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Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy
Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.
Anatomy of a fumarolic system inferred from a multiphysics approach
Fumaroles are a common manifestation of volcanic activity that are associated with large emissions of gases into the atmosphere. These gases originate from the magma, and they can provide indirect and unique insights into magmatic processes. Therefore, they are extensively used to monitor and forecast eruptive activity. During their ascent, the magmatic gases interact with the rock and hydrothermal fluids, which modify their geochemical compositions. These interactions can complicate our understanding of the real volcanic dynamics and remain poorly considered. Here, we present the first complete imagery of a fumarolic plumbing system using three-dimensional electrical resistivity tomography and new acoustic noise localization. We delineate a gas reservoir that feeds the fumaroles through distinct channels. Based on this geometry, a thermodynamic model reveals that near-surface mixing between gas and condensed steam explains the distinct geochemical compositions of fumaroles that originate from the same source. Such modeling of fluid interactions will allow for the simulation of dynamic processes of magmatic degassing, which is crucial to the monitoring of volcanic unrest.
Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries
The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.
Mechanical behavior simulation of MEMS-based cantilever beam using COMSOL multiphysics
This paper presents the studies of mechanical behavior of MEMS cantilever beam made of poly-silicon material, using the coupling of three application modes (plane strain, electrostatics and the moving mesh) of COMSOL Multi-physics software. The cantilevers playing a key role in Micro Electro-Mechanical Systems (MEMS) devices (switches, resonators, etc) working under potential shock. This is why they require actuation under predetermined conditions, such as electrostatic force or inertial force. In this paper, we present mechanical behavior of a cantilever actuated by an electrostatic force. In addition to the simplification of calculations, the weight of the cantilever was not taken into account. Different parameters like beam displacement, electrostatics force and stress over the beam have been calculated by finite element method after having defining the geometry, the material of the cantilever model (fixed at one of ends but is free to move otherwise) and his operational space.
Multi-fidelity uncertainty quantification strategies for large-scale multiphysics applications: PSAAP II particle-based solar energy receiver
The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence or improve the prediction accuracy of such simulations, the impact of uncertainties in model inputs, or even the structure of the models, on the quantities of interest must be measured. This, however, requires a computational budget that is a possibly large multiple of the cost of a single simulation, and thus may become infeasible for expensive simulation models featuring a large number of uncertain inputs and highly non-linear behavior. Therefore, this work explores multi-fidelity strategies to accelerate the estimation of the effect of uncertainties. The main idea behind multi-fidelity methods is to utilize cheaper, lower-fidelity models – than the intended high-fidelity, expensive model of the problem – to generate a baseline solution that together with relatively small number of high-fidelity simulations can lead to accurate predictions. The methods are briefly presented, and their performance assessed on an irradiated particle-laden turbulent flow case related to Stanford’s PSAAP II particle-based solar energy receiver.
Multiphysics simulation of microscale copper printing by confined electrodeposition using a nozzle array
3D printing of metals at the microscale and nanoscale is crucial to produce high-density interconnects and intricate structures in electronic devices. Conventional melting and sintering processes are not suitable for these scales due to a reliance on individual metal particles in the size range of tens of micrometers. Confined electrodeposition (CED) is an established alternative to conventional metal 3D printing processes in which an electrolyte is used to selectively induce deposition of the metal on the printing surface. However, commercialization and efficiency of this process have been limited due to a reliance on sub-micrometer nozzles to achieve desirable deposition rates and single nozzle to achieve uniformity of printed structures. Here, we address these challenges by computationally analyzing an array of microscale nozzles. We tailor the convection within the electrolyte to alter both deposition rate and geometric uniformity of the printed structures. The results show that for large nozzles the evaporation alone is not sufficient to obtain high deposition rates, yet an external pressure can be used to increase deposition and alter uniformity (thickness) of printed structures. Our results can be used to design and analyze new experiments toward parallel multi-nozzle deposition using CED toward high-throughput metal printing.
Rattlesnake: A MOOSE-Based Multiphysics Multischeme Radiation Transport Application
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Multiphysics Analyses of the Bottom Components of the 3D Printed Transformational Challenge Reactor
This research presents multi-physics analyses on the bottom components of the Transformational Challenge Reactor (TCR) facility. These components include the bottom axial reflector and the steel exit cone. The bottom axial reflector is made of pure silicon carbide elements hosting helium cooling channels These elements are 3D printed and therefore can host any arbitrary shape of the helium cooling channels. The design of the bottom reflector considers the neutronics and thermo-fluid dynamics performances as well as the manufacturing process optimization. More precisely, the best design of the bottom reflector reduces neutron leakage by avoiding straight cylindrical helium channels that facilitate neutron leakage, minimizes the helium flow pressure drop, and reduces the number of 3D printed silicon carbide pieces. The exit cone steel structure collects the hot helium from the bottom fuel assemblies and channels the cold helium to the top of the fuel assemblies. The steel simultaneous contact with hot and cold helium flows sets a large thermal gradient. Different designs of the exit cone are proposed to reduce the steel equivalent stress from the helium thermal load. The multi-physics analyses have been performed using Ansys Fluent, Ansys Mechanical, STAR-CCM+, and Serpent computer programs.
Advanced Multiphysics Code Coupling for Cladding Surface Thermocouples During Two-Phase Heat Transfer from Nuclear Fuel
Transient testing of nuclear fuel involves the evaluation of fuel performance under off-normal and accident conditions and is essential for proving design performance. Instrumentation included in such experiments commonly includes thermocouples attached to the outer surface of the cladding to provide temperature measurements throughout the transient. However, the presence of thermocouples on the cladding surface can alter the local heat transfer characteristics with the surrounding coolant. These localized effects can influence the temperature of the nearby cladding surface and introduce uncertainties in interpreting the thermocouple data. Understanding the impact of thermocouples attached to the outer surface of the cladding is crucial for accurate data interpretation as well as its effect on the thermomechanical behavior of the cladding. This paper presents a novel methodology for simulating the impact of outer cladding thermocouples during transient testing of nuclear fuels. The simulation framework leverages the thermal-hydraulic capabilities of RELAP5-3D coupled to the BISON fuel performance code through the RELAPCouplingApp interface. The methodology is compared against Accident Tolerant Fuel Reactivity Initiated Accident-1-E experiment performed at Idaho National Laboratory. The results reveal approximately 100°C difference between thermocouple-altered temperature and virgin cladding surface. The model overpredicts surface rewet time due to conservative correlations.
A Robust, Relaxation–Free Multiphysics Iteration Scheme for CMFD–Accelerated Neutron Transport k–Eigenvalue Calculations–I: Theory
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A Robust, Relaxation-Free Multiphysics Iteration Scheme for CMFD-Accelerated Neutron Transport k -Eigenvalue Calculations—II: Numerical Results
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Linear Source Approximation in MPACT for Efficient and Robust Multiphysics Whole-Core Simulations
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Stability and Performance of the X-CMFD Method for Multiphysics Reactor Calculations
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Verification of Griffin-Pronghorn-Coupled Multiphysics Code System Against CNRS Molten Salt Reactor Benchmark
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3D multiphysics modeling of superconducting cavities with a massively parallel simulation suite
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Interface learning of multiphysics and multiscale systems
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm and put forth a data-driven closure approach based on memory embedding to provide physically correct boundary conditions at the interface. To enable the interface learning for hyperbolic systems by considering the domain of influence and wave structures into account, we put forth the concept of upwind learning toward a physics-informed domain decomposition. The promise of the proposed approach is shown for a set of canonical illustrative problems. Here, we highlight that high-performance computing environments can benefit from this methodology to reduce communication costs among processing units in emerging machine-learning-ready heterogeneous platforms toward exascale era.
Fourier Analysis-Based Evolutionary Multi-Objective Multiphysics Optimization of Liquid-Cooled Heat Sinks
Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This article proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis-based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry is then compared with state-of-the-art solutions in literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform the conventional heat sink geometries.