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Fu, Chun

Publications and source records attributed to Fu, Chun.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of Principal Design Criteria of the Fast Modular Reactor

General Atomics Electromagnetic Systems (GA-EMS) is developing a 50-megawatt electric (MWe) helium-cooled Fast Modular Reactor (FMR). The project has been selected by the U.S. Department of Energy (DOE) for Advanced Reactor Concepts-20 (ARC-20). The long-term goal of the project is to design, license, and commercialize the FMR plant by the mid-2030s. Early engagement with the U.S. Nuclear Regulatory Commission (NRC) is an important step for the developers to license the advanced reactors including the FMR. As an effort to support the design and the preapplication regulatory engagement plan, GA-EMS is developing Principal Design Criteria (PDC) applicable to the FMR design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

A Simple Program to Estimate the Fast Modular Reactor Steady-State Closed Brayton Cycle Operating Parameters

The Fast Modular Reactor (FMR) is a gas-cooled fast reactor (GFR) that produces a net electricity output of 50 MW with helium-driven closed Brayton cycle. The power plant includes reactor system and power conversion unit (PCU). The reactor system produces fission energy that is transferred by helium flow. The PCU consists of a turbine, two compressors, and three heat exchanges. More specifically, the recuperator is a counter-flow energy recovery heat exchanger. The precooler and intercooler are the heat sinks. To analyze and understand the sensitivity of plant performance to the design parameters, a numerical program was written to calculate the plant operating parameters represented by the helium temperature, pressure, mass flow, and the heat sink temperatures. The program was developed for the steady-state operating condition. etc.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

The Fast Modular Reactor (FMR) Pre-Application Regulatory Engagement Plan

The Fast Modular Reactor (FMR) is a 50 MWe Gascooled Fast Reactor (GFR) being developed by General Atomics Electromagnetic Systems (GA-EMS) under U.S. Department of Energy’s (DOE’s) Advanced Reactor Demonstration Program (ARDP), specifically Advanced Reactor Concepts 2020 (ARC-20). The 3-year conceptual design of the FMR is being conducted with verifications of key metrics in fuel, safety, and operational performance. GAEMS is pursuing design, licensing, and commercialization of the proposed reactor, with demonstration by 2030 and deployment by the mid-2030s. The U.S. Nuclear Regulatory Commission (NRC) recommends the pre-application regulatory engagement to provide for early identification of regulatory requirements for advanced reactors and to provide all interested parties with a timely, independent assessment of the safety and security characteristics of advanced reactor designs. As such, GAEMS is developing a pre-application regulatory engagement plan (REP) of the FMR.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

The ASHRAE Great Energy Predictor III competition: Overview and results

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. Furthermore, the competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗