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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Q2 Report for FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

This report describes the work and activities carried out towards the completion of each of the following milestones in FY25 Q2: 1. Demonstrate workflow for generating self-consistent CESOL plasma profiles + first wall and divertor loading prediction and generate the CAT plasma and neutron loading needed for further engineering analysis: $\circ$ Run CESOL with BOUT++/Hermes-3 and immersed boundary condition to directly map to wall: • Run BOUT++/Hermes-3 through the IPS workflow to find radial particle and energy diffusivities to match either the Eich or the physics-based scaling of the SOL heat flux width, and • Expand source of first wall heat flux to include charged particles, neutrals, and radiation from the core+edge. 2. Generate medium fidelity parametrized CAD: $\circ$ Develop the TRACER tool to read an existing CAD, regenerate the geometry based on vertex location and connectivity information, define vertex translation and parameters needed for scaling the CAD, and $\circ$ Utilize the FreeGS code to determine CAT PF coil placement, including minimizing the number of coils, coil current, and electromechanical stresses. 3. Utilize plasma loading for engineering analysis: $\circ$ Couple the plasma loading to input for OpenFOAM and demonstrate initial test of thermal analysis of CAT first wall loading with typical DCLL blanket component cooling boundary conditions. 4. Demonstrate nuclear analysis: $\circ$ Apply initial analysis of tritium transport in DCLL blanket by evaluating spatially resolved tritium generation rates, tritium diffusion and convection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Q1 Report for FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

This report describes the work and activities carried out towards the completion of each of the following milestones in FY25 Q1: 1. Demonstrate workflow for generating self-consistent CESOL plasma profiles + first wall and divertor loading prediction and generate the CAT plasma and neutron loading needed for further engineering analysis: $/circ$ Initially run CESOL with SOLPS and map heat flux using simple HEAT method: ▪ Generate SOLPS grid for CAT reference case, ▪ Implement and apply ‘lower’ fidelity method of using HEAT-like analytic method to map SOLPS heat flux from charged particles to first wall, and ▪ Provide initial heat flux + neutron loading for further engineering analysis. 2. Demonstrate multiphysics magnet analysis: $/circ$ Demonstrate validation of the Elmer workflow by comparing induced stresses due to EM forces generated by TF coils using multiple codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

CARD: CFD for Advanced Reactor Design

Software Tools and Expertise To Address Multiphase Flow Challenges in<p>Research, Design, and Optimization</p><p>This is the annual CARD project update to be presented at the 2024 FECM/NETL Spring R&amp;D Project Review Meeting.</p>

Dietiker, Jeff↗
DOE OSTI2025

Uncertainty quantification in MELCOR Safety analysis of ARIES reactor designs

MELCOR-TMAP is a combined thermal-hydraulics and tritium tracking code developed to simulate severe accident scenarios in fission and fusion power plants. Here, we demonstrate the results of MELCOR-TMAP analyses on historical ARIES program reference designs. By coupling MELCOR-TMAP with the open source RAVEN probabilistic risk analysis framework’s Bayesian UQ capabilities, we also demonstrate key uncertainties in material properties with the highest impact on tritium inventory and plant risk.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

University of Missouri Research Reactor LEU Fuel Element Flow Test Conceptual Design—Hydraulic Reactor Design Parameters

The University of Missouri-Columbia Research Reactor (MURR®) is one of five U.S. high performance research reactors (USHPRR), plus one critical facility, that actively collaborates with the National Nuclear Security Administration (NNSA) Material Management and Minimization(M 3 ) Reactor Conversion Program to convert to the use of low-enriched uranium (LEU, < 20 wt.% U-235) fuel. A new type of LEU fuel with very high density, based on an alloy of uranium and 10 weight percent molybdenum (U-10Mo), is expected to allow the conversion to LEU of USHPRR that have been found unable to be converted with previously qualified uranium silicide-aluminum (U 3 Si 2 -Al) dispersion fuel. MURR has been working with the USHPRR Reactor Conversion (RC) Pillar at Argonne National Laboratory to perform fuel element design and fuel cycle performance analyses, steady-state thermal hydraulics safety analyses, and accident safety analyses in preparation for the conversion of MURR and to support a preliminary Safety Analysis Report (SAR) for conversion to LEU fuel. This work is performed in preparation for the flow test campaign that will be conducted by the USHPRR RC Pillar. The purpose of the hydraulic performance evaluation of the MURR LEU fuel element designed by the RC Pillar is to test a prototypic commercially fabricated LEU fuel element to determine whether any failure modes are observed or predicted in the fuel element, including significant deformations such as plate bending, twisting, or plate detachment from the side plate under selected safety-basis limits for reactor hydraulic conditions. To support the design of the flow test for MURR LEU fuel element hydraulic performance evaluation, design parameters for hydraulic testing of the LEU fuel element are laid out in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Massachusetts Institute of Technology Reactor LEU Fuel Element Flow Test Conceptual Design – Hydraulic Reactor Design Parameters

The Massachusetts Institute of Technology Reactor (MITR-II, also referred to as MITR) is one of six U.S. high performance research reactors (USHPRR), including one critical facility, that is actively collaborating with the U.S. National Nuclear Security Administration (NNSA) Material Management and Minimization (M 3 ) Reactor Conversion Program to convert to the use of low-enriched uranium (LEU, < 20 wt% 235 U) fuel. The MIT Nuclear Reactor Laboratory has been working with the USHPRR Reactor Conversion (RC) Pillar at Argonne National Laboratory to perform fuel element design and fuel cycle performance analyses, steady-state thermal hydraulics safety analyses, and accident safety analyses in preparation for the conversion of MITR and support a preliminary Safety Analysis Report (SAR) for conversion to LEU fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗