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All sources NASA NTRS DOE OSTI

DOE OSTI2021

3D Toroidal Field Multipoles for Curved Accelerator Magnets

Curved magnets producing continuously rotating field multipoles along the length of the bend can provide strong and continuous transverse focusing, making them of interest for accelerator systems such as compact ion beam gantries and synchrotron light sources. Evaluating the utility of such rotating multipole systems requires an accurate description of field behavior for beam physics calculations. This paper presents a helical scalar potential solution in 3D toroidal harmonics relevant to boundary conditions on the surface of a torus. The resulting fields are evaluated for a curved helical quadrupole channel to illustrate field rotation and the effect of magnet curvature.

Brouwer, L.↗

DOE OSTI2022

Balancing Impedance and Controllability in Response Reconstruction

One concept in smart dynamic testing is to match the impedance that a component experiences between test and the environment of interest, but this begs the question: how much of an impedance match is needed and could there be too much? In a prior work, the authors performed MIMO testing with a small component connected to various assemblies, each of which had a differing degree of similarity to the actual flight boundary conditions. The results showed that the fidelity of the response at locations away from the control accelerometers was highly sensitive to the impedance. This work presents further case studies to explore these ideas. Subsequent tests are presented for an assembly that presumably matched the impedance even better, and which was also much more flexible, and the results obtained are even worse than when no attention was given to the impedance. Hence, the work presented here suggests that one should seek a balance between 1.) matching the impedance and 2.) improving the controllability of the component of interest. The concepts are explored using both test data of a benchmark component, for which the environment of interest was recorded as the component flew on a sounding rocket.

Shaker Test, Operational Vibration Environment, Su↗

DOE OSTI2021

Precision cooking system

The present invention comprises a precision cooking oven that utilizes laser sheets to cook food, thus creating homogeneously heated and uniformly seared food bodies and surfaces. Laser sheets move back and forth evenly injecting heat into items being cooked. All food cold or hot areas can be eliminated since intersecting laser lines can be completely projected on and accommodate an item's exterior surfaces. Items with non-uniform cross-sections and properties can be cooked uniformly by controlling the exact amount of energy projected into differing sections of the food. The oven is also capable of cooking autonomously. Since laser sheets can be precisely controlled, cooking results can be very predictable once the boundary conditions for a thermal analysis are determined. The oven can detect the properties of the items to be cooked and thereafter compute the needed time and power to achieve the desired results stipulated by an oven operator.

Lee, Henry Yu-Heng↗

DOE OSTI2022

Coupling subsurface and above-surface models for optimizing the design of borefields and district heating and cooling systems in the presence of varying water-table depth. In: Proceedings, 46th Workshop on Geothermal Reservoir Engineering

Dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange. Current modeling approaches in building and district energy simulation tools typically consider heat conduction through the ground between boreholes, without flow of groundwater. On the other hand, detailed simulation tools for subsurface heat and mass transfer exist, but these fall short in simulating above-surface energy systems. To support the design and operation of such systems, we have developed a coupled model including a software package for building and district energy simulation, and software for detailed heat and mass transfer in the ground. For the first, we use the open-source Modelica Buildings Library, which includes dynamic simulation models for building and district energy and control systems. For the heat and mass transfer in the soil, we use the TOUGH simulator. TOUGH can model heat and multi-phase, multi-component mass transport for a variety of fluid systems, as well as chemical reactions, in fractured porous media. In previous work, we described the coupling of these software packages, including how time-dependent boundary conditions for the borehole walls are synchronized for use in Modelica and TOUGH. We verified that the coupled Modelica/TOUGH code produced consistent results with the original Modelica code for an idealized problem in which heat transfer was purely by conduction in a uniform geologic medium. Here, we examine less idealized problems for which TOUGH’s advanced capabilities for modeling fluid flow are required. The first problem has a shallow vadose zone, and the second problem has a thicker vadose zone with a water-table depth that varies in time, which requires a fine vertical grid discretization for the TOUGH model.

Doughtry, Christine↗

DOE OSTI2022

Interface Capturing Simulations on Pool Boiling Performance With Multiple Nucleation Sites

High-fidelity pool boiling simulations can provide a numerical database for improving mechanistic boiling models by allowing for specific evaluation of interactions among bubbles. Previously published pool boiling simulations investigated two nucleation sites in which bubble growth at one site suppressed nucleation at the other site. Based on previous study results, more complicated interface-capturing simulations on pool boiling were conducted using PHASTA code with locally refined unstructured mesh.First, different boundary conditions (BCs) were assessed to support robustness and reproducibility of the boiling model. Then, a scale study was conducted at a larger domain with nine nucleation sites, four of which were activated. Involving more nucleation sites increased the complexity of bubble interactions from surrounding sites. Finally, bubble departure behavior influenced by wall heat flux was investigated. When heat flux was increased, the order of bubble departure changed, but diagonal bubbles always departed after one another. The departure time interval between the first and second bubble reduced as heat flux increased. The corresponding frequency was almost linearly proportional to the heat flux. In addition, bubble departure behavior was found to be greatly influenced by the nucleation site pattern. Multiple nucleation sites resulted in superimposed inhibitive effects from surrounding sites to each bubble, which extensively delayed the departure.The work presented here provides new insight on the fundamental understanding of boiling phenomena, contributes to the development of a 3D multiphase computational fluid dynamics (M-CFD) model, and provides a more comprehensive database for data-driven pool boiling studies.

Fan, Joy↗

DOE OSTI2022

Times (and the Grid) are A-Changin'

<span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar highlighted two datasets that provide forward-looking projections of electric grid GHG emissions for the United States. These data sets were created to help present-day analyses and decision-support tools incorporate what we know about the ongoing evolution of the U.S. electric grid. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">Additionally, the webinar compared these two databases to other data sources to summarize differences (i.e., attributional versus consequential emissions rates, age and source of underlying data, regionalization approach, and boundary conditions) and highlighted the value and need for expert guidance when an analyst or developer is choosing how to represent electric-sector GHG emissions. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar was intended to set the stage for ACLCA roundtable events to further discuss these issues.

Jamieson, Matthew↗

DOE OSTI2021

Accelerate Nuclear Research and Development by Reducing Time and Cost Spend in the Pre-conceptual Design Phase of Advanced Reactor Experiments

The design process of every new concept, such as advanced nuclear reactors or associated experiments, starts with the pre-conceptual design phase. In this phase, the viability of a wide range of design options needs to be assessed quickly, to understand the operating envelope and its feasibility. A variety of physics models (thermal-hydraulics, neutronics, mechanical design, etc.) has to be considered at this very first design stage and optimum component sizes and materials (e.g. heat exchangers, piping, turbomachinery, coolant type, etc.) have to be chosen for a given set of boundary conditions (e.g. heat source, heat sink, flow rate, etc.). Detailed solutions such as provided by high fidelity methods like computational fluid dynamics (CFD), Monte Carlo methods, etc. and even lower fidelity tools such as system or subchannel codes, etc. are usually not used during the pre-conceptual design due to the relatively long time needed to create input models, the computational time to obtain a solution and the lack of flexibility to quickly investigate different combinations of components, individual component sizes and material properties. High fidelity tools are usually only employed in the conceptual design and later phases once a base concept has been identified during the pre-conceptual design stage. The current practice during the pre-conceptual design stage is that analysts collect the needed equations, material properties, closure laws, etc. and create ad-hoc solutions form scratch for every new problem. There clearly is a lack of a flexible scoping tool that can be used during pre-conceptional design before higher fidelity tools (as described above) come into play. To reduce user errors in ad-hoc solutions and increase fidelity and efficiency, this project aims to investigate and develop a user-friendly scoping tool to address the thermal-hydraulic designing needs during preconceptual experiment design, i.e. Thermal-hydraulic Research Universal Scoping Tool (TRUST). The success of TRUST will provide the nuclear engineers with an easy-to-use and affordable calculator for early reactor system design and optimization.

42 ENGINEERING↗

DOE OSTI2022

Crucible Melter Simulation in Nek5000

Nuclear tank waste at the Hanford site is slated for vitrification in large scale refractory-lined melters to transform it into a stable borosilicate waste form suitable for long-term storage or disposal. Molten glass corrodes the refractory lining over time at a rate correlated to the velocity of molten glass against the refractory-lined wall. To properly design a melter and plan for maintenance, it is necessary to accurately model the glass flow and find the correlation between flow velocity and corrosion rate. This modeling was started in STAR-CCM+ CFD software and is being continued in Nek5000 for its fast-running and quick turnaround code. This poster explains the basic process of reconstructing the geometry, fluid properties, and heating of test melters in Nek5000. The geometry is imported from a mesh file and boundary conditions assigned based on surface IDs. The fluid properties are set in the .par and .usr case files. The heating of the actual crucibles in joule heating with electrodes but simulated in Nek5000 with a volumetric heat source term. This volumetric heat source is fit to the actual heating profile with piecewise polynomials and exponentials. Basic results and verification methods are explained in the poster, as well as further work that must be done to complete modeling.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Beamline Volume Relief Analysis for the PIP-II SSR2 Cryomodule at Fermilab

The beam volume of the Pre-Production Single Spoke Resonator type 2 (ppSSR2) cryomodule [1] for the Proton Improvement Plan-II (PIP-II) [2] project will be protected against over-pressurization using a burst disk. This contribution focuses on the analysis of the relief of such trapped volume during a catastrophic scenario with multiple systems failures. An analytical model, able to predict the pressure in the beam volume depending of the various boundary conditions, has been developed and will be presented along with the results.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

PARTICLE RESOLVED DNS STUDY OF TURBULENCE EFFECTS ON HYPORHEIC MIXING IN RANDOMLY PACKED SEDIMENT BEDS

Pore-resolved direct numerical simulations (DNS) are used to investigate the interactions between stream-water flow turbulence and groundwater flow through a porous sediment bed in the hyporheic zone. Two permeability Reynolds numbers (2.56 and 5.17), representative of aquatic systems and representing ratio of permeability to viscous length scales, were simulated to understand its influence on the momentum exchange at the sediment-water interface (SWI). A doubleaveraging methodology is used to compute the Reynolds stresses, form-induced stresses, and pressure fluctuations. It is observed that both shear layer and turbulent shear stress penetration increases with ReK. Reynolds and form-induced bed-normal stresses increase with ReK. The peak values of the form-induced stresses for the lower (2.56) and higher (5.17) ReK happen within the top layer of the sediment bed. The sum of turbulent and form-induced pressure fluctuations, analyzed at their respective zero-displacement planes, are statistically similar and can be well approximated by a t location-scale distribution fit providing with a model that could potentially be used to impose boundary conditions at the SWI in reach scale simulations.

Karra, Shashank↗

DOE OSTI2022

Thermal Stress Modeling and Analysis of Packed-bed Thermocline Energy Storage Tank for INL Thermal Energy Distribution System (TEDS)

The Thermal Energy Distribution System (TEDS) at Idaho National Laboratory (INL) is a thermal-hydraulic flow loop to support the integration of co-located multiple experimental systems, where a packed-bed thermal energy storage (TES) is installed as a thermal buffer and storage unit for TEDS. The packed-bed TES is adopted in TEDS because of its benefit as a low-cost single-tank storage option compared to the traditional two-tank storage. However, thermal ratcheting is one potential design concern which is caused by the rearrangement of granular filler inside a packed-bed tank during continuous thermal cycling operation of the packed-bed TES tank. If the thermally induced stress exceeds yield strength of the tank wall, it may cause catastrophic consequences like rupture of the thermal storage tank. Thus, it is crucial to understand the phenomenon to ensure the robust operation. Based on the temperature boundary conditions given by transient thermal analyses with computational fluid dynamics (CFD) simulations, the thermal ratcheting analysis is then conducted to evaluate the hoop stress and resultant thermal ratcheting potential of the TES tanks with two different modeling approaches: (1) infinite rigidity model and (2) Drucker-Prager (DP) model. The validity of each modeling method was examined by comparing the numerical simulation with the experimental data obtained from the packed-bed TES tank for Solar One Plant and evaluate the thermal ratcheting potential of the TEDS TES tank.

25 ENERGY STORAGE↗

DOE OSTI2022

Thermal Modeling and Simulation of the Packed-bed Thermal Energy Storage Combined with INL Thermal Energy Distribution System

Dynamic Energy Transport and Integration Lab (DETAIL) at Idaho National Laboratory is to support experimental demonstration and validation research on Nuclear-Renewable Hybrid Energy System [1]. The Thermal Energy Distribution System (TEDS) is a thermal-hydraulic flow loop in DETAIL with its own dedicated control system to support the integration of co-located multiple experimental systems, where a packed-bed thermal energy storage (TES) is installed as a thermal buffer and storage unit. Among various TES options, the packed-bed TES is adopted in TEDS because it offers a low-cost single-tank thermal storage option compared to the traditional two-tank TES. However, since the TES tank is filled with granular fillers having different thermophysical properties from those of TES tank wall, there is a potential thermo-mechanical issue to be carefully addressed like thermal ratcheting which may pose a significant design concern for the packed-bed TES tank. Thermal ratcheting is a thermomechanical process caused by the repeated rearrangement of granular filler inside a TES tank during continuous thermal cycling operation of the packed-bed TES system. If the thermally induced stress exceeds the yield strength of a TES tank wall, catastrophic consequences may happen like rupture of the TES tank. Thus, it is important to understand the phenomenon to ensure the robust operation of the packed-bed TES tank. Given that thermal ratcheting is caused by complex interaction of thermal transport in the porous bed and solid mechanics, the accurate prediction of transient thermal behavior of the packed-bed TES tank, which is the focus of this paper, is critical to the reliable thermal ratcheting analysis. This paper discusses the numerical modeling, simulation, and validation studies that are ongoing at INL to investigate the transient thermal behavior of the packed-bed TES. Of particular concern is the transient thermal process occurring in the packed-bed TES unit that is operated in conjunction with the INL TEDS. The main goal of this research is three-fold: (i) provide preliminary insights into the transient thermal behavior of the TEDS TES tank, (ii) support the thermal measurement and validation plan for TEDS experiment, and (iii) provide transient thermal boundary conditions to support the reliable thermal ratcheting analysis of the TEDS TES tank. For the transient thermal modeling and analysis, a CFD model was developed, and the validity of the modeling approach was examined via comparing the numerical simulation results with the experimental data obtained from various design characteristics of packed-bed TES tanks. Then, the present modeling method was applied for the transient thermal analysis of the TEDS TES tank, and the results are discussed along with the potential improvement of data acquisition strategy for the future TEDS experiments for more precise validation study.

25 ENERGY STORAGE↗

DOE OSTI2022

A Machine Learning-Assisted Framework to Control Thermally Anisotropic Building Envelopes in Residential Buildings

To curb the energy consumption of buildings and their related CO2 emissions, Oak Ridge National Laboratory (ORNL) has developed the thermally anisotropic building envelope (TABE) —a multi-layer design comprising insulation materials and metal foils connected to thermal loops. In this study, a machine learning-assisted framework was developed to control the TABE in residential buildings to reduce the computation load for future optimal rule-based control and application. First, a 2D finite element model was established in COMSOL to calculate the hourly heat flux through exterior walls installed with the TABE. Then, TABE wall heat fluxes were simulated for various given indoor and outdoor boundary conditions, as well as thermal loops fluid temperatures and flow rates. Since the finite element simulations are computationally expensive, an artificial neural network (ANN) was then trained to use as a proxy of the finite element (COMSOL) modeling. Finally, the trained ANN model was coupled with the EnergyPlus model to predict the energy consumption of a US Department of Energy prototype single-family house installed with the TABE. An optimal simple rule-based control was determined from predefined rules for a case study. The results demonstrate that the developed machine learning–assisted framework can reduce 99.9% of the computation time while efficiently managing residential building energy for installed TABE walls.

Shen, Zhenglai↗

DOE OSTI2023

EXPERIMENTAL AND COMPUTATIONAL STUDY OF DIRECT CONTACT MEMBRANE DISTILLATION

Experimental and computational studies of direct contact membrane distillation (DCMD) are conducted. The permeate flux is measured in a module containing a flat sheet membrane. Polytetrafluoroethylene (PTFE) hydrophobic membrane (Membrane Solutions, FPB045A16) with a nominal pore size of 0.45 μm, the thickness of 179~239 μm, the porosity of 80% is used in the experiments. The feed solution is a binary mixture of water and NaCl. The inlet feed temperature is fixed at 70°C, and the feed Reynolds number is varied between 500 and 2000. Two inlet feed concentrations, 0 and 35g/L, are considered. The permeate flow rate was kept constant at Re of 1300 and temperature of 20°C. Three-dimensional steady-state computational fluid dynamics (CFD) simulations are performed using the laminar model. The Navier’s Stokes, energy, and mass transport equations in each channel coupled with flux boundary conditions imposed at the membrane surface are solved. The predicted module-averaged flux is compared to measured data for all operating conditions. Measured and predicted flux agrees well; validating both experiments and model, Temperature polarization in the feed and permeate channel and concentration polarization in the feed channel are characterized by validated CFD simulations.

Oztekin, Alparslan↗

DOE OSTI2023

Lubricant impacts on piston deposit formation in the Enterprise marine diesel research engine

The impact of lubricant formulation on piston deposits was studied using the Enterprise, a reduced-scale, single-cylinder, two-stroke crosshead marine diesel research engine. The Enterprise engine was specially designed for marine diesel lubricant research, with a custom reduced-scale cylinder lubricant injection system and extensive instrumentation of thermal boundary conditions on both the liner and piston. Lubricant conditions typical of full-scale marine diesel engines are obtained by matching mean piston speed, liner temperature profile, and combustion metrics to realistic values.Piston deposit thicknesses were measured after a set period of operation, with a focus on lubricant-based deposits on the lands and ring grooves, using both optical and contact-based measurement techniques. Engine operation for each lubricant was conducted according to a standardized protocol, with precise control of engine speed and load, cylinder lubricant injection rate, coolant temperatures, and system oil temperature; the liner and piston temperatures are continuously monitored during operation. After operation with each lubricant was completed, the engine was disassembled, and the piston and ring deposits were characterized. The piston was then thoroughly cleaned, and the lubricant system flushed, between each lubricant formulation being evaluated. The impacts on piston deposits of the lubricants being evaluated can thus be accurately quantified and are described herein.

Kaul, Brian↗

DOE OSTI2022

Component-wise reduced-order model design optimization such as for lattice design optimization

Systems and methods for optimizing a lattice structure design are disclosed herein. In some embodiments, a method for optimizing a lattice structure design can include (i) modeling the lattice structure with a component-wise reduced-order model (CWROM) and (ii) optimizing the CWROM based on a selected criterion using a topology optimization algorithm for lattice design. The selected criterion can include a boundary condition and a load applied to the lattice structure. By modeling the lattice structure as a CWROM, the optimization process can be very fast while still permitting the accurate computation of physical quantities of the lattice structure.

Choi, Youngsoo↗

DOE OSTI2023

Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant charge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. For Li-ion batteries, for example, lithium moving between electrodes during charging and discharging process causes expansion and contraction of grains, and the strongly anisotropic and nonlinearly [Li]-dependent grain material properties can cause grains to expand into and contract away from each other, leading to chemo-mechanical cracking. In the first part of this work, a RKPM based computational framework for solving the coupled solid-phase lithium conservation with Fickian diffusion and the lithium concentration dependent anisotropic mechanical problem subjected to a highly nonlinear Butler-Volmer boundary condition is introduced. The choice of RKPM completeness conditions for lithium concentration and mechanical deformation fields, and the variational consistency condition for the domain integration of the coupled problem is first determined. In the second part of this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material, by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that is necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure.

damage modeling↗

DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗