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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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620 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Reduced-Order CFD Modeling to Support Waste Loading Optimization in Hanford WTP Vitrification

The U.S. DOE Hanford Site stores over 56 million gallons of radioactive liquid tank waste that must be treated and immobilized for long-term disposal The Waste Treatment and Immobilization Plant (WTP) will vitrify this waste by feeding it into Joule-heated melters, where it is incorporated into a stable borosilicate glass Computational fluid dynamics (CFD) simulations of glass melters can provide insight into the maximum achievable waste loading under varying melter operating conditions Fully resolved VOF multiphase simulations were used as the reference model to capture bubble-driven convection in the melter, including bubble formation, rise behavior, and induced glass melt circulation Effective bubble column diameter and rise velocity were extracted from the resolved simulations, compared with empirical correlations, and refit across relevant viscosity and gas flow rate conditions Explicit gas–liquid interface tracking was replaced with a single-phase momentum source term model, enabling faster steady-state CFD simulations while preserving the dominant hydrodynamic effects of bubbling New empirical correlations were developed for effective bubble column diameter and bubble rise velocity by fitting resolved simulation data across expected melter viscosity and gas flow rate ranges, providing improved inputs for the momentum source term model compared with existing literature correlations The momentum source term model reduced fluid-domain mesh size by 89% and achieved an 8.4× computational speedup relative to resolved bubbling simulations The validated momentum source term approach enables prediction of process-relevant heat transfer behavior in the integrated melter model, including heat transfer from the molten glass to the cold cap, plenum, refractory walls, and surrounding structural regions under varying melter operating conditions

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2024

ECAR-6580 Rev 0 ASME Section III, Division 5 Analysis of the MARVEL PCS and GVS Top Corner

The purpose of this Engineering Calculations and Analysis Report (ECAR) is to document the structural evaluation for part of the Microreactor Applications Research Validation and Evaluation (MARVEL) Primary Coolant System (PCS) and Guard Vessel System (GVS). For the PCS this relates specifically to the Distribution Plenum (DP), The Intermediate Heat Exchanger (IHX), and the Upper Downcomer (UD). For the GVS, this will include only the top corner that is machined into the DP Top Plate. These components will be evaluated using the 2021 version of ASME Section III, Division 5 [1] which is a design code that governs the construction of vessels, storage tanks, piping, pumps, valves, supports, core support structures and nonmetallic core components for use in high temperature reactor systems and their supporting systems. Materials at high temperature are subject to creep and fatigue mechanisms that require additional analyses that aren’t covered in ASME Section III, Division 1 rules. Division 5 contains two approaches: elastic or inelastic, however, additional Code Cases specific to Division 5, allow for an Elastic-Perfectly Plastic (EPP) approach. The components analyzed in this ECAR will use a combination of the elastic and EPP approach. Only Design and Service Levels A and B are evaluated in this ECAR. Service Level D evaluations for the entire PCS are documented in ECAR-6564, “MARVEL Project Primary Coolant System Pressure Vessel Stress Documentation” [2] and for the entire GVS are documented in ECAR-6574, “MARVEL Guard Vessel System FEA and ASME Analysis” [3]. The Design and Service Level A and B analyses for the PCS Downcomer piping and Core Barrel are documented in ANL-24/36, "Engineering Calculations and Analysis of the Core Barrel and Downcomer Piping in the MARVEL PCS” [4], and the GVS (except for the top corner) is in ECAR-6574, “MARVEL Guard Vessel System FEA and ASME Analysis.” See Section 2.0 for more detail on the analysis boundaries.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

SPC-70792 Rev 0 MARVEL Primary Coolant Management Subsystem Tubing Specification

This specification covers the requirements and details for the selection, fabrication, testing, cleaning, marking, shipping, inspection and installation of the Inert Gas System, and Primary Coolant Management System. This applies to tubing from the upper confinement patch panel to the upper plenum, including NaK and argon fill tubing, valves, and hangers. These systems consist of the necessary straight lengths, elbows, reducers, fittings, tube connections, instrument taps, and valves called for by applicable drawings. All components shall be procured as ASME Section III Class 1.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Computational Fluid Dynamics Analysis of Flexible Duct Junction Box Design

IBACOS explored the relationships between pressure and physical configurations of flexible duct junction boxes by using computational fluid dynamics (CFD) simulations to predict individual box parameters and total system pressure, thereby ensuring improved HVAC performance. Current Air Conditioning Contractors of America (ACCA) guidance (Group 11, Appendix 3, ACCA Manual D, Rutkowski 2009) allows for unconstrained variation in the number of takeoffs, box sizes, and takeoff locations. The only variables currently used in selecting an equivalent length (EL) are velocity of air in the duct and friction rate, given the first takeoff is located at least twice its diameter away from the inlet. This condition does not account for other factors impacting pressure loss across these types of fittings. For each simulation, the IBACOS team converted pressure loss within a box to an EL to compare variation in ACCA Manual D guidance to the simulated variation. IBACOS chose cases to represent flows reasonably correlating to flows typically encountered in the field and analyzed differences in total pressure due to increases in number and location of takeoffs, box dimensions, and velocity of air, and whether an entrance fitting is included. The team also calculated additional balancing losses for all cases due to discrepancies between intended outlet flows and natural flow splits created by the fitting. In certain asymmetrical cases, the balancing losses were significantly higher than symmetrical cases where the natural splits were close to the targets. Thus, IBACOS has shown additional design constraints that can ensure better system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

New Insights for Improving the Designs of Flexible Duct Junction Boxes (Fact Sheet)

IBACOS explored the relationships between pressure and physical configurations of flexible duct junction boxes by using computational fluid dynamics (CFD) simulations to predict individual box parameters and total system pressure, thereby ensuring improved HVAC performance. Current Air Conditioning Contractors of America (ACCA) guidance (Group 11, Appendix 3, ACCA Manual D, Rutkowski 2009) allows for unconstrained variation in the number of takeoffs, box sizes, and takeoff locations. The only variables currently used in selecting an equivalent length (EL) are velocity of air in the duct and friction rate, given the first takeoff is located at least twice its diameter away from the inlet. This condition does not account for other factors impacting pressure loss across these types of fittings. For each simulation, the IBACOS team converted pressure loss within a box to an EL to compare variation in ACCA Manual D guidance to the simulated variation. IBACOS chose cases to represent flows reasonably correlating to flows typically encountered in the field and analyzed differences in total pressure due to increases in number and location of takeoffs, box dimensions, and velocity of air, and whether an entrance fitting is included. The team also calculated additional balancing losses for all cases due to discrepancies between intended outlet flows and natural flow splits created by the fitting. In certain asymmetrical cases, the balancing losses were significantly higher than symmetrical cases where the natural splits were close to the targets. Thus, IBACOS has shown additional design constraints that can ensure better system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Computational Fluid Dynamics Analysis of Flexible Duct Junction Box Design

IBACOS explored the relationships between pressure and physical configurations of flexible duct junction boxes by using computational fluid dynamics (CFD) simulations to predict individual box parameters and total system pressure, thereby ensuring improved HVAC performance. Current Air Conditioning Contractors of America (ACCA) guidance (Group 11, Appendix 3, ACCA Manual D, Rutkowski 2009) allows for unconstrained variation in the number of takeoffs, box sizes, and takeoff locations. The only variables currently used in selecting an equivalent length (EL) are velocity of air in the duct and friction rate, given the first takeoff is located at least twice its diameter away from the inlet. This condition does not account for other factors impacting pressure loss across these types of fittings. For each simulation, the IBACOS team converted pressure loss within a box to an EL to compare variation in ACCA Manual D guidance to the simulated variation. IBACOS chose cases to represent flows reasonably correlating to flows typically encountered in the field and analyzed differences in total pressure due to increases in number and location of takeoffs, box dimensions, and velocity of air, and whether an entrance fitting is included. The team also calculated additional balancing losses for all cases due to discrepancies between intended outlet flows and natural flow splits created by the fitting. In certain asymmetrical cases, the balancing losses were significantly higher than symmetrical cases where the natural splits were close to the targets. Thus, IBACOS has shown additional design constraints that can ensure better system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗