Publications

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We examine coupling into azimuthal slots on an infinite cylinder with a infinite length interior cavity operating both at the fundamental cavity modal frequencies, with small slots and a resonant slot, as well as higher frequencies. The coupling model considers both radiation on an infinite cylindrical exterior as well as a half space approximation. Bounding calculations based on maximum slot power reception and interior power balance are also discussed in detail and compared with the prior calculations. For higher frequencies limitations on matching are imposed by restricting the loads ability to shift the slot operation to the nearest slot resonance; this is done in combination with maximizing the power reception as a function of angle of incidence. Finally, slot power mismatch based on limited cavity load quality factor is considered below the first slot resonance.

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This user’s guide documents capabilities in Sierra/SolidMechanics which remain “in-development” and thus are not tested and hardened to the standards of capabilities listed in Sierra/SM 5.4 User’s Guide. Capabilities documented herein are available in Sierra/SM for experimental use only until their official release. These capabilities include, but are not limited to, novel discretization approaches such as the conforming reproducing kernel (CRK) method, numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and J-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations.

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This report describes recommended abuse testing procedures for rechargeable energy storage systems (RESSs) for electric vehicles. This report serves as a revision to the USABC Electrical Energy Storage System Abuse Test Manual for Electric and Hybrid Electric Vehicle Applications (SAND99-0497).

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Using the power balance method we estimate the maximum electric field on a conducting wall of a cavity containing an interior structure supporting eccentric coaxial modes in the frequency regime where the resonant modes are isolated from each other.

Geothermal energy has been underutilized in the U.S., primarily due to the high cost of drilling in the harsh environments encountered during the development of geothermal resources. Drilling depths can approach 5,000 m with temperatures reaching 170 C. In situ geothermal fluids are up to ten times more saline than seawater and highly corrosive, and hard rock formations often exceed 240 MPa compressive strength. This combination of extreme conditions pushes the limits of most conventional drilling equipment. Furthermore, enhanced geothermal systems are expected to reach depths of 10,000 m and temperatures more than 300 °C. To address these drilling challenges, Sandia developed a proof-of-concept tool called the auto indexer under an annual operating plan task funded by the Geothermal Technologies Program (GTP) of the U.S. Department of Energy Geothermal Technologies Office. The auto indexer is a relatively simple, elastomer-free motor that was shown previously to be compatible with pneumatic hammers in bench-top testing. Pneumatic hammers can improve penetration rates and potentially reduce drilling costs when deployed in appropriate conditions. The current effort, also funded by DOE GTP, increased the technology readiness level of the auto indexer, producing a scaled prototype for drilling larger diameter boreholes using pneumatic hammers. The results presented herein include design details, modeling and simulation results, and testing results, as well as background on percussive hammers and downhole rotation.

Early on in 2018 Sandia recognized the Microsystems Engineering, Science and Applications (MESA) Programmatic Asset Lifecycle Planning capability to be unpredictable, inconsistent, reactive, and unable to provide strong linkage to the sponsor's needs. The impetus for this report is to share learnings from MESA's journey towards maturing this capability. This report describes re-building the foundational elements of MESA's Programmatic Asset Lifecycle Planning capability using a risk-based, Multi-Criteria Decision Analysis (MCDA) approach. To begin, MESA's decades-old Piano Chart + Ad Hoc Hybrid Methodology is described with a narrative of its strengths and weaknesses. Then its replacement, the MCDA /Analytical Hierarchy Process, is introduced with a discussion of its strengths and weaknesses. To generate a realistic Programmatic Asset Lifecycle Planning budget outlook, MESA used its rolling 20-year Extended Life Program Plan (MELPP) as a baseline. The new MCDA risk-based prioritization methodology implements DOE/NNSA guidelines for prioritization of DOE activities and provides a reliable, structured framework for combining expert judgement and stakeholder preferences according to an established scientific technique. An in-house Hybrid Decision Support System (HDSS) software application was developed to facilitate production of several key deliverables. The application enables analysis of the prioritization decisions with charts to display and provide linkage of MESA's funding requests to the stakeholders' priorities, strategic objectives, nuclear deterrence programs, MESA priorities, and much more.

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Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

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