Accurate Simulation Research Articles

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.

Visualized generator for the simulation of wastewater quality and quantity variations in the sewer system

Data generators are imperative to support design, management, scenario simulation, risk assessment, and regulatory compliance. Hybrid sewer systems struggle with accurate water quality and quantity monitoring due to variable flow patterns, missing connections, limited monitoring capacity. To accurately regenerate operational data for hybrid sewer system along the sewer shed, a visualized generator was developed to simulate wastewater quantity and quality variations within different scales in the sewer system. The generator was constructed using a multi-level, tree-structured model incorporating various modules, including domestic, industrial, WWTP, and pump stations, to simulate time series variations. A novel instantaneous unit pollutant-hydrograph modeling associated with wastewater conductivity monitoring data was proposed in the generator. The validated generation data of flow, COD, ammonia nitrogen, and phosphate were well-fitted with the full-scale measured data in residential areas, pump stations, and WWTPs. The proposed generator could be used to predict and simulate the dynamic flow and wastewater quality variations at different scale regions in sewer network-wide to support the operation and management of pump stations and WWTPs. The generator modules enable accurate simulation and visualization of water quality and quantity in hybrid sewer system, enhancing the understanding of infiltration, inflow, and pollutant dynamics, especially under challenging conditions like simultaneous RDII and overflow.

Optimization of an N2O Emission Flux Model Based on a Variable-Step Drosophila Algorithm

The application of intelligent process-based crop model parameter optimization algorithms can effectively improve both the model simulation accuracy and applicability. Based on measured values of soil N2O emission flux in wheat fields from 2020 to 2022, and meteorological data from 1971 to 2022, five parameters of the N2O emission flux module in the APSIM model were optimized using the variable step Fruit Fly algorithm (VSS-FOA). The optimized parameters were the soil nitrification potential, the range of concentrated KNH4 of ammonia and nitrogen at semi-maximum utilization efficiency, the proportion of nitrogen loss to N2O during the nitrification process, the denitrification coefficient, and the Power term P for calculating the denitrification water coefficient. Contrasting the optimized parameters using the VSS-FOA algorithm versus the default values supplied with the model substantially improved the goodness-of-fit to field measurements with the overall R2 increasing from 0.41 to 0.74, and a decrease in NRMSE from 17.1% to 11.4%. This work demonstrates that the VSS-FOA algorithm affords a straightforward mechanism for the optimization of parameters in models such as APSIM to enhance the accuracy of model N2O emission flux estimates.

Development of high-efficient asphalt pavement modeling software for digital twin of road infrastructure

To develop digital twin (DT) of road infrastructure, one critical element is computation of pavement responses (strains, stresses, and deflections) under traffic and environmental loading. This study aims to develop high-efficient asphalt pavement modeling software based on semi-analytical finite element method (SAFEM) for DT application. The algorithms address important aspects in vehicle-tire-pavement interaction modeling, such as dynamic vehicular loading, three-dimensional (3-D) non-uniform tire contact stress, viscoelastic behavior of asphalt material, and interface bonding condition. The simulation accuracy is verified by comparison with full-scale test and field measurements, and the relative differences are around 5 % to 20 %. Techniques including optimized discrete Fourier transform, parallel computing, graphics processing unit (GPU) acceleration, and sparse matrices are implemented for computation efficiency. As compared to the traditional 3-D FEM, SAFEM shows significant savings in computation time and storage usage. The high efficiency and accuracy make the software full of potential to be applied for DT of roadway infrastructure.

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

High-resolution regional sea-ice model based on the discrete element method with boundary conditions from a large-scale model for ice drift

Abstract Understanding sea-ice dynamics at the floe scale is crucial to comprehend polar climate systems. While continuum models are commonly used to simulate large-scale sea-ice dynamics, they have limitations in accurately representing sea-ice behaviour at small scales. DEMs, on the other hand, are well-suited for modelling the behaviour of individual ice floes but face limitations due to computational constraints. To address the limitations of both approaches while combining their strengths, we explored the feasibility of using a DEM within a continuum model, where the latter provides boundary conditions for a rectangular high-resolution DEM domain. This paper presents a feasibility study where a discrete model of a 100 × 100 km2 icefield was created using high-resolution optical satellite imagery. Sea-ice dynamics were simulated in the DEM considering environmental forces and integrating large-scale ice-drift velocities as boundary conditions. Model predictions were compared with satellite observations for ice drift and deformation parameters. This numerical approach showed potential for offering accurate, high-resolution predictions of sea ice, particularly in coastal areas and near islands, and may find applications in ice navigation and climate studies. However, further development of the DEM, along with upgrades to the coupled ocean models providing data for the ice component, may be necessary. Additionally, challenges remain to develop a two-way coupling between the DEM and a continuum model, which may be needed to improve the accuracy of large-scale simulations.

Insights on the mechanical properties and failure mechanisms of calcium silicate hydrates based on deep-learning potential molecular dynamics

The molecular-scale mechanical properties of calcium silicate hydrates are crucial to the macro performance of cementitious materials, while achieving coincidence between accuracy and efficiency in computational simulations still remains a challenge. This study utilizes a deep-learning potential, specifically developed for calcium silicate hydrates based on artificial neural network, to achieve molecular dynamics simulations with accuracy comparable to first-principle methods. With this potential, the elastic properties and uniaxial mechanical behaviors are explored, wherein the anisotropy and impact mechanism of calcium ratios are analyzed. The results add to evidence that the deep-learning potential possess a higher accuracy than common force fields. The anisotropy of elastic modulus is mainly attributed to different atomic interactions in various directions, while the anisotropy of strength is additionally affected by the form of failure. This study may advance the accurate molecular-scale simulation and deepen the understanding of the strength source and cohesion mechanism of cement-based materials.

Pore collapse, shear bands, and hotspots using atomistics-consistent continuum models for RDX (1,3,5-trinitro-1,3,5-triazinane): Comparison with molecular dynamics calculations

Shock-induced energy localization is a crucial mechanism for determining shock sensitivity of energetic materials (EMs). Hotspots, i.e., localized areas of elevated temperature, arise when shocks interact with defects (cracks, pores, and interfaces) in the EM microstructure. The ignition and growth of hotspots in a shocked energetic material contribute to rapid chemical reactions that can couple with the passing shock wave, potentially leading to a self-sustained detonation wave. Predictive models for shock-to-detonation transition must correctly capture hotspot dynamics, which demands high-fidelity material models for meso-scale calculations. In this work, we deploy atomistics-guided material models for the energetic crystal RDX (1,3,5-trinitro-1,3,5-triazinane) and perform tandem continuum and all-atom molecular dynamics (MD) simulations. The computational setup for the continuum and MD simulations are nearly identical. The material models used for the calculations are derived from MD data, particularly the equations of state, rate-dependent Johnson–Cook strength model, and pressure-dependent shear modulus and melting temperature. We show that a modified Johnson–Cook model that accounts for shear-induced localization at the pore surface is necessary to represent well—relative to MD as the ground truth—the inelastic response of the crystal under a range of shock conditions. A head-to-head comparison of continuum and atomistic calculations across several metrics of pore collapse and energy deposition demonstrates that the continuum calculations are in good overall agreement with MD. Therefore, this work provides improved RDX material models to perform physically accurate meso-scale simulations, to enhance understanding of hot spot formation, and to use meso-scale hot spot data to inform macro-scale shock simulations.

Construction of Al–Si interatomic potential based on Bayesian active learning

Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems, and the prohibitively high computational cost associated with ab initio molecular dynamics simulations. In order to enhance simulation efficiency and accuracy of the Al–Si alloys’ microstructural evolution, this study employs a dynamic active learning technique, FLARE, to develop a non-parametric machine learning potential that combines the high accuracy of density functional theory (DFT) with the efficiency of classical molecular dynamics (MD). Without relying on extensive initial ab initio molecular dynamics data or existing databases, collection of the necessary data is progressively made during the active learning process, thereby constructing a potential capable of accurately simulating the structure and dynamics of high-temperature Al–Si alloys. By comparing with experimental measurements and ab initio molecular dynamics calculations, the high accuracy and computational efficiency of this potential is demonstrated in predicting energy, force, structure, and dynamic properties. The results provide novel theoretical insights for optimizing Al–Si alloy processing and underscore the usefulness of active learning methods in constructing high-accuracy potentials.

Validation of Noise Barrier Insertion Loss Simulation in Reducing Road Traffic Noise by Site Measurement Data

A noise barrier serves as a method to control noise pollution resulting from road traffic, particularly prevalent in rural areas near heavy traffic routes such as highways. This study focuses on simulating the sound Insertion Loss (IL) of existing noise barriers designed to mitigate road traffic noise around school areas. The primary goal is to assess the precision of the Finite Element Modeling (FEM) program. Utilizing the finite element analysis software, COMSOL, the study models the noise barrier and its surroundings in two-dimensional acoustic radiation problem based on on-site dimensions and parameters. By examining the often-neglected aspect of noise leakage and its impact on noise reduction, the research aims to provide specific guidance for urban planners and architects to optimize noise mitigation in various urban settings. The study reveals a strong agreement between the simulated IL and on-site measurements, with a minimal difference ranging from 0.4 dB to 1.1 dBA on average. Additionally, the research suggests potential improvements to simulation accuracy through adjustments in aperture sizes. The findings demonstrate that finite element simulation can be a valuable tool for designers to assess the effectiveness of noise barriers before construction begins.

Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning

AbstractThe formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics.

Multi-region coupling computational fluid dynamics simulation of the underground compressed air energy storage process

Compressed air energy storage (CAES) in underground spaces is a common method for addressing the instability of renewable energy generation. As the construction and testing of CAES systems are often of high cost, the numerical simulation which offers a more efficient and low-cost research method can provide a better alternative to research the process. In this study, a numerical simulation model has been developed to describe the air movement within the CAES process. Specifically, the study focuses on two different types of motion: air compressible flow within the cavern and air porous flow in the surrounding rock. Using OpenFOAM, a multi-region coupling solver has been developed to address the different governing equations for these two types of motion, and couples pressure, velocity, and temperature at the boundaries. The developed solver is used to simulate a field test of a CAES system. The numerical simulation results closely match the experimental data, verifying the reliability of the solver. Moreover, the solver can achieve multi-region flow simulations that are previously unattainable, resulting in more accurate simulations. Simulations have also been conducted to analyze various working conditions, including different rock permeability, porosity, and air injection mass rates. The results show that different conditions significantly affect air pressure, leakage rate, temperature, and flow field changes within the cavern. These simulation results and the underlying mechanisms have been analyzed to provide reference and technical support for site selection, construction, and operational strategies of CAES systems in underground spaces.

From Darcy to turbulent flow: Investigating flow characteristics and regime transitions in porous media

This research addresses the flow characteristics within a porous medium composed of a monolayer of closely packed spheres, spanning from viscous-dominated to turbulent flow regimes. In the first part of this paper, the turbulent flow characteristics at a 30 MPa pressure drop within the domain are presented. The results are averaged across different cross sections between the inlet and outlet. In the second part of the study, simulations are conducted with pressure drops, ranging from nearly 0 to 100 MPa. The analysis finds distinct flow patterns within the domain and provides estimations for the permeability and the inertial term coefficient. Moreover, the transition from Darcy to non-Darcy and turbulent flow is achieved through the use of different criteria. The specified geometry is suitable for validating and calibrating simplified discrete element method models coupled with computational fluid dynamics. The main goal of this research is to produce a reliable benchmark to figure out the challenge of limited experimental data concerning fluid flow characteristics in densely packed granules specially subjected to high pressure conditions. To do this, representative specimens are designed, accurate simulations are conducted, and precise assessments of the results are carried out.

Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI

Accurately simulating maize (Zea mays L.) yield at the regional scale is of paramount importance for making informed policy adjustments and for ensuring food security. Sobol sensitivity analysis was used in this study to screen sensitive parameters of a process-based crop model (Chinese Agricultural Meteorological Model, CAMM). An upscaling approach was utilized to reduce the intra-pixel heterogeneity error of MODIS LAI. Then upscaled MODIS LAI data were assimilated into the CAMM model through a 4DVar assimilation algorithm to optimize pixel-level sensitive parameters, thereby improving the simulation accuracy of the summer maize growth process and yield. Specific leaf area (SLATB_0, SLATB_1) during the period from emergence to tasseling of summer maize exhibited the strongest impact on summer maize yield. Additionally, the average LAI value within the 85–95 % range of ordered LAI values for small pixels within large pixels effectively reduced the intra-pixel heterogeneity error of MODIS LAI, and pixel-based parameterization of SLATB_0 and SLATB_1 at a larger pixel scale (0.0625°) was achieved. Based on yields recorded at agrometeorological stations from 2015 to 2020, assimilated yields in both data assimilation scheme 1 (DA1, optimization of only the sensitive parameter SLATB_0) and scheme 2 (DA2, simultaneous optimization of sensitive parameters SLATB_0 and SLATB_1) exhibited higher accuracy than schemes without data assimilation (with r values of 0.41–0.72 and NRMSE values of 19–30 %). Furthermore, DA2 showed greater simulation accuracy (r: 0.64–0.93, NRMSE: 9–21 %) than DA1 (r: 0.61–0.91, NRMSE: 12–23 %). Upscaling remotely sensed LAI products can significantly reduce the uncertainties of LAI data at a larger pixel scale, and assimilating these LAI data into crop models can effectively increase the simulation accuracy of crop growth and development processes at the regional scale.

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.

The study of 3D FDTD modelling of large-scale Bragg gratings validated by experimental measurements

Abstract This study discusses the importance of accurately calculating the optical response of Bragg gratings and the challenges associated with the 3D finite-difference time-domain (FDTD) method for simulating large-scale structures. The Bragg grating section in monolithic extended cavity diode lasers is of substantial size, making 3D FDTD simulations computationally challenging due to their complexity. In order to assess the accuracy of 3D simulations, we compare them with experimental results. Using a precise model design, involving a systematic analysis of simulation parameters, we obtain a good agreement between 3D FDTD simulations and the experimental results.

Assessment of the Influence of Input Parameters in CFD Simulation of Ventilation in an Experimental Chamber

Abstract The aim of this paper is to evaluate the impact of selected input parameters on the accuracy and usability of CFD simulations. This paper compares the achieved results of Computational Fluid Dynamics (hereinafter CFD) simulations of air age with the ventilation results of a specific experimental box. A total of 16 variants were simulated for different simulation settings, including various computational domains and meshes in OpenFOAM, Ansys Fluent, DesignBuilder, and BlueCFD-AIR software. The paper primarily focuses on the importance of meeting the suitability criterion for the yPlus parameter used in the wall function of turbulent models k-ε and k-ω SST, and the resulting air age. Simulation with the best yPlus parameter shows the best agreement in airflow velocity and air age with the experiment. Overall, this led to an improvement in accuracy by 41 % compared to original simulation and 56 % compared to the previous our best OpenFOAM simulation.

Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part A: Fundamentals

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations.

Investigation on the hydraulic loss in the vertical inlet and outlet of a pumped storage power station

Abstract The vertical inlet/outlet is commonly adopted in the upper reservoir of pumped-storage power stations, in which the internal flow patterns exert a critical influence on the safety and operational efficiency of the entire power station. Based on the standard k-ε turbulence model coupled with the revised wall roughness theory, this investigation presented a numerical model of a vertical inlet/outlet in a pumped-storage upper reservoir and the validity and accuracy of the numerical simulation are validated through comparison well with the results of the model experiments. The results suggest that the emergence of vortices of varying intensities near the inlet during power-generation scenarios at dead water levels and at 3.5 times of the rated flow rate (389.6 m3/s). The bend section experiences fairly nonuniform flow velocity, with the highest average flow hydraulic loss in the entire vertical pipe inlet/outlet system. These insights provide a substantial utility for operational strategies and optimization designs in related engineering projects.

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