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225 records · Page 13

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DOE OSTI2021

Advancing Catalytic Fast Pyrolysis Through Integrated Experimentation and Multi-Scale Computational Modeling

This webinar will highlight recent results from a multi-disciplinary research effort in which integrated reaction testing was coupled with particle- and reactor-scale computational modeling to advance catalytic fast pyrolysis (CFP) for the production of renewable hydrocarbon fuels. Data will be presented from a series of ex situ CFP experiments in which a fixed bed of Pt/TiO2 was utilized with co-fed H2 to upgrade woody biomass pyrolysis vapors. Further discussion will include the application of these data towards the development of (1) a multiscale simulation framework to de-couple apparent kinetics from both intraparticle and reactor-scale transport phenomena and (2) a finite element computational model to understand and predict thermal excursions during catalyst regeneration. Throughout the presentation, the speakers will emphasize synergistic outcomes derived from the collaborative approach and highlight ongoing research efforts to accelerate technology maturation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗

DOE OSTI2023

Effective Lanthanide Diffusivity through U-Zr Metallic Fuel

Fuel-cladding chemical interaction (FCCI) is one of the main concerns for the performance of U-Zr metallic fuels. Although FCCI results in complex phase transformations and morphological changes in both the fuel and cladding sides of the interface, the formation of a brittle layer in the cladding side of the interface (sometimes referred to as wastage) has one of the most significant impacts on the cladding mechanical integrity. This brittle layer is associated with the formation of intermetallic compounds between the cladding constituents Fe, Cr and the lanthanide fission product species. The formation of this cladding wastage layer has been the focus of recent efforts to develop a mechanistic modeling framework, including production of lanthanides in the fuel, transport through the fuel to the fuel-cladding interface, and phase transformation to form intermetallic phases. To calculate the rate of transport of lanthanides through the fuel, a multi-scale computational approach has been used. Atomistic calculations have been performed to determine the diffusivities of Nd, the most prevalent lanthanide species, through the solid fuel matrix and along pore surfaces. These diffusivities have been used as input parameters for a mesoscale model to determine an effective diffusion coefficient that accounts for the formation of porosity, its interconnection, and infiltration with bond sodium from the fuel-cladding gap. The effective diffusivity has been used in engineering-scale simulations using the BISON fuel performance code, and BISON simulations have been compared to representative validation cases from the EBR-II reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Systems Level Fuel Cycle Modeling in TMAP8 - A Demonstration

The tritium migration analysis program (TMAP) has been used for tritium inventory tracking and analysis for several years, and the Multiphysics Object Oriented Simulation Environment (MOOSE)-based TMAP8 has several improvements over TMAP4 and TMAP7, such as support for multiple dimensions and non-cartesian coordinate systems, as well as interoperability with sub-apps at higher and lower length scales. We demonstrate that TMAP8 has the additional capacity to solve systems-level problems using zero-dimensional ordinary differential equations by reproducing a literature model which describes a systems-level fuel-cycle of tritium inventory in a hypothetical fusion power plant. The capacity to run several coupled multi-scale physics calculations as part of a single package will be necessary for accurate blanket and fuel-cycle design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

SCM overview & the EBR-II shutdown heat removal tests validation.

Pronghorn is an engineering-scale, coarse-mesh, thermal-hydraulics tool for supporting reactor-core simulations of advanced nuclear reactors. Most of the current efforts in Pronghorn have been devoted in developing porous finite-volume capabilities and adapting closure correlations for coarse-mesh thermal-hydraulics modeling. However, for liquid-metal reactors (LMRs) with wire-wrapped fuel pin assemblies, a pin-level thermal-hydraulic resolution is required for most safety case studies (pin rupture, channel blockage, etc.). For this purpose, a new Subchannel application is developed in MOOSE, which affords the required flow field resolution, while still preserving an engineering-scale approach. This new solver can be natively coupled to Pronghorn and other MOOSE objects to enable full-core, multi-physics, multi-scale engineering studies. This presentation presents the main features of the SCM code and demonstrates a validation case based on the EBR-II SHRT tests.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2025

Multi-scale modeling of wastage layer formation in metallic fuel cladding

Fuel-cladding chemical interaction (FCCI) is a major concern for U-Zr metallic fuels' performance, primarily due to the formation of a brittle layer (wastage) in the cladding. This brittle layer, resulting from intermetallic compounds between cladding constituents Fe, Cr, and lanthanide fission products, significantly impacts the cladding's mechanical integrity. Recent efforts focus on developing a mechanistic modeling framework to understand lanthanide production, transport to the fuel-cladding interface, and phase transformation to intermetallic phases. A multi-scale computational approach has been used to calculate lanthanide transport rates, with atomistic calculations determining Nd diffusivities through the solid fuel matrix and along pore surfaces. These diffusivities inform a mesoscale model to determine an effective diffusion coefficient, accounting for porosity and infiltration with bond sodium. This effective diffusivity is used in engineering-scale simulations via the BISON fuel performance code, which has been validated against EBR-II and FFTF reactor experiments.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗