fuel (e.g. Jet A).
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Physical Review E
Shock-driven implosions with 100% deuterium (D2) gas fill compared to implosions with 50:50 nitrogen-deuterium (N2D2) gas fill have been performed at the OMEGA laser facility to test the impact of the added mid-Z fill gas on implosion performance. Ion temperature (Tion) as inferred from the width of measured DD-neutron spectra is seen to be 34%±6% higher for the N2D2 implosions than for the D2-only case, while the DD-neutron yield from the D2-only implosion is 7.2±0.5 times higher than from the N2D2 gas fill. The Tion enhancement for N2D2 is observed in spite of the higher Z, which might be expected to lead to higher radiative loss, and higher shock strength for the D2-only versus N2D2 implosions due to lower mass, and is understood in terms of increased shock heating of N compared to D, heat transfer from N to D prior to burn, and limited amount of ion-electron-equilibration-mediated additional radiative loss due to the added higher-Z material. This picture is supported by interspecies equilibration timescales for these implosions, constrained by experimental observables. The one-dimensional (1D) kinetic Vlasov-Fokker-Planck code ifp and the radiation hydrodynamic simulation codes hyades (1D) and xrage [1D, two-dimensional (2D)] are brought to bear to understand the observed yield ratio. Comparing measurements and simulations, the yield loss in the N2D2 implosions relative to the pure D2-fill implosion is determined to result from the reduced amount of D2 in the fill (fourfold effect on yield) combined with a lower fraction of the D2 fuel being hot enough to burn in the N2D2 case. The experimental yield and Tion ratio observations are relatively well matched by the kinetic simulations, which suggest interspecies diffusion is responsible for the lower fraction of hot D2 in the N2D2 relative to the D2-only case. The simulated absolute yields are higher than measured; a comparison of 1D versus 2D xrage simulations suggest that this can be explained by dimensional effects. The hydrodynamic simulations suggest that radiative losses primarily impact the implosion edges, with ion-electron equilibration times being too long in the implosion cores. The observations of increased Tion and limited additional yield loss (on top of the fourfold expected from the difference in D content) for the N2D2 versus D2-only fill suggest it is feasible to develop the platform for studying CNO-cycle-relevant nuclear reactions in a plasma environment.
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Presentation of an overview of Q Framework at Sandia at Frama-C Days in Paris, France. Website for event is here: https://cea.invityou.com/frama-c-days
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This is for a presentation based on an extended abstract accepted to LDMSCon 24.
In an effort to decarbonize legacy energy systems, several projects around the world are exploring alternatives to natural gas. Gaseous hydrogen is proposed as a carbon-free fuel to displace natural gas in existing legacy natural gas distribution systems, some of which continue to operate after 100 years (or more) in service. These systems, particularly in older industrial centers, contain cast iron pipe. However, the fracture resistance of most metals is degraded in gaseous hydrogen environments. This study evaluated the fracture resistance of several legacy cast iron pipe materials while exposed to gaseous hydrogen. Measurements were performed in three environments: air, a gas mixture with hydrogen partial pressure of 1 bar and pure hydrogen with a partial pressure of 34 bar. Although cast iron is generally considered a low ductility metal, elastic-plastic fracture methods are needed to assess the fracture resistance of the relatively small specimens that can be extracted from legacy pipe. Hydrogen reduced the fracture resistance of these cast iron materials by 10-40%. In air, the fracture resistance was determined to be as high as 21 MPa m1/2, whereas in gaseous hydrogen at pressure of about 1 bar the fracture resistance was as low as 13 MPa m1/2. Additional modest degradation of the fracture resistance was assessed at higher partial pressure (as low as 12 MPa m1/2).
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Describes some of the non-lithium-ion battery technologies that CEC is evaluating for long duration energy storage in CA.
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This is a presentation for a short course on accelerators at the 2024 International Power Modulator and High Voltage Conference (Indianapolis, IN, May 28-Jun 1, 2024), and largely consists of already-published material.
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Computational Materials Science
Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. The prediction variance is shown to have a strong correlation with the absolute error over approximately 24 orders of magnitude. Using this prediction variance, an active learning algorithm is constructed to iteratively train a potential by selecting the structures with the most uncertain properties from a pool of candidate structures. The relative importance of the energy, force, and stress errors in the objective function is shown to have a strong impact upon the trajectory of their respective net error metrics when running the active learning algorithm. Batched training of different batch sizes is also tested against singular structure updates, and it is found that batches can be used to significantly reduce the number of retraining steps required with only minor impact on the active learning trajectory.
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This poster is a presentation at the DOE EERE VTO AMR for 2024
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Hydrogen energy storage can be used to achieve goals of national energy security, renewable energy integration, and grid resilience. Adapting underground natural gas storage facility (UNGSF) infrastructure for underground hydrogen storage (UHS) is one method of storing large quantities of hydrogen that has already largely been proven to work for natural gas. There are currently some underground salt caverns in the United States that are being used for hydrogen storage by commercial entities, but it is still a fairly new concept in that it has not been widely deployed nor has it been done with other geologic formations like depleted hydrocarbon reservoirs. Assessments of UHS systems can help identify and evaluate risks to people both working within the facility and residing nearby. This report provides example risk assessment methodologies and analyses for generic wellhead and processing facility configurations, specifically in the context of the risks of unintentional hydrogen releases into the air. Assessment of the hydrogen containment in the subsurface is also critically important for a safety assessment for a UHS facility, but those geomechanical assessments are not included in this report.
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U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping costs reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security in the design process can provide robust, economical, and effective physical protection systems (PPS). The purpose of this work is both to develop a framework for the integration of security into the design phase of a molten salt reactor (MSR) and show how to effectively design a PPS with a reduced staffing headcount. Specifically, this work focuses on integrating PPS design features into a developed facility layout by making minor modifications to building structures. A suite of tools, including Scribe3D©, PathTrace©, and Blender©, were used to model a hypothetical, generic domestic MSR facility. Physical protection elements such as sensors, cameras, barriers, and responders were added into the model based on defending the hypothetical MSR facility against a hypothetical design basis threat (DBT). Multiple outsider sabotage scenarios were examined, with adversary team sizes ranging from 4–8 to determine security system effectiveness. The results of this work will influence PPS designs and facility designs for U.S. domestic MSRs. This work will also demonstrate how a series of experimental and modeling capabilities across the Department of Energy (DOE) complex can impact the design and completion of security-by-design (SeBD) for small modular reactors (SMRs). The conclusions and recommendations in this document may be applicable to all SMR designs.
Invited presentation at 2024 Gordon Research Conference on Energetic Materials, presenting work from LDRD project 222468 and ASC P&EM program.
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Annals of Nuclear Energy
Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term INSPECTA, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. This work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.
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