Parameters Of Numerical Model Research Articles

Leak-Rate Through Carbon Brush Seals: Experimental Tests Versus Predictions From a Porous Medium Approach

Abstract This study presents a detailed comparative analysis between experimental leakage flow rates and numerical predictions for carbon brush seals with long bristles, utilizing a porous medium model approach. A series of tests were carried out on a static rig (without rotor rotation). The experimental setup allows tests under various interference conditions, revealing significant insights into the flow behavior through the brush seal. A numerical model based on the Darcy-Forchheimer equation is developed to interpret the complex flow dynamics within the brush seal, accounting for viscous, compressible and inertial effects. The study evaluates the impact of brush deformation and porosity on flow resistance, leveraging experimental data to refine the numerical model parameters. This investigation not only deepens the understanding of brush seal flow physics but also improves the predictive accuracy of the numerical model in simulating operational conditions.

RFA-YOLOv8: Steel Plate Surface Defect Detection Algorithm Based On Improved YOLOv8

Abstract To address the issues in existing steel plate surface defect detection algorithms, such as low accuracy, numerous model parameters, and high computational complexity, this paper introduces a novel algorithm named RFA-YOLOv8, which is built on an enhanced version of YOLOv8n. The approach enhances the C2f module within the backbone network and incorporates a receptive field attention (RFA) mechanism to refine the convolution layer, leading to the development of the C2f_RFAConv structure. This advancement significantly boosts feature extraction capabilities in complex scenarios. Experimental results demonstrate that RFA-YOLOv8 achieves a 74.9% accuracy on the NEU-DET dataset, surpassing YOLOv8n by 1.8%, with minimal changes in model parameters and floating-point operations. Moreover, improvements in both recall and precision rates confirm that RFA-YOLOv8 delivers high detection accuracy and efficiency, making it valuable for industrial quality inspections.

Characterization of the active fault deformation zone of the Chegualin Fault in the alluvial plain of southwestern Taiwan

The activity of a creeping active fault poses significant challenges to engineering structures due to surface deformation. Therefore, quantifying the strain concentration caused by an active fault, delineating the extent and location of the Active Fault Deformation Zone (AFDZ), estimating long-term deformation trends, and predicting future deformations are crucial in the field of engineering geology.This study comprehensively integrates multi-timescale analytical methods by incorporating detailed geodetic data, rupture surveys, morphotectonic analysis, geological borehole data, biochronological data, radiocarbon dating, and Holocene uplift rate analysis to identify or confirm the locations of active faults and the long-term evolution trends of active deformation zones. Based on our findings, three active fault planes are identified, with one possibly being the main fault with the highest activity. Furthermore, by comparing long-term deformation rates derived from isochrone lines with short-term deformation rates obtained from leveling, we observe a risk of slip rate deficit. These findings have significant implications for engineering geology. As a general contribution, our study can serve as a site screening strategy for similar locations. Regarding the infrastructure we targeted, we provide critical input parameters for further numerical models (such as the trishear model) to simulate surface deformation, and offer essential design parameters for structures. Considering potential coseismic deformation on the investigated fault, these results are fundamental for future investigations or mitigation plans.

Analysis of Granite Deformation and Rupture Law and Evolution of Grain-Based Model Force Chain Network under Anchor Reinforcement

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite rock mass is essential. It can provide significant reference and support for the design of underground engineering, engineering safety assessment, the theory of rock mechanics, and resource development. In this study, indoor experiments are combined with numerical simulations to explore the impact of fracture dip angles on the mechanical behavior of unanchored and anchored granite samples from both macroscopic and microscopic perspectives. It also investigates the evolution of the anchoring and crack-stopping effect of rock bolts on granite containing fractures with different dip angles. The results show that the load-displacement trends, displacement fields, and debris fields from indoor experiments and numerical simulations are highly similar. Additionally, it was discovered that, in comparison to the unanchored samples, the anchored samples with fractures at various angles all exhibited a higher degree of tensile failure rather than shear failure that propagates diagonally across the samples from the regions around the fracture tips. This finding verifies the effectiveness of the numerical model parameter calibration. At the same time, it was observed that the internal force chain value level in the anchored samples is higher than in the unanchored samples, indicating that the anchored samples possess greater load-bearing capacity. Furthermore, as the angle αs increases, the reinforcing and crack-stopping effects of the rock bolts become increasingly less pronounced.

Optimization of Ansys CFX Input Parameters for Numerical Modeling of Pump Performance in Turbine Operation

The paper deals with the issue of determining the optimal setting of input variables in Ansys CFX for modeling pump flow in turbine operation (PAT). The pump model was created in Autodesk Inventor. The mesh for numerical simulations was created using Ansys Fluent Meshing, considering the mesh quality parameters’ skewness and aspect ratio. The Ansys CFX computational model was experimentally verified on an actual pump by measuring the performance parameters on a test circuit and using the PIV (particle image velocimetry) method. The research indicated that the most suitable setting for the model input variables was the inlet pressure and PAT flow rate combination. Another option was to adjust the pressure at the pump inlet and outlet. However, the calculation time in this case was up to 30% longer. The comparison of the model results with the experiment showed that the deviations in the numerical model performance values did not exceed 10% of the values measured on the test circuit. Only the calculated torque was 1.2 ± 0.13 Nm higher on average than the torque measured on the test circuit. This difference is most likely due to the simplification of the geometry of the computational mesh in order to reduce the computation time.

FDP-FL: differentially private federated learning with flexible privacy budget allocation

Abstract Federated learning (FL) as a privacy-preserving technology enables multiple clients to collaboratively train models on decentralized data. However, transmitting model parameters between local clients and the central server can potentially result in information leakage. Differentially private federated learning (DPFL) has emerged as a promising solution to enhance privacy. Nevertheless, existing DPFL schemes suffer from two issues: (i) most schemes that aim to achieve desired model accuracy may incur a high privacy budget. (ii) several schemes that consider the trade-off between privacy and accuracy by utilizing linear clipping bound may distort numerous model parameters. In this paper, we first propose FDP-FL, a flexible differential privacy approach for FL. FDP-FL introduces a novel series sum privacy budget allocation instead of uniform allocation and enables adaptive and nonlinear noise scale decay. In this way, a tight bound for cumulative privacy loss can be achieved while optimizing model accuracy. Then in order to mitigate gradient leakages caused by honest-but-curious clients and server, we further design client-level FDP-FL and record-level FDP-FL, respectively. Experimental results demonstrate that our FDP-FL improves model accuracy by $\sim $13.3% compared with the basic DP-FL under a fixed privacy budget and outperforms existing trade-off schemes with the same hyperparameter setting.

Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data

Masonry arches and vaults are common historic structural elements that frequently experience asymmetric loading due to seismic action or abutment settlements. Over the past few decades, numerous studies have sought to enhance our understanding of the structural behavior of these elements for the purpose of preventive conservation. The assessment of the structural performance of existing constructions typically relies on effective numerical models guided by a set of unknown input parameters, including geometry, mechanical characteristics, physical properties, and boundary conditions. These parameters can be estimated through deterministic optimization functions aimed at minimizing the discrepancy between the output of a numerical model and the measured dynamic and/or static structural response. However, deterministic approaches overlook uncertainties associated with both input parameters and measurements. In this context, the Bayesian approach proves valuable for estimating unknown numerical model parameters and their associated uncertainties (posterior distributions). This involves updating prior knowledge of model parameters (prior distributions) based on current measurements and explicitly considering all sources of uncertainties affecting observed quantities through likelihood functions. However, two significant challenges arise: the likelihood function may be unknown or too complex to evaluate, and the computational costs for approximating the posterior distribution can be prohibitive. This study addresses these challenges by employing Approximate Bayesian Computation (ABC) to handle the intractable likelihood function. Additionally, the computational burden is mitigated through the use of accurate surrogate models such as Polynomial Chaos Expansions (PCE) and Artificial Neural Networks (ANN). The research focuses on setting up numerical models for simple structural systems (tie-rods) and inferring unknown input parameters, such as mechanical properties and boundary conditions, through Bayesian updating based on observed structural responses (modal data, strains, displacements). The main novelties of this research regard, on the one hand, the proposal of a methodology for obtaining a reliable estimate of the axial force in ancient tie-rods accounting for different sources of uncertainty and, on the other hand, the application of ABC to obtain the structural identification inverse problem solution.

VLF oscillation mechanism analysis of underwater vehicle based on overlapping grid

ABSTRACT The present study aims to investigate the VLF oscillation mechanism of underwater vehicles motion by conducting numerical simulation on the AFF-1. The straight-line simulations of AFF-1 were performed using RANS equations with a Reynolds stress turbulence model at different speeds, and an initial flow field with converged resistance results was obtained. Meanwhile, the accuracy of the numerical model and input parameters were validated by the resistance results. The sinuous motion of AFF-1 was simulated based on dynamic fluid body interaction (DFBI) and overlapping grid. The results indicated that the inherent instability of the AFF-1 significantly impacts its motion dynamics. Specifically, when AFF-1was in straight-line motion at a constant forward velocity, it was subjected to the influence of pressure component of hydrodynamic force and moment, resulting in slow trajectory divergence motion of AFF-1. In scenarios where manual manipulation is employed, this instability leads to the emergence of VLF oscillations.

A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)

Abstract. A new calving algorithm is developed in the glacier model Elmer/Ice that allows unrestricted calving and terminus advance in 3D. The algorithm uses the meshing software Mmg to implement anisotropic remeshing and allow mesh adaptation at each time step. The development of the algorithm, along with the implementation of the crevasse depth law, produces a new full-Stokes calving model capable of simulating calving and terminus advance across an array of complex geometries. Using a synthetic tidewater glacier geometry, the model is tested to highlight the numerical model parameters that can alter calving when using the crevasse depth law. For a system with no clear attractor at a pinning point, the model time step and mesh resolution are shown to alter the simulated calving. In particular, the vertical mesh resolution has a large impact, increasing calving, as the frontal bending stresses are better resolved. However, when the system has a strong attractor, provided by basal pinning points, numerical model parameters have a limited effect on the terminus evolution. Conversely, transient systems with no clear attractors are highly influenced by the choice of numerical model parameters. The new algorithm is capable of implementing unlimited terminus advance and retreat, as well as unrestricted calving geometries, applying any vertically varying melt distribution to the front for use in conjunction with any calving law or potentially advecting variables downstream. In overcoming previous technical hurdles, the algorithm opens up the opportunity to improve both our understanding of the physical processes and our ability to predict calving.

Simulation-based optimization concept for integrated assessment models: Case study MEDEAS-world

Simulation based optimization concept has been so far presented at local and national energy systems planning levels. This method represents a systematic way of dealing with uncertainties and combinatorial complexity when using modelling tools of very large size with significant number of possible policy inputs, and could be useful to analyse policy recommendations in broader models integrating more dimensions than energy. For the first time, the concept is successfully demonstrated using a working integrated assessment model (IAM), although some attempts have been mentioned in literature previously. IAMs aim to link main features of society, economy and energy with the biosphere and atmosphere into one modelling framework. The concept has been demonstrated for the IAM MEDEAS-World using system dynamics platform Vensim (version 9.3.4.) for both simulation and optimization. For an optimization problem formulation, 12 decision variables are selected from numerous input model parameters, the gross domestic product per capita has been selected for a goal function from simulation result as quantifiable policy objective, while for the constraint CO2 emissions is selected as subject to binding targets. It is possible to compile the simulation code so that optimization running time is around 10 times shorter. Preliminary results show that it is possible to implement richer optimization problems without significant increases in the running time using personal computers. The main contribution of this work is to prove the concept of simulation-based optimization by a demonstration within a working IAM. This opens the way to promising future work to inform about optimal combinations of available policies within different storylines, as well as providing additional general understanding of the IAM which could allow to improve the model. The original Vensim code has been provided in the Appendix.

Post-liquefaction settlement analysis of shallow foundations: numerical modeling and effective parameters

ABSTRACT This paper evaluates the seismic performance of shallow foundations located on liquefiable soil during seismic shaking and post-liquefaction using numerical analyses. Numerical simulations are performed using the UBCSAND constitutive model in FLAC software with validation against prior centrifuge tests conducted by the last author. The numerical analyses carried out in this study emphasize the post-seismic-liquefaction settlement of foundations which has not been addressed adequately compared with the co-seismic settlements. The effect of the foundation surcharge type, embedment depth, relative density, compaction depth, and foundation width are studied by a series of numerical parametric studies. The results show that soil densification and embedment depth can significantly affect the post-liquefaction-induced settlement of shallow foundations compared to the other simulated mitigation strategies. The results indicate that simultaneously considering both the relative density and the compaction depth ratio can significantly contribute to reducing post-liquefaction foundation settlement.

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

AbstractMethane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET‐ CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance‐based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET‐CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

Design for early-age structural performance of 3D printed concrete structures: A parametric numerical modeling approach

This paper presents a design framework based on structural performance of 3D concrete printing (3DCP) that considers three strength criteria for early-age failure modes: plastic collapse, elastic buckling, and flexural collapse. A simulator of structural behavior during printing was developed to take any shell or linear model, slice it into layers, perform incremental structural analysis layer by layer, and assign the respective material properties by adjusting the age of each layer when a new one is added. Then, finite element analysis for each printing stage indicates whether collapse might occur based on rheological and mechanical material properties over time. Printing experiments validated the criteria based on collapse layer estimation for a solid cylinder (plastic collapse), a thin-wall structure (elastic buckling), and an overhang structure (flexural collapse). Plastic collapse was most accurately predicted by a Drucker-Prager yield criterion for an unrestrained base and by a Modified Lade and Mohr-Coulomb criterion for a partially restrained base, while elastic buckling was predicted by linear elastic analysis. Additionally, this paper introduces flexural collapse as a distinct failure mode in early age 3DCP, proposing design criteria for overhang structures based on printing experiments. Experimental lessons learned were used to establish design constraints to aid the design of 3DCP structures, which were tested in the design of a hollow column and a cantilever structure. This study advances computational design for 3DPC structures with a new design methodology that combines constrained design and parametric numerical modeling.

Numerical Model Parameters Choice of Helical Savonius Wind Rotor: CFD Investigation and Experimental Validation

Electrical power is essential for human beings welfare. The available wind as a clean and renewable source of energy has whetted extensive interest over decades. Savonius vertical axis wind rotor as an energy converter has the merit of being adequate for specific implementations owing to its lower cost and independency on wind direction. From this perspective, multiple studies have been conducted to boost its efficiency. This research work emphasizes on the helical Savonius wind rotor (HSWR). The basic objective is to investigate the impact of selecting the numerical model parameters on its aerodynamic and performance characteristics. Experimental tests were realized with a 3D printed HSWR in a wind tunnel. The experimental performances in terms of power, static and dynamic torque coefficients were addressed. Next, a numerical study was undertaken through Ansys Fluent 17.0 software. Grid, turbulence model and rotating domain size tests were examined. Good accordance was obtained, which validated the numerical model with an averaged error of 5%. The maximum power coefficient proved to be equal to 0.124 at a tip speed ratio of 0.73 and 0.1224 at a tip speed ratio of 0.69, respectively, numerically and experimentally

A simplified mathematical modeling approach for thermal runaway of Li(Ni0.8Co0.1Mn0.1)O2 pouch cells based on thermal runaway experimental data

Currently, the numerical modeling methods used to characterize the thermal runaway and propagation mechanisms in NCM811 pouch batteries still face numerous challenges. This is primarily due to the uncertainties in thermal runaway reactions and the immense computational demands encountered when modeling begins at the granular level of the cell. To address this issue, this study designed a series of experiments for an NCM811 pouch battery. By employing experimental data, reasonable assumptions were made regarding the combined effects of solid-state thermal conduction, electrochemical heating, and fluid-solid heat transfer through gas jet streams. A simplified thermal runaway mathematical modeling method based on experimental data was proposed, effectively tackling the problem. This framework was then applied to thermal runaway experiments on a single NCM811 battery used in this study, providing essential input parameters for three-dimensional numerical modeling of modules composed of multiple batteries in multiphysics simulation software. The simulation results derived from the proposed method were in good agreement with experimental data. This study can characterize the thermal runaway and thermal propagation characteristics of battery modules using single-cell thermal runaway experiments. Moreover, the proposed modeling approach not only predicted the experimental outcomes of the NCM811 pouch battery with reasonable accuracy but also provided smoother data, elucidating the highly fluctuating trends observed in experimental data. This method also offers guidance for designing fire prevention and smoke exhaust systems for battery packs. This study presents a novel approach for the mathematical modeling of thermal runaway and thermal propagation in battery modules using NCM811 pouch batteries, contributing to the ongoing development of lithium-ion battery module design.

Estimation of Piston Surface Temperature During Engine Transient Operation for Emissions Reduction

Abstract Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

Experimental Monitoring of Dynamic Parameters of the Sub-Ballast Layers as a Prerequisite for a High-Quality and Sustainable Railway Line

Monitoring dynamic load transfer from train traffic to sub-ballast layers is crucial for verifying the reliability and safety of railway lines, assessing the design cost-effectiveness and achieving minimum environmental impact. For this purpose, measurements in labs, in situ or modeling the influence of dynamic loads on the immediate and long-term roadway quality are often performed using suitable software. The available test sections enabled monitoring of the dynamic loads and optimizing the critical spots where increased dynamic effects from railway traffic may occur. The subject of this paper is the calibration of the sensors installed in the different test sections of the trans-European corridor number V. As a result, the necessary input parameters for the subsequent numerical modeling of the dynamic effects on the track substructure and vibration propagation on the available sections of the upgraded railway line were obtained. The sensor calibration was carried out on the experimental field, part of the Experimental Basis of the Department of Railway Engineering and Track Management. As part of the calibration, the sensitivity of the sensors embedded in the track bed to the applied dynamic loads resulting from the impact effects of the lightweight deflectometer was assessed. The result of the calibration was the demonstration of sufficient sensitivity of the sensors and their suitability for implementation in an actual railway track structure, with the aim of obtaining relevant values of the response of the sub-ballast layers to dynamic loads and assessing the operational impacts on the sustainable environment. Also, the main result of the research was the possibility of using the theoretical–experimental route to optimize the layers of the railway body.

Effect of the Turbulence Model on the Computational Results of a Lucid Spherical Rotor

Due to the excessive increase in the energy demand, renewable energy has become an alternative for electricity production for industrial and domestic needs. Water energy has received significant investment as a sustainable and clean energy source. Lucid spherical rotors, a kind of hydro-power converter, are cross flow rotors designed to be mounted within a pipeline in order to gather excess energy available in gravity-fed water pipelines. This paper focuses on the effect of the numerical model parameters choice, namely the turbulence model, on the Lucid spherical rotor hydrodynamic characteristics. Numerical simulations were carried out through Ansys Fluent software 17.0 using the unsteady Reynolds-Averaged Navier-Stokes (URANS) equations. Four turbulence models: RNG k-ε, Realizable k-ε, SST k-ω and transition SST were tested. Performance characteristics in terms of torque and power coefficients in addition to hydrodynamic features of the flow around the considered rotor were analyzed. The adopted numerical model was validated based on previous experimental findings from the literature. It was found that the realizable k-ε model showed a good agreement with experimental results. Thus, it was adopted for the Lucid spherical rotor simulation. The obtained findings could provide further direction for researchers to use the Lucid spherical water turbine.

Translating pumping test data into groundwater model parameters: a workflow to reveal aquifer heterogeneities and implications in regional model parameterization

Groundwater modelers frequently grapple with the challenge of integrating aquifer test interpretations into parameters used by regional models. This task is complicated by issues of upscaling, data assimilation, and the need to assign prior probability distributions to numerical model parameters in order to support model predictive uncertainty analysis. To address this, we introduce a new framework that bridges the significant scale differences between aquifer tests and regional models. This framework also accounts for loss of original datasets and the heterogeneous nature of geological media in which aquifer testing often takes place. Using a fine numerical grid, the aquifer test is reproduced in a way that allows stochastic representation of site hydraulic properties at an arbitrary level of complexity. Data space inversion is then used to endow regional model cells with upscaled, aquifer-test-constrained realizations of numerical model properties. An example application demonstrates that assimilation of historical pumping test interpretations in this manner can be done relatively quickly. Furthermore, the assimilation process has the potential to significantly influence the posterior means of decision-pertinent model predictions. However, for the examples that we discuss, posterior predictive uncertainties do not undergo significant reduction. These results highlight the need for further research.

An overview of BRDF models in computer graphics

This paper aims to provide an overview of the Bidirectional Reflectance Distribution Function (BRDF) and the various parametric numerical models, known as BRDF models, used to make it easier and more efficient to use BRDF data in computer graphics. It introduces the basics of BRDF and classifies and describes some well-known BRDF models, including their advantages and disadvantages. The paper also covers the concept of data-driven models, which can achieve a high degree of realism by directly acquiring and using measured BRDF data in the rendering process.