Simulation Time Research Articles

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Optimising building performance for a resilient Future: A Multi-Objective approach to Net Zero energy strategies

The escalating impacts of climate change on urban environments underscore the imperative for energy-efficient buildings. As the building sector constitutes approximately 40% of global energy consumption, the implementation of NZE designs is critical for emission reduction. This study utilised Building Performance Optimisation methodologies alongside a multi-stage optimisation framework to evaluate energy consumption, Life cycle cost and carbon emissions under various climate scenarios (RCP 2.6, 4.5, and 8.5) in three Algerian cities—Constantine (semi-arid), Algiers (Mediterranean), and Ghardaïa (arid).The results revealed that the integration of passive, active, and renewable energy strategies significantly curtailed energy consumption and CO2 emissions across different climatic contexts. Specifically, passive measures achieved reductions in energy use ranging from 39% in Ghardaïa to 15% in Constantine. Active systems, including high-efficiency HVAC technologies, resulted in energy savings of 59% in Ghardaïa, 52% in Algiers, and 47% in Constantine. Furthermore, renewable energy sources, with a focus on solar photovoltaics, contributed additional reductions: 44% in Algiers, 26% in Ghardaïa, and 20% in Constantine. The multi-stage optimisation approach also enhanced computational efficiency, significantly reducing simulation time and facilitating the rapid identification of optimal strategies.These findings highlight the efficacy of integrating a range of energy strategies to achieve NZE. Nevertheless, while these systems demonstrate robust performance under current conditions, their effectiveness diminishes under future climate scenarios, underscoring the necessity for adaptive and resilient design approaches. Ongoing optimisation and innovation will be essential to maintaining the efficacy of NZE strategies in the face of extreme future climate conditions.

Mixed equilibrium/nonequilibrium effects govern surface mobility in polymer glasses

Using angle-resolved X-ray photoelectron spectroscopy, sum-frequency generation vibrational spectroscopy, contact angle measurements, and molecular dynamics simulations, we verify that the glass transition temperature (Tg) of polymer glass is lower near the free surface. However, the experimental Tg-gradients showed a linear variation with depth (z) from the free surface, while the simulated equilibrium Tg-gradients exhibited a double exponential z-dependence. In typical simulations, Tg is determined based on the relaxation time of the system reaching a prescribed threshold value at equilibrium. Conversely, the experiments determined Tg by observing the unfreezing of molecular mobility during heating from a kinetically arrested, nonequilibrium glassy state. To investigate the impact of nonequilibrium effects on the Tg-gradient, we reduced the thermal annealing time in simulations, allowing the system to fall out of equilibrium. We observe a decrease in the relaxation time and the emergence of a modified z-dependence consistent with a linear Tg-gradient near the free surface. We further validate the impact of nonequilibrium effects by studying the dependence of the Tg on the heating/cooling rate for polymer films of varying thickness (h). Our experimental results reveal significant variations in the Tg-heating/cooling rate dependence with h below the bulk Tg, which are also observed in simulation when the simulated system is not equilibrated. We explain our findings by the reduction in mass density within the inner region of the system under nonequilibrium conditions, as observed in simulation, and recent research indicating a decrease in the local Tg of a polymer when placed next to a softer material.

Dynamic fluid flow model for phosphate slurry pipeline: OCP main pipeline as case study

The transportation of phosphate slurry through large-scale pipelines presents significant challenges due to the complex behavior of multiphase flows, particularly with varying solid content, density, and dynamic viscosity. Efficient and accurate prediction of flow behavior is critical for optimizing the operation of such pipelines. This work aims to develop a dynamic computational model to simulate phosphate slurry flow in pipelines. The case study focuses on the OCP Group’s main slurry pipeline, which links the mining sites at Khouribga to the industrial plants at Jorf Lasfar, Morocco. This pipeline system spans a total length of 187.124 km, consisting of 5237 pipes with an inner diameter ranging from 0.8546 m to 0.8578 m, and features several elevation changes along its ground level. Using a section-averaged, dynamic approach and the Finite Volume scheme, the model computes essential flow parameters, including density, dynamic viscosity, and pressure, for the incompressible and non-Newtonian slurry and process water flows. The model’s accuracy is validated against on-site measured data, showing an average deviation of ±0.32% for outlet density, and below 10% for pressure along the pipeline, underscoring the model’s reliability. Additionally, a sensitivity analysis was conducted to illustrate the impact of key parameters on the predicted head losses and pressures along the pipeline. This analysis shows that the slurry viscosity is the most critical parameter, significantly influencing these predictions. This model provides high accuracy and reasonable CPU time for real-time simulation and monitoring, while also offering significant potential for optimizing pipeline operations and ensuring the reliability of phosphate transport.

Predicting crack nucleation in commercially pure titanium using orientation imaging microscopy and machine learning

Due to the prohibitively long experimental and simulation times, dwell fatigue (DF) failure prediction in titanium and its alloys is a challenging task. Since most of these failures have a microstructural level origin, this work focusses on utilizing minimal experiments and machine learning for predicting failure initiation points in a given microstructure. Failure initiation points in commercially pure titanium were identified using interrupted tensile and DF tests. Orientation imaging data was used to train a Random Forest model to calculate the relative importance of various grain orientation-based features to crack nucleation. Subsequently a predictive model for identifying locations that are likely to form a DF crack in a microstructure is developed.

Analyzing hydroxyl radical accessibility related to hydrogen abstraction in a DNA sugar moiety using molecular dynamics simulations

Exposure of water to ionizing radiation induces OH radical formation. Within cellular environments, the presence of OH radicals can stimulate the abstraction of hydrogen atoms from the sugar backbone of DNA. Subsequent damage to DNA structures leads to various diseases. Multiple studies have elucidated this phenomenon, especially computational studies examining the differences in the degree of abstraction between the sugar hydrogens (H1′, H2′, H2″, H3′, H4′, H5′, and H5″). However, the details of this phenomenon have not yet been clarified. It is rare for OH radicals to approach DNA within the simulation time, making it difficult to adequately sample the configurations in which OH radicals immediately precede the abstraction of the hydrogen atoms. To address this problem, we performed molecular dynamics simulation to calculate the relative accessibility by putting a potential on nucleotides and OH radicals. As a result, we found that the accessibility of OH radicals to each hydrogen atom differs from that of water molecules as solvents. A more detailed accessibility analysis revealed that the angle of the OH radicals approaching the hydrogen atoms of ribose and the energy barrier for abstracting the hydrogen atoms can be considered to improve the correspondence with the experimental data. Moreover, we found that the behavior of water molecules and OH radicals toward accessibility to DNA differs significantly and showed that the factors are related to the physicochemical properties of water molecules and OH radicals, as well as the structure of DNA.

Physics simulation capabilities of LLMs

Large Language Models (LLMs) can solve some undergraduate-level to graduate-level physics textbook problems and are proficient at coding. Combining these two capabilities could one day enable AI systems to simulate and predict the physical world. We present an evaluation of state-of-the-art (SOTA) LLMs on PhD-level to research-level computational physics problems. We condition LLM generation on the use of well-documented and widely-used packages to elicit coding capabilities in the physics and astrophysics domains. We contribute ∼50 original and challenging problems in celestial mechanics (with REBOUND), stellar physics (with MESA), 1D fluid dynamics (with Dedalus) and non-linear dynamics (with SciPy). Since our problems do not admit unique solutions, we evaluate LLM performance on several soft metrics: counts of lines that contain different types of errors (coding, physics, necessity and sufficiency) as well as a more educational’ Pass-Fail metric focused on capturing the salient physical ingredients of the problem at hand. As expected, today's SOTA LLM (GPT4) zero-shot fails most of our problems, although about 40% of the solutions could plausibly get a passing grade. About 70%–90% of the code lines produced are necessary, sufficient and correct (coding & physics). Physics and coding errors are the most common, with some unnecessary or insufficient lines. We observe significant variations across problem class and difficulty. We identify several failure modes of GPT4 in the computational physics domain, such as poor physical units handling, poor code versioning, tendency to hallucinate plausible sub-modules, lack of physical justification for global run parameters (e.g., simulation time, or upper-lower bounds for parametric exploration) and inability to define steady-state or stopping conditions reliably. Our reconnaissance work provides a snapshot of current computational capabilities in classical physics and points to obvious improvement targets if AI systems are ever to reach a basic level of autonomy in physics simulation capabilities.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.

Finite-element Adaptive Meshing Statistics of Liquid Crystal Coaxial Phase Shifters for mmW Electronics and THz Photonics Beyond Display: A Comparative Study

Sub-mmW and THz frequencies offer ultra-high-speed communications for next-generation 5G/6G networks, wherein research advancing the understanding will benefit the electronics and photonics community. Specifically, meshing resolutions across varying frequency ranges and material properties are central to the solution accuracy, reliability, and cost (memory and time) of computational mmW and THz simulations, particularly for the emerging reconfigurable coaxial phase shifters employing nematic liquid crystals (LCs) as tunable media. In the present study, a comparative meshing statistics analysis is conducted for two devices designed for 60 GHz and 0.3 THz, respectively. Each design features two distinct tuning states (LC permittivity of 2.754 and 3.3, respectively), all pertinent to the coaxial TEM (Transverse Electromagnetic) mode. By quantifying the broadband meshing and solution statistics of diverse frequencies and dielectric tuning states for the first time, we establish memory-conserving computational metrology involving reconfigurable coaxial devices operationalized with LC-filled tunable dielectrics tailored for mmW electronics and THz photonics. Full Text: PDF References M. Makowski, I. Ducin, K. Kakarenko, J. Suszek, A. Kowalczyk, "Performance of the 4k phase-only spatial light modulator in image projection by computer-generated holography", Phot. Lett. Poland 8, 1 (2016). CrossRef J.F. Li, H.R. Li, "Liquid Crystal-Filled 60 GHz Coaxially Structured Phase Shifter Design and Simulation with Enhanced Figure of Merit by Novel Permittivity-Dependent Impedance Matching", Electronics 13, 3 (2024). CrossRef J.F. Li, H.R. Li, "Modeling 0.3 THz Coaxial Single-Mode Phase Shifter Designs in Liquid Crystals with Constitutive Loss Quantifications", Crystals 14, 4 (2024). CrossRef J.F. Li, D.P. Chu, "Liquid Crystal-Based Enclosed Coplanar Waveguide Phase Shifter for 54–66 GHz Applications", Crystals 9, 12 (2019). CrossRef Z. Cendes, "The development of HFSS", USNC-URSI Radio Science Meeting (Fajardo, IEEE 2016). CrossRef T. Vaupel, "Accuracy And Modeling Improvements For An Integral Equation Framework Applied To Thin Layer Microstrip And Substrate Integrated Waveguide (Siw) Structures", EuCAP (Copenhagen, IEEE 2020). CrossRef L.J. Jiang, W.C. Chew, "Low-frequency fast inhomogeneous plane-wave algorithm (LF-FIPWA)", Microwave Opt. Tech. Lett. 40, 2 (2004). CrossRef J. Zhu, D. Jiao, "Eliminating the Low-Frequency Breakdown Problem in 3-D Full-Wave Finite-Element-Based Analysis of Integrated Circuits", IEEE Trans. Microw. Theory Tech. 58, 10 (2010). CrossRef

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Minimum Energy Atomic Deposition: A novel, efficient atomistic simulation method for thin film growth

Thin-film growth is an area of research concerned with complex phenomena happening at atomic scales. Therefore, molecular simulation has been an important tool to confront experimental results to theoretical assumptions. However, the traditional thin film growth simulation methods, i.e., Molecular Dynamics (MD) and kinetic Monte-Carlo (kMC) and combinations thereof, suffer from limitations inherent to their design, i.e., limitations in system size and simulation time for MD and predetermined reaction rates and reaction sites for kMC. Consequently, it is practically impossible to simulate the evolution of polycrystalline growth resulting in ∼100nm thick films with realistic stress fields and defect structures, such as grain boundaries, stacking faults, etc. In this work, we propose a versatile and efficient atomistic simulation method (Minimum Energy Atomic Deposition) which works by direct insertion of atoms at points of minimal potential energy through efficient scanning of candidate positions and rapid relaxation of the system. This method allows simulating ≥100nm film thickness while mimicking experimental growth rates and high crystallinity and low-defect concentration and enables in-depth studies of atomic growth mechanisms, the evolution of crystal defects, and residual stress build-up. We demonstrate the efficiency and versatility of the method through the deposition of Al on Si, Al on Al, and Si on Si. The simulation results are systematically compared with experimental observations of thin-film deposition, yielding consistent observations. The method has been implemented in open-source LAMMPS software, making it easily accessible to the research community.

A Novel Approach Integrating the Method of Characteristics with Large Time-Step Scheme (MOC-LTS) for Efficient Transient Flow Simulation in Liquid Pipelines

Water hammer incidents pose a significant risk to the safety and stability of pipeline operations. Therefore, rapid and precise transient flow simulation is essential for efficiently developing scientific water hammer control strategies. Nevertheless, the prevailing transient flow simulation methods for liquid pipelines predominantly employ explicit schemes to solve transient flow control equations, necessitating adherence to the stability criterion of Courant-Friedrichs-Lewy (CFL) ≤ 1. This results in limited time step sizes, which in turn constrains computational efficiency, particularly when simulating hydraulic behavior in large-scale pipeline networks. In this paper, a novel approach integrating the method of characteristics (MOC) with a large time-step scheme (LTS) is proposed to enable rapid and accurate simulation of transient flow in liquid pipelines. The proposed approach discretizes the computational domain into contiguous control volumes, ategorizing them as either boundary or internal control volumes depending on whether they are affected by boundary conditions within a large time step. For internal control volumes, an LTS scheme based on the first-order Godunov format is employed to improve computational efficiency. For boundary control volumes, MOC is applied iteratively to accurately capture boundary dynamics until the simulated time matches that of the internal control volumes. Quantitative analysis is conducted to verify the performance of the proposed approach. The results confirm that the proposed approach outperforms the MOC in computational efficiency while maintaining minimal accuracy loss.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

A parallel compositional reservoir simulator for large-scale CO2 geological storage modeling and assessment

Storing CO2 in deep aquifers and depleted gas reservoirs is an effective way to achieve carbon neutrality. However, the numerical simulation of CO2 storage in these formations is challenging due to the complexity of gases-brine systems. The number of gas species included in the gases-brine fluid models of existing simulators cannot meet the rapidly evolving CO2 sequestration scenarios. To address this intricate issue, we developed a three-dimensional fully implicit parallel CO2 geological storage simulator (PRSI-CGCS) on distributed-memory computers based on our in-house parallel platform. This simulator uses a compositional fluid model with a diverse range of gas species, including CO2, C1 ~ C3, N2, H2S, as well as newly added gases H2 and O2, which may be encountered in geological CO2 storages. Besides, we provide more suitable scaling factors for different gases in the stability analysis bypassing (SAB) method to accelerate the gases-brine phase equilibrium calculations. PRSI-CGCS does not incorporate energy conservation equations, and salt precipitation or dissolution is also not considered. Numerical experiments show that our simulator is scalable, robust and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster. Besides, after our modification on scaling factors, the SAB method can reduce the number of stability analyses by 61.39 % to 88.71 %, thereby reducing simulation time. Furthermore, case studies indicate that injecting O2 and H2 along with CO2 reduces the stability or capacity of CO2 storage and increases the pressure required for injection. However, this impact is not significant when the impurity content is less than 10 %.

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

Online controller optimization for a voltage-doubler boost rectifier with high power factor

ABSTRACT The design of converters is essential for the proper use of electrical energy. Half-bridge topology represents a suitable conversion scheme to implement a boost rectifier converter. Consequently, this article proposes the parameter optimization of a proportional-integral-derivative (with filter) controller for a half-bridge boost rectifier with a high power factor. Regarding the high simulation time for the converter, stochastic hill-climbing and simulated annealing algorithms are employed for the optimization process. The results display that the optimized controller allows suitable values for the power factor and output waveform. The controller parameters obtained through optimization algorithms result in a high power factor of 0.9934 when the half-bridge boost rectifier is operating at full load.

Piecewise-scheduled thrust command control for in-service thrust performance improvement of gas turbine aero-engines: A hybrid fast design approach

Gas turbine aero-engines including turbofans, as the dominant powerplant for modern civil aircraft, convert the fossil energy in the jet fuel to propulsive forces via the thermo-dynamic cycle. Unfortunately, thrust regulation capabilities of gas turbine aero-engines are inevitably affected by uncertainties in measurements and gas path degradation during the life cycle, while quantification efforts for these uncertainties by traditional random analysis methods are usually considerable. In this paper, a piecewise-scheduled thrust command controller is proposed based on the improvement of the industrial baseline controller and current measurement levels, aiming at enhancing in-service thrust performance within a tolerable computational burden for engine fleets against these uncertainties. The proposed controller is equipped with a bank of embedded thrust maps for different flight cycle segments, which is fulfilled by a hybrid fast design approach incorporating a random analysis part and an analytical design part, as a new uncertainty quantification method. An industrial baseline controller with the identified thrust mode is also designed as the comparison basis. Simulations are carried out on a validated aero-thermal turbofan engine model with publically accessible uncertainty statistics. Simulation time for constructing the embedded thrust maps of the proposed controller is decreased by 99.8% on a desktop computer, compared to Monte-Carlo approach. Meanwhile, simulation results show that the proposed controller owns a bounded and tight thrust distribution for both new and severely degraded engine fleets at the take-off state within the permitted safety margin of the engine, which mitigates the significant under-thrust consequences from the baseline controller. Hence, the uncertainty control benefits of the proposed controller and its design efficiency are guaranteed.

Research on Internal Flooding Analysis of Small Modular Reactors Based on Particle Method

Abstract The analysis of internal water flooding in nuclear power plants is a necessary part of safety review in the approval process of nuclear power plant construction projects. According to relevant regulations, it is necessary to conduct water flooding spread analysis on pipelines, water tanks, or pools filled with liquid in nuclear power plant buildings, analyze the spread path and water flooding height. This article analyzes and calculates the flow characteristics of water in a nuclear power plant building using a fluid calculation method based on the Lagrangian method (particle method). Taking the small modular reactor JR building as a case study, it analyzes the spread and height of four water flooding sources inside the building. According to the simulation time of 1800S, the results show that the flooding caused by the No. 2 water source is the most severe, with a flooding height of 1.367m. Drainage or preventive measures need to be taken to prevent the pipeline from breaking accidents; The flooding sources 1, 2, and 4 have a relatively mild impact, resulting in a lower flooding height and will not have a significant impact on important safety equipment, with little impact on the safety of nuclear power plants. The CFD analysis method was used to conduct water flooding analysis for nuclear power plants, which has the characteristics of strong adaptability and good visualization. It can dynamically simulate the entire process of water flooding analysis and better guide the design of water flooding protection for nuclear power plants.

A critical assessment of geological weighing lysimeters: Part 2—Modelling field scale soil moisture storage and hydrological fluxes

AbstractLand surface models (LSMs) are used to simulate the terrestrial component of water, energy, and biogeochemical cycles. These simulations are useful for water resources management, drought and flood prediction, and numerical climate/weather prediction. However, the usefulness of LSMs are dependent by their ability to reproduce states and fluxes realistically. Accurate measurements of water storage are useful to calibrate and validate LSMs outputs. Geological weighing lysimeters (GWLs) are instruments that can provide field‐scale estimates of integrated total water storage within a soil profile. We use field estimates of total water storage and subsurface storage to critically evaluate two different land surface models: the Modélisation Environnementale communautaire—Surface Hydrology (MESH) which uses the Canadian Land Surface Scheme (CLASS), and the Structure for Unifying Multiple Modeling Alternatives: (SUMMA). These models have differences in how the processes and properties of the land surface are represented. We attempted to parameterize each model in an equivalent manner, to minimize model differences. Both models were able to reproduce observations of total water storage and subsurface storage reasonably well. However, there were inconsistencies in the simulated timing of snowmelt; depth of soil freezing; total evapotranspiration; partitioning of evaporation between soil evaporation and evaporation of intercepted water; and soil drainage. No one model emerged as better overall, though each model had specific strengths and weaknesses that we describe. Insights from this study can be used to improve model physics and performance.

Molecular dynamics approach to efficient hydrogen generation process of Co-B catalysts decorating lanthanides (La, Ce, Pr, Nd) supported by flat-sheet and twisted ThMoB4-type graphene from NaBH4 hydrolysis: Insights from non-self-consistent GFN1-xTB method

In this study, the promoter role on highly efficient hydrogen generation productivity and H2 formation mechanisms of Co-B-X catalysts modified by rare earths (X = La, Ce, Pr, Nd) supported by flat-sheet and twisted ThMoB4-type graphene from sodium borohydride (NaBH4) hydrolysis was investigated using molecular dynamics (MD) method based on non-self-consistent tight binding GFN1-xTB Method. The twisted ThMoB4-type graphene layer was constructed by adsorbing of ethylene carbonate (EC) molecule on armchair site of graphene surface with applying of geometric optimization process. The addition of Nd to CoB exhibited to higher H2 release compared to other CoB containing lanthanides (La, Ce and Pr) both flat-sheet and twisted ThMoB4-type graphene. While the number of H2 for Co-B-Nd is 14, the H2 amount is only 8 for Co-B-Ce supported flat-sheet graphene at the end of simulation time. Also, the placing of twisted graphene instead of flat-sheet in the catalytic complex led to about 36 % increase in H2 number for Co-B-Nd. The computational results revealed that the availability of the active sites, such as basicity of catalytic environment related to OH and H species and the mobility of Co atom, played an important role for catalytic activity and performance for H2 production. This work can provide new insight for experimental studies of Co-B-X (X = La, Ce, Pr, Nd) catalysts for hydrogen production in atomic-level and the creation of new hydrogen energy applications and facilitates for H2 generation efficiency.