Publications

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presentation for DAMOP 2024

This manuscript presents a complete framework for the development and verification of physics-informed neural networks with application to the alternating-current power flow (ACPF) equations. Physics-informed neural networks (PINN)s have received considerable interest within power systems communities for their ability to harness underlying physical equations to produce simple neural network architectures that achieve high accuracy using limited training data. The methodology developed in this work builds on existing methods and explores new important aspects around the implementation of PINNs including: (i) obtaining operationally relevant training data, (ii) efficiently training PINNs and using pruning techniques to reduce their complexity, and (iii) globally verifying the worst-case predictions given known physical constraints. The methodology is applied to the IEEE-14 and 118 bus systems where PINNs show substantially improved accuracy in a data-limited setting and attain better guarantees with respect to worst-case predictions.

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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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Paper targets SPEEDAM 2024 (https://www.speedam.org). The paper provides details of a model predictive control designed to operate a four-zone medium-voltage AC/DC electric ship.

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This presentation is for the Society of Experimental Mechanics Annual Conference, to be delivered on June 5th.

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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.

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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).

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the western system coordinating council 9-bus test system using synthetic multi-device outage scenarios.

The Single Volume Scatter Camera (SVSC) Collaboration aims to develop portable neutron imaging systems for a variety of applications in nuclear non-proliferation. Conventional double-scatter neutron imagers are composed of several separate detector volumes organized in at least two planes. A neutron must scatter in two of these detector volumes for its initial trajectory to be reconstructed. As such, these systems typically have a large footprint and poor geometric efficiency. We report on the design and characterization of a prototype monolithic neutron scatter camera that is intended to significantly improve upon the geometrical shortcomings of conventional neutron cameras. The detector consists of a 50 mm×56 mm× 60 mm monolithic block of EJ-204 plastic scintillator instrumented on two faces with arrays of 64 Hamamatsu S13360-6075PE silicon photomultipliers (SiPMs). The electronic crosstalk is limited to < 5% between adjacent channels and < 0.1% between all other channel pairs. SiPMs introduce a significantly elevated dark count rate over PMTs, as well as correlated noise from after-pulsing and optical crosstalk. In this article, we characterize the dark count rate and optical crosstalk and present a modified event reconstruction likelihood function that accounts for them. We find that the average dark count rate per SiPM is 4.3 MHz with a standard deviation of 1.5 MHz among devices. The analysis method we employ to measure internal optical crosstalk also naturally yields the mean and width of the single-electron pulse height. We calculate separate contributions to the width of the single-electron pulse-height from electronic noise and avalanche fluctuations. We demonstrate a timing resolution for a single-photon pulse to be (128 ± 4) ps. Finally, coincidence analysis is employed to measure external (pixel-to-pixel) optical crosstalk. We present a map of the average external crosstalk probability between 2×4 groups of SiPMs, as well as the in-situ timing characteristics extracted from the coincidence analysis. Further work is needed to characterize the performance of the camera at reconstructing single- and double-site interactions, as well as image reconstruction.

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

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An analysis was performed to determine whether a hydrogen jet flame impinging on a tunnel ceiling composed of multiple prestressed steel reinforced concrete box beams could result in permanent damage to the tunnel. The lower layer of the concrete box beam was modeled to determine whether heat reaches the steel reinforcing bars and whether spalling could occur. Heat transfer analysis shows that the temperature remains constant at the location of the steel reinforcing bars after 1.3 minutes of impingement and reaches a maximum of 130°C after 5 minutes. However, assuming a constant impingement for 5 minutes is an over estimation due the existing fire model which includes conservative assumptions. Explosive spalling may occur at a thin layer (~0.05 in. at 50 seconds, 0.1 in. at 5 minutes) at the bottom surface of the concrete box beam, but the steel reinforcing bars will not be exposed to the hydrogen flame.

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Monte Carlo simulations are at the heart of many high-fidelity simulations and analyses for radiation transport systems. As is the case with any complex computational model, it is important to propagate sources of input uncertainty and characterize how they affect model output. Unfortunately, uncertainty quantification (UQ) is made difficult by the stochastic variability that Monte Carlo transport solvers introduce. The standard method to avoid corrupting the UQ statistics with the transport solver noise is to increase the number of particle histories, resulting in very high computational costs. In this contribution, we propose and analyze a sampling estimator based on the law of total variance to compute UQ variance even in the presence of residual noise from Monte Carlo transport calculations. We rigorously derive the statistical properties of the new variance estimator, compare its performance to that of the standard method, and demonstrate its use on neutral particle transport model problems involving both attenuation and scattering physics. We illustrate, both analytically and numerically, the estimator's statistical performance as a function of available computational budget and the distribution of that budget between UQ samples and particle histories. We show analytically and corroborate numerically that the new estimator is unbiased, unlike the standard approach, and is more accurate and precise than the standard estimator for the same computational budget.

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