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Several methods have recently been developed to solve multiphysics transients using the Improved Quasi-Static method and its derivatives. In order to address some perceived drawbacks of these methods, we have developed a new method for solving multiphysics transient calculations using the Predictor-Corrector Quasi-Static method. Our method involves computing reactivity feedback parameters during each transport timestep in order to enable reactivity feedback on the small timescale used by the point kinetics phase of the calculation. The advantage of this approach is that the transport solver does not need to store local flux information between time steps, potentially making it more appropriate for use with a Monte Carlo solver. We have demonstrated the accuracy of our method by solving a simple model problem that exhibits difficult multiphysics behavior. Additionally, we have compared our results against another recently published multiphysics coupling scheme, confirming that our approach does not negatively affect the accuracy of the transient solution.

The goal of this work was to calculate the impact of the delayed neutron precursor drift in fast spectrum Molten Salt Reactors (MSRs) using coupled solutions from the neutronics code PROTEUS and the computational fluid dynamics code Nek5000. Specifically, using a multiphysics approach to solve the effective delayed neutron fraction (βeff) or delayed neutron precursor distribution for reactors with flowing fuel salts would provide valuable information for transient simulations and safety assessments. Given the multiple options for the flux solution and geometric resolution/fidelity in PROTEUS, two approaches were developed and applied to various test cases: PROTEUS-NODAL/Nek5000 and PROTEUS-SN/Nek5000. For the former, the precursors are tracked in the built-in precursor drift model in PROTEUS-NODAL, whereas in the latter, Nek5000 directly tracks the precursors. Both approaches were used to solve a single test channel problem and showed excellent agreement in the calculated β eff . Separately, a 3D hourglass-shaped core was modeled using the PROTEUS-SN/Nek5000 approach. This problem was designed to demonstrate the capability of the discrete ordinates (S N ) solver and Nek5000 to model complex core designs with axially varying geometries and the ability for Nek5000 to track the precursors and calculate the resulting β eff . In addition, the Nek5000 calculations revealed the presence of recirculation zones in the hourglass design, which could lead to significant temperatures in the fuel salt and surrounding materials. These first coupled solutions show why these approaches may be necessary for not only predicting the precursor drift effect in fast MSRs but also for reactor design and performance assessments.

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

This review paper provides an overview of the literature for topology optimisation of fluid-based problems, starting with the seminal works on the subject and ending with a snapshot of the state of the art of this rapidly developing field. “Fluid-based problems” are defined as problems where at least one governing equation for fluid flow is solved and the fluid–solid interface is optimised. In addition to fluid flow, any number of additional physics can be solved, such as species transport, heat transfer and mechanics. The review covers 186 papers from 2003 up to and including January 2020, which are sorted into five main groups: pure fluid flow; species transport; conjugate heat transfer; fluid–structure interaction; microstructure and porous media. Each paper is very briefly introduced in chronological order of publication. A quantititive analysis is presented with statistics covering the development of the field and presenting the distribution over subgroups. Recommendations for focus areas of future research are made based on the extensive literature review, the quantitative analysis, as well as the authors’ personal experience and opinions. Since the vast majority of papers treat steady-state laminar pure fluid flow, with no recent major advancements, it is recommended that future research focuses on more complex problems, e.g., transient and turbulent flow.

This work presents the results for a coupled neutronic-thermalhydraulic-thermomechanic pin-level depletion calculation of a PWR fuel assembly using Serpent2-SUBCHANFLOW-TRANSURANUS. This tool is based on a semi-implicit depletion scheme with pin-by-pin feedback, mesh-based field exchange and an object-oriented software design. The impact of including fuel-performance capabilities is analyzed, with focus on high-burnup effects. The treatment of the Doppler feedback to the neutronics is examined as well, in particular the use of radial fuel-temperature profiles or radially averaged values.

Partial convergence of CMFD can help to stabilize multiphysics iteration schemes. In this paper, an efficient multiphysics iteration scheme with near-optimal partially convergent CMFD implemented in MPACT is presented. In the new scheme, the feedback intensity of the problem is automatically estimated, and the relative convergence of CMFD solver is adjusted accordingly. Numerical results show that MPACT with near-optimal partially convergent CMFD can have almost the same convergence rate in problems with feedback as those without feedback. For the problems tested here the run time may be reduced by more than 20% and up to 49% compared with that of current MPACT.

To address the safety risks caused by thermal runaway in lithium iron phosphate (LiFePO₄) energy storage batteries, and to improve the timeliness and accuracy of fault monitoring while ensuring the safe operation of energy storage systems, this study focuses on a 314 Ah liquid‐cooled battery module. It conducts a coupled gas‐mechanics‐acoustics multi‐physics simulation and experimental validation of the thermal runaway process. Mechanical response, acoustic propagation, and gas diffusion models were built using ABAQUS, COMSOL, and Ansys Fluent, respectively. Combined with experimentally measured stress peaks (1308 kg), acoustic signal characteristics, and hydrogen diffusion data, model inputs and validation were completed. The NSGA‐III multi‐objective optimization algorithm was applied to determine the optimal sensor configuration. The results show that: the thermal runaway stress is transmitted to the seventh cell in the module within 0.001 s, so the stress sensor should be placed on the large surface of this cell; the transient acoustic signal peak pressure during safety valve activation exceeds 17 Pa (sound pressure level > 118.6 dB), achieving millisecond‐level full coverage, and a single broadband acoustic sensor placed at the top center of the module is sufficient for monitoring; thermal runaway gas response is optimal in the top center area (Position 5), with the edge fault scenario detected first at 1.1 s and reaching the warning threshold (0.01 kmol/m³) at 1.6 s, while in the center fault scenario, the warning threshold is reached at 0.1 s. Gas sensors should therefore be preferentially arranged at the top center of the module. The sensor optimization layout strategy proposed in this paper provides theoretical and technical support for the safe operation of energy storage batteries and helps improve the effectiveness of fault monitoring in energy storage systems.

This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework. Key Points The paper describes the augmented Noah LSM The augmented Noah LSM allows multiphysics options (hence Noah‐MP) The Noah‐MP outperforms the original Noah LSM

The stationary-head prototypes unusually are designed using low cost manufacture and simple construction, without bolt or nut to join both the stationary-head and shell. The shell has four holes to supply hot/cold fluid, and next to the tube-sheet hole to supply cold/hot fluid, the position both of them are inside the stationary-head. The calculation of the dimensions of the heat exchanger aims to determine the quality of the heat exchanger based on the overall heat transfer coefficient, impurity factor, and the pressure drop that will occur.Calculations using the LMTD method, obtained that receive heat released has a large unity with time Q, then the heat received by cold fluid is Q = 4565.16 W, LMTD produced also shows the number 20, with a proven factor (F) is 1. Comparison obtained from the calculation of the tube side and shell side is the value of Re generated is greater than the value of Re on the shell side.

Electrical machines are important devices that convert electric energy into mechanical work and are widely used in industry and people’s life. Undesired vibrations are harmful to their safe operation. Reviews from the viewpoint of fault diagnosis have been conducted, while summaries from the perspective of dynamics is rare. This review provides systematic research outlines of this field, which can help a majority of scholars grasp the ongoing progress and conduct further investigations. This review mainly generalizes publications in the past decades about the dynamics and vibration of electrical machines. First the sources of electromagnetic vibration in electrical machines are presented, which include mechanical and electromagnetic factors. Different types of air gap eccentricity are introduced and modeled. The analytical methods and numerical methods for calculating the electromagnetic force are summarized and explained in detail. The exact subdomain analysis, magnetic equivalent circuit, Maxwell stress tensor, winding function approach, conformal mapping method, virtual work principle and finite element analysis are presented. The effects of magnetic saturation, slot and pole combination and load are discussed. Then typical characteristics of electromagnetic vibration are illustrated. Finally, the experimental studies are summarized and the authors give their thoughts about the research trends.