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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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DOE OSTI2024

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Robust Event Simulation Variants Endowed to Ns-3 for General Exploration

Sandia's ns-3 contributions are modifications to the open source ns-3 network simulator that enable simulation speedup. One improvement, for example, removes the need for simulating the transmission and receipt of packets between nodes that are too distant to actually be able to communicate. In large-scale simulations this optimization has shown significant gains in performance. Additional contributions will pave the way for parallel discrete simulation (PDES) in ns-3. SAND2020-12456 O Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dickson, Joseph↗

DOE OSTI2022

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Career Development through Discreet Simulation Modeling - 19009

The cleanup mission of the Department of Energy (DOE) Office of River Protection (ORP) at the Hanford Site is to immobilize 212,000 m3 (56 million gallons) of nuclear waste, currently stored in 177 underground tanks, at the Waste Treatment and Immobilization Plant (WTP) and associated supplemental treatment facilities. Waste transfers to and from the WTP utilize the intricate Hanford Waste Feed Delivery (WFD) system, a network composed of: double-shell tanks (DSTs), single shell tanks (SSTs), the effluent treatment facility (ETF), a large number of pumps, valves, and leak detectors, and thousands of feet of underground transfer lines. This equipment must be prepared to safely and reliably transfer waste to the WTP. This study used discrete event simulation modeling to explore the behavior of the waste transfer system to identify areas of improvement. Two models and their subsequent results are presented: the Effluent Treatment Facility and the next-to-be-retrieved single-shell tanks (A/AX Farms). This study focused on identifying throughput bottlenecks, operational constraints, choke-point analysis, and redundancy in equipment, process design, and operational strategy. Analysis performed on the ETF OR Model indicated that the existing facility operation strategy will not result in a sufficient throughput to prevent WTP melter idling. Furthermore, in the absence of equipment failures, the facility was still shown to be unable to reach the required throughput to prevent melter idling. It is concluded that ETF system maintenance events are currently the main bottlenecks in the ETF facility. Analysis performed on the SST (A/AX) OR model indicate that the main drivers of the retrieval schedule are the hard retrieval start dates for each SST tank. In addition, it was concluded that operating the ventilation trains in parallel results in 15 times less retrieval interruptions and shortens the retrieval durations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗