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At least 289 records · Page 16

Simulations and models for the Richtmyer– Meshkov instability with broadband perturbations

The Richtmyer–Meshkov instability (RMI) is shock driven and affects many phenomena from inertial fusion to supernova explosions. The behavior of single-modes in the RMI has been studied extensively but less is known with the broadband perturbations that occur in applications. Here, we describe extensive numerical simulations and modeling of the RMI with broadband perturbations with an initial power spectrum of the form P∝ k m , where k is the wavenumber. The hydrodynamic condition is the same as in the $\theta$-Group Collaboration [Thornber et al., “Late-time growth rate, mixing, and anisotropy in the multimode narrowband Richtmyer–Meshkov instability: The $\theta$-group collaboration,” Phys. Fluids 29, 105107 (2017)] with a Mach ~1.86 shock and gamma-law = 5/3 fluids with Atwood number A = 0.5. The bubble amplitude h B is found to grow in two stages. Initially, h B undergoes a phase-inversion and grows linearly in time (t) at a rate consistent with a linear theory. Asymptotically, h B grows as a power law $~t^{\theta}$ when $\langle{k}\rangle{|h_B|}$ > O(1), where $\langle{k}\rangle$ is the average wavenumber for the initial spectrum. The RMI behavior in simulations and modeling agree over a wide range of exponent m, spectral width, initial amplitude, and time. The agreement is quantified objectively using statistical analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

GeoMicro3D: A novel numerical model for simulating the reaction process and microstructure formation of alkali-activated slag

Highlights: • The GeoMicro3D model was innovatively developed for simulating the reaction process and microstructure formation of AAS. • In GeoMicro3D, the dissolution of slag and reactions of aqueous ions are fundamentally described in the local lattice cell. • A novel approach is proposed to speed up the nucleation simulations. • GeoMicro3D was implemented and verified with the relevant experimental data and thermodynamic calculation results. For the first time, this study developed a novel model, named GeoMicro3D, to simulate the reaction process and microstructure formation of alkali-activated slag. The GeoMicro3D model consists of four modules that are designed to simulate, respectively: (i) the initial spatial distribution of real-shape slag particles in alkaline activator, (ii) the dissolution of slag and diffusion of ions via the transition state theory and lattice Boltzmann method, respectively, (iii) the spatial distribution of reaction products using a nucleation probability theory, and (iv) the chemical reactions with thermodynamic modelling. Afterwards the GeoMicro3D model was implemented and verified. The simulation results were discussed and compared with the relevant experimental data and thermodynamic calculation results using GEMS. A good agreement was found in the comparisons, showing the strong simulation capability of GeoMicro3D.

36 MATERIALS SCIENCE↗

Internally cooled membrane-based absorber for dehumidification and water heating: Validated model and simulation study

This report presents a comprehensive model for advanced membrane-based absorber components accounting for three separate fluid streams, one of them providing internal cooling, with complex flow patterns. The model was implemented with various liquid desiccants and their properties (including LiCl, CaCl 2 , and [emim][OMS] ionic liquid [IL]). The model has been validated using data from two laboratory prototype absorbers, one with CaCl 2 as the working fluid and one with an IL as the working fluid. Entropy analysis was further carried out to verify the model and understand effect of internal cooling on the entropy variation of three fluid streams. The developed model and codes are expected to enable detailed configuration optimization and provide in-depth understanding of three-fluid heat and mass exchanger (HMX) performance. The paper also describes parametric studies of the HMX components in utilizing the latent heat removed in space cooling to heat water and numerically explores the unique benefit of its application in a semi-open absorption heat pump water heater. The results show that a single three-fluid HMX has the potential to achieve simultaneous dehumidification and water heating efficiently at cost effectiveness, particularly in hot and humid climates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Simulation and Modeling of Time-Resolved X-Ray Detector for the Saturn Accelerator

In this work, we present the technology-aided computer design (TCAD) device simulation and modeling of a silicon p-i-n diode for detecting time-dependent X-ray radiation. We show that the simulated forward and reverse breakdown current–voltage characteristics agree well with the measured data under nonradiation environment by only calibrating carrier lifetimes for the forward bias case and avalanche model critical fields for the reverse bias condition. Using the calibrated parameters and other nominal material properties, we simulated the radiation responses of the p-i-n diode and compared with experimental data when the diode was exposed to X-ray radiation at Sandia’s Saturn facility and the Idaho State University (ISU) TriMeV facility. For Saturn’s Gaussian dose-rate pulses, we show three findings from TCAD simulations. First, the simulated photocurrents are in excellent agreement with the measured data for two dose-rate pulses with peak values of 1.16×10 10 and 1.88×10 10 rad(Si)/s. Second, the simulation results of high dose-rate pulses predict increased delayed photocurrents with longer time tails in the diode electrical responses due to excess carrier generation. Third, simulated peak values of diode radiation responses versus peak dose rates at different bias conditions provide useful guidance to determine the dose-rate range that the p-i-n diode can reliably detect in experiment. For TriMeV’s non-Gaussian dose-rate pulse, our simulated diode response is in decent agreement with the measured data without further calibration. We also studied the effects of device geometry, recombination process, and dose-rate enhancement via TCAD simulations to understand the higher measured response in the time after the peak dose-rate radiation for the p-i-n diode exposed to TriMeV irradiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Modelica-based modeling and simulation of district cooling systems: A case study

While equation-based object-oriented modeling language Modelica can evaluate practical energy improvements for district cooling systems, few have adopted Modelica for this type of large-scale thermo-fluid system. Further, to our best knowledge, district cooling modeling studies have yet to include hydraulics in piping networks alongside plant models featuring realistic mechanical systems and controls. These are critical details to include when looking to make energy and control improvements in many physical system installations. To fill these gaps, this study released new open-source district cooling models at the Modelica Buildings Library and leveraged these models for a real-world case study at the University of Colorado Boulder. Here, the site includes six buildings connected to a central chiller plant featuring a waterside economizer. Several energy saving strategies are pursued based on the validated model, including control setpoint optimization, equipment modification, and pump setpoint adjustments. Results indicate that a combination of the studied measures can save the campus annually 84.6 MWh of energy, 8.9% of electricity costs, 58.0 metric tons of carbon dioxide emissions, while the waterside economizer cuts down chillers’ run times by 201 days/year, reducing maintenance costs and extending chiller life.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modeling and simulation of transitional Rayleigh–Taylor flow with partially averaged Navier–Stokes equations

In this work, the partially averaged Navier–Stokes (PANS) equations are used to predict the variable-density Rayleigh–Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number 500. This is a prototypical problem of material mixing, featuring laminar, transitional, and turbulent flow, instabilities and coherent structures, density fluctuations, and production of turbulence kinetic energy by both shear and buoyancy mechanisms. These features pose numerous challenges to modeling and simulation, making the RT flow ideal to develop the validation space of the recently proposed PANS Besnard–Harlow–Rauenzahn-linear eddy viscosity model closure. The numerical simulations are conducted at different levels of physical resolution and test three approaches to set the parameters $f_\phi$ defining the range of physically resolved scales. The computations demonstrate the efficiency (accuracy vs cost) of the PANS model predicting the spatiotemporal development of the RT flow. Results comparable to large-eddy simulations and direct numerical simulations are obtained at significantly lower physical resolution without the limitations of the Reynolds-averaged Navier–Stokes equations in these transitional flows. The data also illustrate the importance of appropriate selection of the physical resolution and the resolved fraction of each dependent quantity $\phi$ of the turbulent closure, $f_\phi$. These two aspects determine the ability of the model to resolve the flow phenomena not amenable to modeling by the closure and, as such, the computations’ fidelity.

Navier Stokes equations↗

A coupled DEM-IMB-LBM model for simulating methane hydrate exploitation involving particle dissolution

The coupled discrete element and lattice Boltzmann method using an immersed moving boundary scheme was extended to simulate methane hydrate exploitation involving mass transport and particle dissolution. In this coupled DEM-IMB-LBM model, a new Dirichlet-type thermal boundary condition is extended to simulate moving curved boundaries with constant concentration. A novel periodic boundary including an efficient searching algorithm for particle contact is proposed to reduce the computational cost and boundary effect. So, this model is validated by two numerical examples: a circular particle with concentration convection-diffusion moving in a horizontal channel and mass transport from a cylinder particle in a simple shear flow. The numerical results obtained from the proposed model agree well with previous studies. To further demonstrate the capacity of the proposed model, simulations of methane hydrate exploitation including two formations in marine sediments are carried out. The numerical results indicate that the coupled DEM-IMB-LBM is not only capable of simulating the dissolution of hydrate particles at the grain level, but also recover the sand erosion and migration process in a fundamental perspective during the methane hydrate exploitation process.

42 ENGINEERING↗

EGS Collab Modeling and Simulation Working Group Teleconference Series (1-98)

This submission contains the presentation slides and recordings from the first 98 EGS Collab Modeling and Simulation Working Group teleconferences. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗

Status of Research Activities on LVDTs: Modeling and Simulation Development and Performance Characterization of Commercial LVDTs

FY-22 research focused on two major areas. The first area was modeling and simulation with linked testing that will advance the Linear Variable Differential Transformers (LVDT) technology for use by stakeholders requiring LVDTs in upcoming irradiation tests. The second area focused on testing LVDTs purchased from U.S. suppliers that were investigated in FY-21. These cross-cutting development activities will ensure stakeholders have the current state-of-the-art LVDT-based technologies for deployment in future irradiation tests. Two stand-alone reports are included in the appendixes. The purpose of this report is to provide a convenient body of work to access the reports and to evaluate the data and conclusions provided. Appendix A, “Testing of the RDP Translation Transducer,” contains a complete evaluation of an RDP Electrosense linear transducer that has the capability of replicating the form, fit, and function of LVDTs currently being used at Idaho National Laboratory (INL). Appendix B is an extract from the report, “Advanced Sensors and Instrumentation Program at Boise State University (BSU), August 2022, Linear Variable Differential Transformers.” This report contains modeling and testing results conducted at BSU and INL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Finding Real Uncertainties From Physical Simulations

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗

Clump survival and migration in VDI galaxies: an analytical model versus simulations and observations

ABSTRACT We address the nature of the giant clumps in high-z galaxies that undergo violent disc instability, distinguishing between long-lived and short-lived clumps. We study the evolution of long-lived clumps during migration through the disc via an analytical model tested by simulations and confront theory with CANDELS-HST observations. The clump ‘bathtub’ model, which considers gas and stellar gain and loss, involves four parameters: the accretion efficiency α, the star formation rate (SFR) efficiency ϵd, and the outflow mass-loading factors for gas and stars, η and ηs. The corresponding time-scales are comparable to the migration time, two-three orbital times. The accretion-rate dependence on clump mass, gas, and stars, allows an analytical solution involving exponential growing and decaying modes. For the fiducial parameter values there is a main evolution phase where the SFR and gas mass are constant and the stellar mass is rising linearly with time. This makes the inverse specific SFR an observable proxy for clump age. When η or ϵd are high, or α is low, the decaying mode induces a decline of SFR and gas mass till the migration ends. Later, the masses and SFR approach an hypothetical exponential growth with a constant specific SFR. The model matches simulations with different, moderate feedbacks, both in isolated and cosmological settings. The observed clumps agree with our predictions, indicating that the massive clumps are long-lived and migrating. A challenge is to model feedback that is non-disruptive in massive clumps but suppresses SFR to match the galactic stellar-to-halo mass ratio.

Dekel, Avishai↗

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

Development of a numerical model for simulating stress corrosion cracking in spent nuclear fuel canisters

Abstract Prediction and detection of the chloride-induced stress corrosion cracking (CISCC) in Type 304 stainless steel spent nuclear fuel canisters are vital for the lifetime extension of dry storage canisters. This paper conducts a critical review that focuses on the numerical modeling and simulation on the research progress of the CISCC. The numerical models emphasizing the residual stress, susceptible microstructure, and corrosive environment are summarized individually. Meanwhile, the simulation studies on the role of hydrogen-assisted cracking are reviewed. Finally, a multi-physical numerical model, which combines the different fields is proposed based on our recent investigation.

Materials Science↗

Transient Modeling and Simulation of a Generic Stable Salt Reactor

A SAM system-level model of a generic stable salt reactor has been developed to investigate thermal-hydraulic behavior and safety performance under steady and transient conditions. The model integrates information generated from a reactor physics analysis using PROTEUS and PERSENT, and a computation fluid dynamics (CFD) analysis using STAR-CCM+. A loose, iterative coupling scheme between PROTEUS and SAM is implemented to calculate the equilibrium power and temperature distributions in the steady-state critical core condition. The converged steady-state model is then used in PERSENT to calculate the four reactivity feedback temperature coefficients (Doppler, fuel density, coolant density, and core radial expansion) and kinetic parameters that are needed in SAM to model the temperature feedback effects in transient simulations. Within the fully enclosed liquid fuel pins, natural convection is the dominant heat transfer mechanism. The STAR-CCM+ model of the fuel pin considers conjugate heat transfer from the liquid fuel salt to the pin cladding and external reactor coolant. The CFD results of the axial and radial temperature profiles are used to empirically determine an effective fuel salt thermal conductivity in the SAM fuel pin model so that the temperatures predicted by the SAM model match as closely as possible the CFD results. In the central region of the fuel pin, the effective thermal conductivity is as high as similar to 60 times the physical fuel salt thermal conductivity. The whole-plant SAM model is then used to simulate an unprotected station blackout transient. The results of this simulation showed that the large negative fuel axial expansion reactivity feedback reduces fission power to similar to 2.4% nominal power. The core is cooled by natural circulation, which removes heat in the core to the emergency heat removal system, and ultimately, to the ambient. However, peak fuel salt and cladding temperatures can potentially reach as high as 1500 K, albeit briefly, if the shutdown mechanism fails to operate.

stable salt reactor; transient simulations; system↗

Cascade Models in Simulation of Extended Heavy Targets Irradiated by Accelerated Proton and Deuteron Beams

The paper presents a survey of the main numerical models used for simulation of interaction of accelerated particle beams with target nuclei. These models form the core of the software for simulation of various experiments and experimental facilities both for scientific and applied purposes. The beam and target parameters considered in detail in this study (protons and deuterons with energies from 0.66 to 4 AGeV and bulk U targets) cover the range of interest in development of new concepts of nuclear power production aided by accelerated particle beams.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗