Comprehensive Simulation Research Articles

Synergizing shale enhanced oil recovery and carbon sequestration: A novel approach with dual lateral horizontal wells

The dual challenge of enhancing oil recovery while sequestering carbon dioxide (CO2) in oil reservoirs is a pivotal concern in the energy sector. CO2 injection is recognized for its ability to decrease oil density and viscosity, thereby improving oil mobility and recovery rates. Traditionally, efforts have been concentrated either on enhancing oil recovery (EOR) or carbon storage, but not many efforts spent to couple EOR and CO2 sequestration. Hence, novel techniques to optimize engineering designs to synergize EOR with CO2 sequestration is the best approach to maximize the opportunities of storing emission gas and contribute to the global world decarbonization goals.This study introduces an innovative dual lateral horizontal well design, aimed at simultaneously boosting oil recovery from shale reservoirs and enhancing CO2 retention. By integrating a conceptual understanding of oil recovery mechanisms with empirical data from the field, this research contrasts the proposed dual lateral design with the conventional Huff-n-Puff gas injection technique, commonly employed in shale oil formations.Our findings demonstrate that the dual lateral horizontal wells significantly outperform other injection methods in both oil recovery and CO2 storage. Upon optimization, the dual lateral injection design continues to surpass the Huff-n-Puff method in terms of CO2 storage, oil recovery, and net present value (NPV). This investigation not only presents innovative gas injection strategies in shale reservoirs but also provides insights into optimizing gas injection methods to enhance production efficiency and contribute to climate change mitigation through improved carbon capture and storage capacities.Through comprehensive numerical simulations and empirical data analysis, the study explores the optimization of well spacing, revealing that a dual lateral well with optimized spacing between 20 and 30 feet achieves the best outcomes in terms of oil recovery, CO2 retention, and net present value (NPV). The research presents a unique coupling of economic and environmental benefits, supported by economic analysis that includes the potential impact of CO2 tax credits.The novelty of this research is underscored by its integrated approach to CO2-EOR, the development of a dual lateral well design, and the optimization of well spacings for maximized efficiency. By providing a scalable solution that is both economically viable and environmentally sustainable, this research contributes a significant paradigm shift in the field of EOR and CO2 sequestration, with implications for policy and investment strategies in the energy sector. The findings propose a new direction for shale reservoir exploitation, promising to enhance production efficiency while contributing to global efforts in greenhouse gas reduction.

Exploring the Impact of Split Ring Resonator Metamaterial Structure Integration on 4X4 MIMO Antenna Performance in Sub-6GHz Bands for 5G Applications

This research delves into the realm of antenna engineering, with a specific focus on examining the performance of 4x4 MIMO antennas with and without integration of split ring resonator metamaterial structure. The objective is to comprehend the influence of split ring resonator metamaterials on antenna characteristics within the 6 GHz frequency range. Employing CST Studio Suite software, comprehensive simulations are conducted to evaluate various parameters including gain, directivity, radiation efficiency, S-parameters, power distribution, VSWR, and total efficiency. In the frequency band up to 6GHz, observed gain values were 24.62dB without split ring resonator metamaterial integration and ranging from 6dB to 27dB with integration. Through a systematic comparison, the study aims to delineate the precise effects of split ring resonator metamaterials on antenna performance, thereby offering valuable insights into their potential for enhancing MIMO antenna systems.

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model.

Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART

Sequential, multiple assignment randomized trials (SMARTs), which assist in the optimization of adaptive interventions, are growing in popularity in education and behavioral sciences. This is unsurprising, as adaptive interventions reflect the sequential, tailored nature of learning in a classroom or school. Nonetheless, as is true elsewhere in education research, observed effect sizes in education-based SMARTs are frequently small. As a consequence, statistical efficiency is of paramount importance in their analysis. The contributions of this manuscript are twofold. First, we provide an overview of adaptive interventions and SMART designs for researchers in education science. Second, we propose four techniques that have the potential to improve statistical efficiency in the analysis of SMARTs. We demonstrate the benefits of these techniques in SMART settings both through the analysis of a SMART designed to optimize an adaptive intervention for increasing cognitive behavioral therapy delivery in school settings and through a comprehensive simulation study. Each of the proposed techniques is easily implementable, either with over-the-counter statistical software or through R code provided in Supplemental Material.

On Estimating Multi- Stress Strength Reliability for Inverted Kumaraswamy Under Ranked Set Sampling with Application in Engineering

The terrible operating constraints of many real-world events cause systems to malfunction regularly. The failure of systems to perform their intended duties when they reach their lowest, highest, or both extreme operating conditions is a phenomenon that researchers rarely focus on. The multi-stress strength reliability R=P(W<X<Z)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R = P(W<X<Z)$$\\end{document} is deemed in this study for a component whose strength X falls between two stresses, W, and Z, where X, W, and Z are independently inverted Kumaraswamy distributed. Both maximum likelihood and maximum product spacing procedures are employed to obtain the reliability estimator under simple random sampling (SRS) and ranked set sampling (RSS) methodologies. Four scenarios for reliability estimators are considered. The reliability estimator in the first and second cases can be determined by applying the same sample design (RSS/SRS) to the strength and stress distributions. When the sample data for W and Z originate from RSS while those for X are acquired from SRS, the third reliability estimator is calculated. The drawn data of the strength and stress random variables, which are obtained from SRS and RSS, respectively, are taken into consideration in the final scenario. The effectiveness of the suggested estimators is compared using a comprehensive computer simulation. Lastly, three real data sets have been used to determine reliability estimators.

Reactive direct simulation Monte Carlo modeling of gas reactive transport in three dimensional porous fibrous media across rarefied to continuum regimes

Gas flow in complex porous media can vary across rarefied to continuum regimes depending on the characteristic size of porous media. To study the gas reactive transport process in three dimensional porous fibrous media across rarefied to continuum regimes, a comprehensive reactive direct simulation Monte Carlo model coupling with heat conduction and radiation model as well as reaction process is proposed. The results calculated by the proposed model are verified to fit well with those obtained using analytical model and experimental data. The “inlet” effect promoting the skeleton temperature is found. There are existing competitions between the heat conduction, reaction rate and convection, radiation process. The temperature of porous fibrous media skeleton increases with the catalyst coverage, and decreases with increase of Knudsen number. When the proportion of hydrogen increases, the increase in the temperature of the porous fibrous media skeleton is initially rapid and then gradual. These studies help to deeply understand the chemical reaction mechanism in porous media across rarefied to continuum regimes.

Computational analysis of microchannel heat sinks with constant circular pin fins: Performance evaluation and optimization

This research entails a comprehensive computational simulation aimed at comparing the performance of microchannel heat sinks (MCHS) featuring 55-pin fins, characterized by constant circular pins with a diameter of 15µm, uniform spacing of 50µm, and variable heights ranging from 60 to 35µm. The primary objective was to assess the performance of these models with a reference Mini channel. Mesh independence validation was meticulously executed on MCHS with pin fins to ascertain the accuracy of the computational findings. The simulation encompassed seven Reynolds numbers within the range of 500–1500. The performance evaluation incorporated hydraulic and thermal factors, specifically pressure drop, Nusselt number, and the thermal performance factor (TPF). A holistic assessment of overall performance was achieved through the establishment of a heat sink thermal performance factor, accounting for both hydraulic and thermal aspects. The optimal geometry was determined based on this factor, and corresponding visual representations were generated to illustrate these findings.

Enhancing Communication Reliability: Designing Microwave Links for Bahir Dar-Woretta Connectivity

This paper explores the need for establishing a microwave link between Bahir Dar and Woretta as an alternative communication solution to the existing optical fiber infrastructure. Microwave links offer an effective way to overcome challenges posed by rugged terrains and unfavorable environmental conditions that hinder the deployment of fiber optics. As Woretta emerges as a key economic and investment hub within the Amhara Region, demand for reliable and efficient communication is expected to grow significantly. The study encompasses various aspects of planning and designing the microwave link, including site surveys, consideration of fade margins, frequency planning, link budget calculations, and assessing the feasibility and reliability of the proposed link. The paper employs LINKPlanner 5.4.1 software to simulate and validate the results. Due to terrain constraints, a direct link between Bahir Dar and Woretta is not feasible. Instead, a two-hop link is proposed, involving transmission from Bahir Dar to Zege, and then from Zege to Woretta. This alternative configuration ensures optimal connectivity while addressing the terrain limitations. By presenting a comprehensive analysis and simulation of the microwave link, this paper provides valuable insights into the planning and implementation of a robust communication infrastructure. The proposed microwave link will offer a reliable and efficient alternative to the existing optical fiber network, ensuring uninterrupted connectivity to support the region’s growth and development.

Enhanced Randomized Harris Hawk Optimization of PI Controller for Power Flow Control in the Microgrid with the PV-Wind-Battery System

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid's power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (HHO) to fine-tune the PI controller is proposed, using the algorithm's ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm's exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid's stability and performance. The study evaluates the effectiveness of the proposed Enhanced Randomized Harris Hawk Optimization-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.

A Study on Unified Modelling Approach for Memristor: Next Generation Semiconductor Devices

This research investigates the voltage-current (V-I) characteristics of three distinct memristor models, each representing a unique composition and behaviour. The model, denoted as TiN-TiOx-HfOx-Pt-Bilayered Memristor model, is a compact representation designed for Metal–Oxide Resistive Random Access Memory (RRAM). Matlab tool is used for the simulation to analyse V-I characteristics of Memristor. Through comprehensive analysis and simulations, we aim to provide a detailed insight into the intrinsic behaviours of the memristor models. Understanding the V-I characteristics of these models is crucial for their potential applications in emerging memory technologies. This Study elaborate the mathematical modelling of memristor with the outcome untuned V-I Characteristics - DC Switching characteristics. The findings contribute to the broader field of memristor research, fostering advancements in electronic memory devices and computational systems

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

Exceptionally Fast Separation of Xylene Isomers with Zeolitic Nanotube Array Membranes.

The separation of xylene isomers is of vital importance in chemical industry but remains challenging due to their similar structure and overlapping physiochemical properties. Membrane-based separations using the zeolite MFI, graphene oxide, and metal-organic frameworks have been intensively studied for this application, but the performance is limited by the well-known rule that the filtrate permeance scales inversely with the membrane thickness. We propose a novel membrane design that is capable of breaking this rule, based on an array of recently discovered zeolite nanotubes. Each zeolite nanotube possesses a 3.6-nm-wide central channel, connecting to dense, uniform 0.8-nm-wide holes on its wall that act as selective pores. Comprehensive molecular dynamics simulations show that this membrane exhibits permeance exceeding current state-of-the-art membranes by at least an order of magnitude while simultaneously maintaining an acceptable selectivity. In particular, a thicker membrane featuring longer zeolite nanotubes exhibits a higher permeance due to the presence of more selective pores. The proposed membrane design is expected to be broadly applied to other gas separations and even desalination as long as zeolitic nanotubes with customized pores are available.

Development of multiple input supply based modified SEPIC DC–DC converter for efficient management of DC microgrid

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters.

Ranked set sampling imputation methods in presence of correlated measurement errors

Very little attention has been paid by the authors for providing improved imputation methods under correlated measurement errors (CMEs) using various sampling designs. This article addresses several power ratio imputation methods and the resulting estimators under ranked set sampling (RSS) when CMEs are present. The mean square error (MSE) is developed to assess how well the suggested estimators work under CMEs. The effectiveness of the proposed imputation methods and corresponding resultant estimators is assessed by a comprehensive simulation utilizing an artificially generated population. Further, an application of the proposed imputation methods is also provided using real data from Sweden’s municipalities consisting of the total number of seats in 1982 in municipal council taken as study variable and the number of conservative seats in 1982 in municipal council taken as auxiliary variable. The CMEs might arise due to common surveyor biases if the same surveyor collects information on both the “total number of seats in municipal council” and “the number of conservative seats in that municipal council”. The simulation and real data results show that the proposed power ratio imputation methods outperform the mean, conventional ratio, and product imputation methods.

A Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation.

ABSTRACT An alternative transcription start site (ATSS) is a major driving force for increasing the complexity of transcripts in human tissues. As a transcriptional regulatory mechanism, ATSS has biological significance. Many studies have confirmed that ATSS plays an important role in diseases and cell development and differentiation. However, exploration of its dynamic mechanisms remains insufficient. Identifying ATSS change points during cell differentiation is critical for elucidating potential dynamic mechanisms. For relative ATSS usage as percentage data, the existing methods lack sensitivity to detect the change point for ATSS longitudinal data. In addition, some methods have strict requirements for data distribution and cannot be applied to deal with this problem. In this study, the Bayesian change point detection model was first constructed using reparameterization techniques for two parameters of a beta distribution for the percentage data type, and the posterior distributions of parameters and change points were obtained using Markov Chain Monte Carlo (MCMC) sampling. With comprehensive simulation studies, the performance of the Bayesian change point detection model is found to be consistently powerful and robust across most scenarios with different sample sizes and beta distributions. Second, differential ATSS events in the real data, whose change points were identified using our method, were clustered according to their change points. Last, for each change point, pathway and transcription factor motif analyses were performed on its differential ATSS events. The results of our analyses demonstrated the effectiveness of the Bayesian change point detection model and provided biological insights into cell differentiation.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting doselocations.

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

Harmonics management and hosting capacity enhancement: Optimal double-resistor damped double-tuned power filter with artificial hummingbird optimization.

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Investigation of Sidewall and Reynolds Number Effects in a Ribbed Square Duct

AbstractThis paper presents a comprehensive three-dimensional Direct Numerical Simulation (DNS) investigation of flow characteristics over a roughened wall in a duct, exploring bulk Reynolds number variations from 4400 to 13,200 and considering the influence of side walls. Notably, a significant increase in friction factor highlights heightened resistance to flow due to surface roughness, emphasizing the importance of considering surface conditions in engineering applications. The study reveals three recirculation zones in the streamwise direction, indicating intricate separation phenomena caused by the interaction between the ribbed wall and the flow. Furthermore, the presence of sidewalls creates two major vortices perpendicular to the flow direction, highlighting the complexity of the vortical structures in this configuration, crucial for predicting flow behavior and optimizing system performance. The study demonstrates that the influence of the Reynolds number on these vortices is not well-scalable with respect to outer units, in contrast with respect to smooth ducts. Investigating side-wall effects, increased turbulent production rate, and non-equilibrium turbulence along the side walls highlight the sensitivity of turbulent stresses to Reynolds number and side-wall proximity. In conclusion, the paper provides novel insights into the interplay between Reynolds numbers, roughened surfaces, and boundary conditions in turbulent flows, laying a foundation for a deeper understanding of the flow in duct with high roughness.