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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 OSTI2017

Superconducting Magnet Power Supply and Hard-Wired Quench Protection at Jefferson Lab for 12 GeV Upgrade

The superconducting magnet system in Hall B being designed and built as part of the Jefferson Lab 12 GeV upgrade requires powering two conduction cooled superconducting magnets - a torus and a solenoid. The torus magnet is designed to operate at 3770 A and solenoid at 2416 A. Failure Modes and Effects Analysis (FMEA) determined that voltage level thresholds and dump switch operation for magnet protection should be tested and analyzed before incorporation into the system. The designs of the quench protection and voltage tap sub-systems were driven by the requirement to use a primary hard-wired quench detection sub-system together with a secondary PLC-based protection. Parallel path voltage taps feed both the primary and secondary quench protection sub-systems. The PLC based secondary protection is deployed as a backup for the hard-wired quench detection sub-system and also acts directly on the dump switch. Here, we describe a series of tests and modifications carried out on the magnet power supply and quench protection system to ensure that the superconducting magnet is protected for all fault scenarios.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗

DOE OSTI2020

A Code-Agnostic Driver Application for Coupled Neutronics and Thermal-Hydraulic Simulations

While the literature has numerous examples of Monte Carlo and computational fluid dynamics (CFD) coupling, most are hard-wired codes intended primarily for research rather than as standalone, general-purpose applications. In this work, we describe an open source application, ENRICO, that enables coupled neutronic and thermal-hydraulic simulations between multiple codes that can be chosen at runtime (as opposed to a coupling between two specific codes). The application has been designed such that the control flow logic, domain mapping, nonlinear fixed-point iteration, solution transfers, and convergence checks are all agnostic to the underlying physics solvers used. Special emphasis has also been placed on enabling efficient execution on distributed-memory computing environments. The transfer of solution fields between solvers is performed in memory rather than through filesystem I/O. Additionally, solvers can be configured to run on overlapping or disjoint sets of processes. To date, coupling with the OpenMC and Shift Monte Carlo codes, the Nek5000 CFD code, and a simplified heat diffusion and subchannel solver has been implemented in ENRICO. We present results for coupled simulations of a single light-water reactor fuel assembly based on the NuScale reactor using various combinations of the physics solvers. For this problem, the coupled simulations are shown to converge in about four Picard iterations. A comparison of the heat source and temperature distributions computed by ENRICO using OpenMC coupled with Nek5000 and Shift coupled with Nek5000 illustrates remarkable agreement between the codes.

42 ENGINEERING↗

DOE OSTI2018

Instrumentation and Control Selection for the 12 GeV Hall-B Magnets at Jefferson Lab

Here, as part of the Jefferson Lab 12 GeV accelerator upgrade, the Experimental Physics Hall B detector system requires two superconducting magnets – a Torus and a Solenoid. The specifications required maximum space for the detectors which led to the choice of conduction cooling for each magnet. The Torus consists of 6 trapezoidal 'race-track'-type coils connected in series with an operating current of 3770 A. The Solenoid is an actively shielded 5 Tesla magnet consisting of 5 coils connected in series operating at 2416 A. Within the hall the two magnets are located in close proximity to each other and are surrounded by particle detectors. We describe the philosophy behind the instrumentation selection and control design that accounts for this proximity and other challenging working conditions. We describe the choice of sensor technologies, as well as the control and data acquisition methods. The magnet power and cryogenic control sub-systems are implemented using Allen Bradley Control Logix 1756-L72 Programmable Logic Controllers. Sensor instrumentation read-backs are routed into the PLC via National Instruments cRIO hardware (Field Programmable Gate Arrays or FPGA/RT application) using Jefferson Lab designed FPGA-based multi-sensor-excitation-chassis. Configuration, monitoring, and alarm handlers for the magnet systems are provided via an Experimental Physics Instrumentation & Control System interface (EPICS). Failure Modes and Effects Analysis (FMEA) and the requirement to monitor critical parameters during operation guided the selection of instrumentation and associated hardware. The design of the quench protection and voltage tap sub-systems was driven by the anticipated level of voltages developed during a magnet quench. The primary hard-wired quench detection and protection sub-system together with the secondary PLC-based protection sub-system is also discussed. The successful commissioning and subsequent performance of these magnets demonstrates the robustness of the design and implementation approach that was adopted by the Jefferson Lab team and serves as an excellent 'How To' guide for future projects of this size and complexity.

12 GeV↗

DOE OSTI2017

Higher Order Dispersion and Momentum Compaction in MAD-X/PTC using NormalForm

Dispersion and the momentum compaction factor have been treated since ages in the literature of accelerator physics to rst order. Since quite some time dispersion and the momentum compaction factor can be derived via PTC and thereby in MAD-X to arbitrary order (presently hard-wired to fourth order in MAD-X). It was realized that little is known about this high order derivation in the community. This paper attempts to clarify how the higher order momentum compaction factors are derived from rst principle allowing for an alternative way to calculate them.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Ultra: Underwater Laser Telecommunications & Remote Access (Final Report)

High speed wireless communication has proven elusive in subsea environments due to the inherent bandwidth limitations of acoustics and range limitations of other transmission modalities. A truly connected subsea system necessitates a high-speed, resilient architecture that can enable the integration of new sensor technologies and edge analytics and allow closed-loop monitoring and control of subsea operations for integrity monitoring and optimization. Like terrestrial Internet of Things applications, the realization of this “digital subsea” vision requires the application of high speed, point-to-point wireless technologies to complement rather than replace “hard-wired” communications such as optical fiber or acoustic systems. This work addresses the development of ULTRA (Underwater LASER Telemetry and Remote Access), an ultra-long range underwater laser communications system for use in critical points of the subsea communications architecture to increase reliability, operational flexibility, and reduce communication system maintenance associated with physical subsea connections.

02 PETROLEUM↗

DOE OSTI2015

Three-dimensional stacked structured ASIC devices and methods of fabrication thereof

A 3D stacked sASIC is provided that includes a plurality of 2D reconfigurable structured structured ASIC (sASIC) levels interconnected through hard-wired arrays of 3D vias. The 2D sASIC levels may contain logic, memory, analog functions, and device input/output pad circuitry. During fabrication, these 2D sASIC levels are stacked on top of each other and fused together with 3D metal vias. Such 3D vias may be fabricated as through-silicon vias (TSVs). They may connect to the back-side of the 2D sASIC level, or they may be connected to top metal pads on the front-side of the 2D sASIC level.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Control Decision and Control Development of Hanford Tank Farms First Two ISA-84 Safety Instrumented Systems - 17370

Hanford's 242-A Evaporator has had the mission of reducing waste volume in the Hanford Tank Farms since the late 1970's. Until 2014, the Documented Safety Analysis for the 242-A facility considered protection and controls of the collocated worker (workers outside the facility) and the public. In 2010, Tank Farms Nuclear Safety engineers initiated a significant revision of the Documented Safety Analysis to implement DOE-STD-3009, Change Notice 3, which requires the protection of the facility worker in addition to the collocated worker and public. Through the use of an extensive process hazards analysis performed on the facility, it was concluded that it was essential to control conditions in the primary evaporation vessel (C-A-1) such that flammable gasses could not build up to the Lower Flammability Limit, and that another control was necessary to ensure that the C-A-1 vessel could not overflow. Early control decision meetings held during the development of the Documented Safety Analysis revision yielded no simple solution to the protection of workers from a flammable gas deflagration. Controls selected for the waste overflow accident were all mitigative in nature (i.e. the detection of a waste overflow after it already happened). Engineering could not replace the mitigative controls with preventive controls without attempting to credit some of the existing facility interlocks associated with the C-A-1 vessel operations. Software and hard-wired interlocks had been used at the facility to prevent overheating of the waste and overfilling of the C-A-1 vessel. These interlocks were considered useful to the prevention of the flammable gas deflagration and vessel overfilling accidents, but lacked the pedigree and reliability necessary to credit the interlocks as safety significant. It was at this stage of the Documented Safety Analysis control decision and control development that engineering became aware of the DOE's acceptance of the ISA-84 instrumented system standard. It became apparent that the ideal solution was not to attempt to credit existing interlock systems, but to develop a new system of 'safety interlocks' to the ISA-84 standard. A meeting was held with upper management of both engineering and operations to propose the use of safety instrumented systems at the 242-A facility. URS Corporation (now AECOM), who had recently supported the Savanah River site in developing ISA-84 controls, was consulted to support the development of two new instrumented systems for the 242-A facility. In addition to the use of the newly adopted ISA-84 standard, the failure modes and effect analysis process was implemented in a very effective manner to support the development of the new safety instrumented systems. Through a brand new methodology, and through a very dedicated and hard-working engineering, projects, and operations team, the new controls were designed, built, tested, and commissioned. At this time, five successful waste reduction campaigns have been performed at the facility with the new safety instrumented systems. During these waste processing campaigns, trips and spurious trips of the SIS have occurred - all of which acted properly. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Efficient and Zero Net Energy-Ready Plug Loads

Plug loads are the devices plugged into common electrical outlets. Modern buildings contain hundreds of these devices which, together, are responsible for at least 25 percent of the energy use in California buildings. Energy consumed by plug loads is increasing because the “Internet of Things” is, to a great extent, the “Internet of Plug Loads.” This project developed technologies that will reduce the energy use of plug loads. Two crosscutting strategies were investigated in detail: (1) technologies to reduce standby power use to near zero, and (2) methods to increase the use of direct current (DC) as a power source. Two new approaches to reduce standby to near zero were developed and have potential applicability to a wide range of devices. Prototype DC appliances were developed to demonstrate energy savings by bypassing power supplies and avoiding other losses. Networks of DC-powered devices can save even more energy and provide other benefits, such as resiliency during power outages. A unique category of energy-using devices was identified that provide life safety, health, and security to building occupants. Their installation or use is dictated by building codes, health providers, insurance companies, and other entities—none of whom would ordinarily prioritize energy efficiency. In the case of ground fault circuit interrupts (GFCIs), savings of 80 percent appear possible, simply by adopting the most efficient designs. Home medical equipment, particularly oxygen concentrators, are a rising category of energy use where significant savings are possible. This report also reviews the codes, standards, and other policies that affect energy use of plug loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗