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I.1 Heavy-Duty Diesel Combustion (Sandia National Laboratories)

Srna, Ales

Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of cleaner and more fuel-efficient engine operating strategies. Most current production heavy-duty diesel engines use a combination of both in-cylinder and exhaust emissions-control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes that can be influenced by using multiple fuel injections. Past work performed under this project showed that adding a second injection can reduce soot to levels below what would have been produced by an unchanged first injection, thereby increasing load while decreasing soot and potentially reducing brake specific fuel consumption. Information characterizing the important in-cylinder processes with multiple injections has been gleaned from ensemble-averaged planar laser-induced incandescence (PLII) imaging visualizing the soot cloud and planar induced fluorescence (PLIF) of OH characterizing the soot oxidation regions. PLII showed a consistent disruption of the first injection soot cloud by the second injection. In conjunction with OH-PLIF, differences in soot oxidation patterns for multiple injections compared to single injections were observed. This understanding was further enhanced in FY20, when high-speed imaging resolving the above-mentioned effects in a single cycle were combined with direct numerical simulations investigating the multiple-injection ignition process on the microscopic level of turbulence and chemistry interaction. In FY21, these findings in conjunction with findings from other researchers published in the scientific literature were composed into a preliminary multiple-injection conceptual model of fuel-mixing, injection and ignition processes. Remaining key research questions were also highlighted. In addition, wall heat flux was investigated experimentally and with numerical simulations to understand the potential of multiple injections to reduce the engine heat losses and further enhance the efficiency.

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TYPE Other Report YEAR 2021
DOIOSTI

Neuromorphic Graph Algorithms

Parekh, Ojas D.; Wang, Yipu; Ho, Yang; Phillips, Cynthia A.; Pinar, Ali P.; Aimone, James B.; Severa, William M.

Graph algorithms enable myriad large-scale applications including cybersecurity, social network analysis, resource allocation, and routing. The scalability of current graph algorithm implementations on conventional computing architectures are hampered by the demise of Moore’s law. We present a theoretical framework for designing and assessing the performance of graph algorithms executing in networks of spiking artificial neurons. Although spiking neural networks (SNNs) are capable of general-purpose computation, few algorithmic results with rigorous asymptotic performance analysis are known. SNNs are exceptionally well-motivated practically, as neuromorphic computing systems with 100 million spiking neurons are available, and systems with a billion neurons are anticipated in the next few years. Beyond massive parallelism and scalability, neuromorphic computing systems offer energy consumption orders of magnitude lower than conventional high-performance computing systems. We employ our framework to design and analyze new spiking algorithms for shortest path and dynamic programming problems. Our neuromorphic algorithms are message-passing algorithms relying critically on data movement for computation. For fair and rigorous comparison with conventional algorithms and architectures, which is challenging but paramount, we develop new models of data-movement in conventional computing architectures. This allows us to prove polynomial-factor advantages, even when we assume a SNN consisting of a simple grid-like network of neurons. To the best of our knowledge, this is one of the first examples of a rigorous asymptotic computational advantage for neuromorphic computing.

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TYPE SAND Report YEAR 2021
DOIOSTI

High-Brightness Ultraviolet Lasers for Leap-Ahead National Security Applications

Skogen, Erik J.; Fortuna, Seth A.; Allerman, Andrew A.; Smith, Michael L.; Alford, Charles; Crawford, Mary H.

In this project we endeavored to improve the state-of-the-art in UV lasers diodes. We made important advancements in several fronts from modeling, to epitaxial growth, to fabrication, and testing. Throughout the project it became clear that polarization doping would be able to help advance the state of laser diode design in terms of electrical performance, but the optical design would need to be investigated to ensure that a 2D guided mode would be supported. New capability in optical modeling using commercial software demonstrated that the new polarization doped structures would be viable. New capability in pulsed testing was established to reach the current and voltage required. Our fabricated devices had some parasitic electrical paths which hindered performance that we were ultimately unable to overcome in the project timeframe. We do believe that future projects will be able to leverage the advancements made under this project.

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TYPE SAND Report YEAR 2021
DOIOSTI

Diesel-like Fuels, Combustion, and Emissions

Busch, Stephen

The need to reduce the carbon footprint from medium- and heavy-duty diesel engines is clear; low-carbon biofuels are a powerful means to achieve this. Liquid fuels are rapidly deployed because existing infrastructure can be utilized for their production, transport, and distribution. Their impact is unique as they can decrease the greenhouse gas (GHG) emissions of existing vehicles and in applications resistant to electrification. However, introducing new diesel-like bio-blends into the market is very challenging. At a minimum, it requires a comprehensive understanding of the life-cycle GHG emissions of the fuels, the implications for refinery optimization and economics, the fuel’s impact on the infrastructure, the effect on the combustion performance of current and future vehicle fleets, and finally the implications for exhaust aftertreatment systems and compliance with emissions regulations. Such understanding is sought within the Co-Optima project.

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TYPE Other Report YEAR 2021
DOIOSTI

Lasergate: A windowless gas target for enhanced laser preheat in magnetized liner inertial fusion

Physics of Plasmas

Galloway, Benjamin R.; Slutz, Stephen A.; Kimmel, Mark; Rambo, Patrick K.; Schwarz, Jens; Geissel, Matthias; Harvey-Thompson, Adam J.; Weis, Matthew R.; Jennings, Christopher A.; Field, Ella; Kletecka, Damon; Looker, Quinn M.; Colombo, Anthony; Edens, Aaron; Smith, Ian C.; Shores, Jonathon; Speas, Christopher S.; Speas, Robert J.; Spann, A.P.; Sin, J.; Gautier, S.; Sauget, V.; Treadwell, P.A.; Rochau, Gregory A.; Porter, John L.

At the Z Facility at Sandia National Laboratories, the magnetized liner inertial fusion (MagLIF) program aims to study the inertial confinement fusion in deuterium-filled gas cells by implementing a three-step process on the fuel: premagnetization, laser preheat, and Z-pinch compression. In the laser preheat stage, the Z-Beamlet laser focuses through a thin polyimide window to enter the gas cell and heat the fusion fuel. However, it is known that the presence of the few μm thick window reduces the amount of laser energy that enters the gas and causes window material to mix into the fuel. These effects are detrimental to achieving fusion; therefore, a windowless target is desired. The Lasergate concept is designed to accomplish this by "cutting"the window and allowing the interior gas pressure to push the window material out of the beam path just before the heating laser arrives. In this work, we present the proof-of-principle experiments to evaluate a laser-cutting approach to Lasergate and explore the subsequent window and gas dynamics. Further, an experimental comparison of gas preheat with and without Lasergate gives clear indications of an energy deposition advantage using the Lasergate concept, as well as other observed and hypothesized benefits. While Lasergate was conceived with MagLIF in mind, the method is applicable to any laser or diagnostic application requiring direct line of sight to the interior of gas cell targets.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus

Harnessing exascale for whole wind farm high-fidelity simulations to improve wind farm efficiency

Crozier, Paul; Adcock, Christiane; Ananthan, Shreyas; Berger-Vergiat, Luc; Brazell, Michael; Brunhart-Lupo, Nicholas; Henry De Frahan, Marc T.; Hu, Jonathan J.; Knaus, Robert C.; Melvin, Jeremy; Moser, Bob; Mullowney, Paul; Rood, Jon; Sharma, Ashesh; Thomas, Stephen; Vijayakumar, Ganesh; Williams, Alan B.; Wilson, Robert; Yamazaki, Ichitaro; Sprague, Michael A.

Abstract not provided.

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TYPE Conference Presentation YEAR 2021
DOIOSTI

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

Adams, Brian M.; Bohnhoff, William J.; Dalbey, Keith R.; Ebeida, Mohamed S.; Eddy, John P.; Eldred, Michael S.; Hooper, Russell W.; Hough, Patricia D.; Hu, Kenneth T.; Jakeman, John D.; Khalil, Mohammad; Maupin, Kathryn A.; Monschke, Jason A.; Ridgway, Elliott M.; Rushdi, Ahmad A.; Seidl, Daniel T.; Stephens, John A.; Winokur, Justin G.

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.

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TYPE SAND Report YEAR 2021
DOIOSTI
Results 11151–11200 of 101,000
Results 11151–11200 of 101,000
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