Geometric Simplification Research Articles

Modeling traffic pollutants in a street canyon by CFD: Idealized line sources versus multiple realistic car sources

Idealized sources are commonly used to reproduce the traffic emission in street canyons in experimental and numerical investigations. However, it remains unclear whether idealized sources can accurately reproduce the pollutant dispersion compared to more realistic sources. The goal of this paper is to investigate the impact of idealized and realistic sources on traffic-induced pollutant concentration in a street canyon by numerical simulation with Computational Fluid Dynamics (CFD). First, the scale-adaptive simulation (SAS) results of mean velocity and concentration are compared with wind-tunnel (WT) data for idealized line sources (ILS) and a satisfactory agreement is found between the SAS and WT results. Next, SAS are performed to investigate the impact on mean velocity and mean pollutant concentration of ILS versus multiple realistic car sources (MRCS) cases, designed to mimic a configuration of traffic jam-like conditions (i.e. rows of idling cars) in a street canyon. Different levels of geometrical simplifications of sources and total emission rates are considered. Although this research study focuses on these aforementioned specific set of conditions and conclusions relate solely to these circumstances, the SAS results show that the overall-averaged concentration associated with MRCS and the simplified source-geometry cases is about 1.18–6.22 times larger than that by ILS. Thus, ILS may be inadequate to reproduce the pollutant concentration in real scenarios. This study can contribute to enhancing the pollution prediction accuracy and reduce the potential risk of human exposure.

Deep learning as Ricci flow.

Deep neural networks (DNNs) are powerful tools for approximating the distribution of complex data. It is known that data passing through a trained DNN classifier undergoes a series of geometric and topological simplifications. While some progress has been made toward understanding these transformations in neural networks with smooth activation functions, an understanding in the more general setting of non-smooth activation functions, such as the rectified linear unit (ReLU), which tend to perform better, is required. Here we propose that the geometric transformations performed by DNNs during classification tasks have parallels to those expected under Hamilton's Ricci flow-a tool from differential geometry that evolves a manifold by smoothing its curvature, in order to identify its topology. To illustrate this idea, we present a computational framework to quantify the geometric changes that occur as data passes through successive layers of a DNN, and use this framework to motivate a notion of 'global Ricci network flow' that can be used to assess a DNN's ability to disentangle complex data geometries to solve classification problems. By training more than 1500 DNN classifiers of different widths and depths on synthetic and real-world data, we show that the strength of global Ricci network flow-like behaviour correlates with accuracy for well-trained DNNs, independently of depth, width and data set. Our findings motivate the use of tools from differential and discrete geometry to the problem of explainability in deep learning.

Aircraft Structural Assessments in Data-Limited Environments: A Validated FE Method

Aircraft operators often modify aircraft configurations, install new equipment, and alter airframes to accommodate this equipment, leading to operations in flight envelopes different from original design profile. These modifications necessitate airframe structural assessments, which typically require comprehensive aircraft design data, often unavailable to operators. This study aims to develop and validate a practical method for finite element analysis (FEA) of aircraft structures in the absence of this detailed design data. Focusing on a case study involving structural analysis of an aircraft wing, this study presents assumptions and idealizations used to develop 2.5D finite element (FE) model of the wing. Fidelity of this model is established by comparing FE analysis results with experimental data. Key validation metrics include reaction forces, load distribution at wing-fuselage attachments, and deformation at reference points on the wing under design load. Comparison between FE analysis and experimental results is carried out to substantiates accuracy of these geometric simplifications and idealizations of load-carrying behaviour of structural members. Therefore, practicality of these idealizations in absence of design data is demonstrated. This study offers a novel approach for structural assessments of aircraft without relying on proprietary design data. The validated method enhances capability of aircraft operators to perform effective structural analyses, thereby extending service life of aircraft with continued airworthiness.

Impedance modeling of low-voltage cables considering capacitances and parameter uncertainty using finite-element method

Recent research presented methods to improve the calculation of low voltage cable parameters by using finite-element models due to increasing focus on power quality issues in distribution grids. However, the focus was mostly on series parameters and nominal frequency. In this paper the calculation of cable capacitances as well as their impact on harmonic impedance of a single cable and in a rural low voltage grid is discussed using detailed finite-element models. Moreover, the accuracy of geometric simplification was quantified. The results are used to compare and optimize existing analytical equations. Furthermore, sources of uncertainty in cable modeling were identified and their range of impact on impedance was subsequently studied to give recommendations regarding necessary study cases for a robust assessment of grid impedance.

Cutout effects on the vibration of sandwich auxetic cylindrical shells with an experimental validation

This study investigates the influence of auxetic core layers on the vibrational characteristics of sandwich cylindrical shells with cut-outs. The analysis utilizes the First-order Shear Deformation Theory (FSDT) and analytical techniques. The irregular behavior of auxetic re-entrante honeycomb cells necessitates the exploration of their effects on various systems. Additionally, the proposed method offers advantages over Finite Element Method (FEM). FEM modeling suffers from increased computational time and reduced accuracy with a large number of elements or high aspect ratio elements. These challenges are particularly significant for shells with geometric features of disparate scales, such as large cylinders with tiny cutouts or cutouts with low aspect ratios. However, the present research addresses these challenges by employing a method that integrates five panels with varying dimensions. Consequently, each section can utilize specific Two-Dimensional Generalized Differential Quadrature (2D-GDQ) grid points, tailored to the size of each side, enabling rapid and accurate prediction of displacement and stress parameters, particularly for shorter sides. The proposed method excels at precisely modeling cutouts while preserving their realistic properties. This is achieved by avoiding geometric and material simplifications and by assigning distinct boundary conditions to each region, including critical areas like corners, internal, and external sides. Moreover, Frequency Response Functions (FRFs) obtained from accelerometer and laser signals, alongside vibration-induced sound wave signals captured by electroacoustic microphones, validate the precision of the numerical calculations.

Investigation for Monte Carlo simulation with geometric simplification of the GammaCell 220 irradiator with experimental measurements using Fricke xylenol gel (FXG)

Radiation therapy necessitates dosimetry using materials with densities similar to human tissue and at a lower cost. Comparisons with simulation methods will help validate new dosimeters and their measurements. To experimentally simulate radiation effects, an industrial irradiator like the GammaCell 220 can be utilized. This study aims to validate FXG dosimetry through Monte Carlo simulations using the MCNP and TOPAS codes, employing a simplified geometry model of the GammaCell 220 irradiator. The FXG dosimeter demonstrated linearity with an R2 of 0.99517. Monte Carlo simulations using both codes showed coherence, with MCNP exhibiting greater similarity to experimental results. The incorporation of simplified Monte Carlo modeling demonstrates the feasibility of obtaining satisfactory results for the GammaCell 220 irradiator geometry. Additionally, it validates the applicability of FXG dosimetry for future utilization in radiation therapy.

Optimization of Welded Joints under Fatigue Loadings

In this paper, the notch effect in weldments has been investigated, and the optimal configuration of different types of welded joints has been analysed using the implicit gradient approach. By considering this implicit gradient method, it is possible to calculate the effective stress related to fatigue damage, with the effective stress being a continuous scalar function of the real stress tensor components, even in the presence of sharp edges. Hence, the search for the optimal configuration that maximises fatigue life can be tackled as the condition of minimum effective stress obtained by changing the weld shape and geometrical parameters. Both load-carrying cruciform joints and spot welds made of steel have been considered. The structural details have been studied by modelling actual shapes without any geometric simplification. Moreover, the same numerical procedure has been considered independently of the size, shape or load condition without imposing restrictive rules on the FE mesh.

Modeling oxygen transport in the brain: An efficient coarse-grid approach to capture perivascular gradients in the parenchyma.

Recent progresses in intravital imaging have enabled highly-resolved measurements of periarteriolar oxygen gradients (POGs) within the brain parenchyma. POGs are increasingly used as proxies to estimate the local baseline oxygen consumption, which is a hallmark of cell activity. However, the oxygen profile around a given arteriole arises from an interplay between oxygen consumption and delivery, not only by this arteriole but also by distant capillaries. Integrating such interactions across scales while accounting for the complex architecture of the microvascular network remains a challenge from a modelling perspective. This limits our ability to interpret the experimental oxygen maps and constitutes a key bottleneck toward the inverse determination of metabolic rates of oxygen. We revisit the problem of parenchymal oxygen transport and metabolism and introduce a simple, conservative, accurate and scalable direct numerical method going beyond canonical Krogh-type models and their associated geometrical simplifications. We focus on a two-dimensional formulation, and introduce the concepts needed to combine an operator-splitting and a Green's function approach. Oxygen concentration is decomposed into a slowly-varying contribution, discretized by Finite Volumes over a coarse cartesian grid, and a rapidly-varying contribution, approximated analytically in grid-cells surrounding each vessel. Starting with simple test cases, we thoroughly analyze the resulting errors by comparison with highly-resolved simulations of the original transport problem, showing considerable improvement of the computational-cost/accuracy balance compared to previous work. We then demonstrate the model ability to flexibly generate synthetic data reproducing the spatial dynamics of oxygen in the brain parenchyma, with sub-grid resolution. Based on these synthetic data, we show that capillaries distant from the arteriole cannot be overlooked when interpreting POGs, thus reconciling recent measurements of POGs across cortical layers with the fundamental idea that variations of vascular density within the depth of the cortex may reveal underlying differences in neuronal organization and metabolic load.

Mesh stiffness calculation of defective gear system under lubrication with automated assessment of surface defects using convolutional neural networks

As typical failure mode of gear system, the tooth surface pitting, spalling can be detected in most long-running gear system, especially under heavy-load and high-speed condition, or under lubricant-starvation condition, the tooth pitting is characterized by irregular contour and random distribution. Most previous study on the defective gear system mainly based on manually detection of defective region, or just rely on geometric simplification of defects, leading to inaccurate results with low-efficient method, therefore, the machine-vision-based defect inspection method is proposed in the study of defective gear system. First, the pitting defects on gear tooth surface is detected and segmented based on involutional neural network U-net, then the tooth surface with segmented defective region is mapped to the elastohydrodynamic lubrication model of spur gear system, finally, the tribological behavior in addition to the mesh stiffness under lubrication condition of defective spur gear system are investigated and discussed. The results reveal that the machine-vision-based defects inspection could improve the accuracy and efficiency of the failure study for gear system.

A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization

_ The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. Keywords ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm

Brain Material Properties and Integration of Arachnoid Complex for Biofidelic Impact Response for Human Head Finite Element Model.

Finite element head models offer great potential to study brain-related injuries; however, at present may be limited by geometric and material property simplifications required for continuum-level human body models. Specifically, the mechanical properties of the brain tissues are often represented with simplified linear viscoelastic models, or the material properties have been optimized to specific impact cases. In addition, anatomical structures such as the arachnoid complex have been omitted or implemented in a simple lumped manner. Recent material test data for four brain regions at three strain rates in three modes of loading (tension, compression, and shear) was used to fit material parameters for a hyper-viscoelastic constitutive model. The material model was implemented in a contemporary detailed head finite element model. A detailed representation of the arachnoid trabeculae was implemented with mechanical properties based on experimental data. The enhanced head model was assessed by re-creating 11 ex vivo head impact scenarios and comparing the simulation results with experimental data. The hyper-viscoelastic model faithfully captured mechanical properties of the brain tissue in three modes of loading and multiple strain rates. The enhanced head model showed a high level of biofidelity in all re-created impacts in part due to the improved brain-skull interface associated with implementation of the arachnoid trabeculae. The enhanced head model provides an improved predictive capability with material properties based on tissue level data and is positioned to investigate head injury and tissue damage in the future.

Joint simplification of various types spatial objects while preserving topological relationships

Cartographic generalization includes the process of graphically reducing information from reality or larger scaled maps to display only the information that is necessary at a specific scale. After generalization, maps can show the main things and essential characteristics. The scale, use and theme of maps, geographical features of cartographic regions and graphic dimensions of symbols are the main factors affecting cartographic generalization. Geometric simplification is one of the core components of cartographic generalization. The topological relations of spatial features also play an important role in spatial data organization, queries, updates, and quality control. Various map transformations can change the relationships between features, especially since it is common practice to simplify each type of spatial feature independently (first administrative boundaries, then road network, settlements, hydrographic network, etc.). In order to detect the spatial conflicts a refined description of topological relationships is needed. Considering coverings and mesh structures allows us to reduce the more general problem of topological conflict correction to the problem of resolving topological conflicts within a single mesh cell. In this paper, a new simplification algorithm is proposed. Its peculiarity is the joint simplification of a set of spatial objects of different types while preserving their topological relations. The proposed algorithm has a single parameter — the minimum map detail size (usually it is equal to one millimeter in the target map scale). The first step of the algorithm is the construction of a special mesh data structure. On its basis for each spatial object a sequence of cells is formed, to which points of this object belong. If a cell contains points of only one object, its geometric simplification is performed within the bounding cell using the sleeve-fitting algorithm. If a cell contains points of several objects, geometric simplification is performed using a special topology-preserving procedure.

Estimation of relative permeability curves in fractured media by coupling pore network modelling and volume of fluid methods

The relative permeability of porous and fractured media is an important concept for various technological applications including hydrocarbon recovery, CO2 sequestration, hydrogen storage, hydrology, and microfluidics. In pore-scale simulations, relative permeability curves can be obtained either by pore-network modelling or direct numerical simulations (e.g., Navier–Stokes based and Lattice Boltzmann methods). While pore network models rely on several conceptual mathematical and geometric simplifications compared to more rigorous direct numerical methods, pore network modelling is often the only option to conduct the fast numerical simulation at a core-scale and regular desktop PC within a reasonable time frame. In this study, we analyse the ability of the developed hybrid pore network modelling and volume of fluid methods (VOF–PNM) model to accurately predict the two-phase flow displacement in a ramified system of fractures. This is done by conducting a number of 2D and 3D two-phase flow simulations with different viscosity ratios, contact angles, and capillary numbers. The VOF–PNM output with respect to history-matched relative permeability and capillary pressure curves is then compared to the reference conventional VOF simulations using a range of validation metrics. Such benchmarking allows concluding that the developed VOF–PNM solver can model flow through a system of fractures with an acceptable accuracy compared to the widely used VOF method. In turn, the conventional dynamic PNM algorithm failed to match the relative permeability and capillary pressure when compared to the reference VOF simulations. In terms of computational time, the non-parallelised hybrid VOF–PNM model is at least two orders of magnitude faster than parallelised VOF simulation and only one order of magnitude slower than the conventional dynamic PNM algorithm. The findings are beneficial for simulating two-phase flow through a fractured system at a core scale, as a design tool for microfluidic devices, and for further evolution of the VOF–PNM model into a comprehensive tool for transient multi-scale multi-physics simulations in coal.

Applying New Algorithms for Numerical Integration on the Sphere in the Far Field of Sound Pressure

For some sound sources, the function of the square of sound pressure amplitudes on the sphere in the far field is an integrable function or can be integrated with geometrical simplifications, so an exact or approximated analytical expression for the sound power can be calculated. However, often the sound pressure on the sphere in the far field can only be defined in discrete points, for which a numerical integration is required for the calculation of the sound power. In this paper, two new algorithms, Anchored Radially Projected Integration on Spherical Triangles (ARPIST) and Spherical Quadrature Radial Basis Function (SQRBF), for surface numerical integration are used to calculate the sound power from the sound pressures on the sphere surface in the far field, and their solutions are compared with the analytical and the finite element method solution. If function values are available at any location on a sphere, ARPIST has a greater accuracy and stability than SQRBF while being faster and easier to implement. If function values are available only at user-prescribed locations, SQRBF can directly calculate weights while ARPIST needs data interpolation to obtain function values at predefined node locations, which reduces the accuracy and increases the calculation time.

Modelling Air Cooling in a Module of Prismatic Li-Ion Cells

The control of temperature in lithium-ion stacks is of increasing industrial importance. In fact, an efficient thermal conditioning system ensures a homogeneous temperature distribution among the cells, preventing conditions which could hinder performance or led to temperature runaway and so a disruptive failure of the module. CFD models for thermal analysis can be useful in the design phase to optimize the module layout and to size the cooling system, as well as to support experimental campaigns in the definition of reduced-order thermal models to be implemented into the Battery Management System (BMS) for in-operation temperature prediction. The present work presents the development of a computational model of a 48V-20Ah 20-cell Li-ion module and its air-cooling system based on COMSOL Multiphysics software. Unlike most papers available in the literature, the individual cells are modelled by considering an internal ohmic heat generation term as a function of both state of charge, cell temperature and sign of the applied current (charge or discharge), exploiting experimentally validated data provided by the manufacturer in temperature-controlled conditions. In addition, the reversible thermal generation term due to entropic phenomena, which is often ignored in the literature, was computed alongside an irreversible term accounting for cell overpotentials. The entropic contribution, which was estimated experimentally and optimised computationally, resulted non-negligible when the module is cycled at ≤ 1C. To take into account the effect of the fan cooling system, the motion field was solved in COMSOL using a turbulent k-ε model. To ensure model accuracy, only minor geometrical simplifications were introduced to the fluid volume. The resulting geometrical complexity, made of manifold and narrow channels, called for special attention in the computational resolution. Assuming a decoupled behaviour, namely that the thermal evolution does not affect the flow field, allowed to use the stationary fluid-dynamic solution as the starting point for the time-dependent thermal simulation. The model was run under different experimental current profiles and the validation was performed on the entire module under effective operating conditions and not on a single cell under natural convection condition with the external environment, as is usually found in the literature. The computational data were found to be in good agreement with experimental measurements for all the cells even after charge-discharge cycles lasting more than two hours. The simulations provided insights to improve the positions of thermal sensors inside the module and to optimize the efficiency of the cooling system, that could not be done based on thermal measurements only. Figure 1

STRUCTURAL WEAKNESS OF THE TWENTY EYES AQUEDUCT IN THE WADI OF CARCAUZ (ALMERIA, SPAIN)

Abstract. The Twenty Eyes Aqueduct is a work of hydraulic engineering with several centuries of history. In the present work, the innovative Cloud-to-BIM-to-FEM methodology is applied, capable of converting BIM models, generated from point clouds, into finite element models. The point cloud was obtained using UAV photogrammetry, carrying out a 3D survey of the current state of the aqueduct and its surroundings. The point cloud obtained has served as the basis for the generation of an HBIM model that accurately represents the geometry of the aqueduct (Cloud-to-BIM). The HBIM model has been transformed into a finite element model that respects the uniqueness of the monument without excessive geometric simplifications (BIM-to-FEM). The structural analysis of the FEM model has been able to evaluate the instability conditions of the assembly in the event of an earthquake, although it does behave adequately in the face of wind actions.

Oilstone Processing and Its Impact on the Surface Texture of Cylinder Bore

AbstractHoning is one of the abrasive-based machining processes to remove material through the asperity interaction between numerous stochastic grains distributed on oilstone (also called honing stone) and workpiece. Therefore, the oilstone surface topography characterized by grain morphology, size, posture and position distribution, protrusion heights and etc. is of great significance to understand honing mechanism in terms of establishing an accurate kinematic model and further analyzing the oilstone property's impact on honing process and honed surface texture characteristics including groove density, roughness heights, and plateau/valley amplitudes. Conventionally, two typical approaches have been employed to establish the surface topography of abrasive-based cutting tools: experimentally microscopic observation and backward modeling/simulation from the assumed ideal distribution laws such as Gaussian or uniform distribution for stochastic grain characteristics. The first method is usually time-consuming and only measures surface topography within rather small area, whereas the second one is highly dependent on the authenticity of assumed statistical distribution laws. To overcome these weaknesses, the research proposed a functional forward method (FFM) to accurately predict surface topography of oilstone based on simulating its manufacturing processes in succession to avoid distributional assumptions and geometrical simplification. The methodology takes into consideration five important stochastic characteristics of oilstone including grain morphology, size, posture, position distribution, and grain wear during honing process, to guarantee the credibility, authenticity, and generality of the surface topography generated from honing. Based on the oilstone surface topography, the kinematic simulation method (KISM) was applied to analyze the honed surface texture characteristics of cylinder bore with oilstone samples under different stirring times. Therefore, the methodology bridges oilstone manufacturing parameters, oilstone surface topography, and further the honed surface texture to provide a fresh insight into the parameter’s optimization of the oilstone manufacturing process by achieving a better control on the honed surface texture of the cylinder bore.

Temperature Effect in the Babassu (Orbignya speciosa) Oil: A Physico-Chemical Study

Babassu (Orbignya speciosa) is a Brazilian palm with extraordinary importance in socioeconomic and ecologic terms. It is found in humid tropical areas, especially in degraded landscapes. There are several uses for babassu coconut and babassu oil. However, their immense potential for large-scale providing other industrial products still needs to be explored due to the necessity for modern scale planning and deep knowledge of vast spectrum thermodynamic properties. This paper gathers a new experimental physico-chemical study of the temperature effect on two critical properties, density and ultrasonic velocity for babassu oil, due to its rising economic significance and a high potential for intensive farming in regions with low economic resources. We consider how accurately different theoretical prediction methods work due to modern processes, design, and algorithm simulations being strongly computer-oriented. The Agrawal-Thodos equation for density and Collision Factor Theory for ultrasonic velocity was selected, mainly attending to ease of use and range of application. We observed a good response at the studied conditions, despite geometrical simplifications into triglyceride molecules and using estimated critical magnitudes by molecular group contribution approach. A broad comparison was made with disposable open literature thermodynamic data, showing an essential dispersion of data, and highlighting the quality of the experimental data presented in this work.

Transition from volcano-sagging to volcano-spreading

Many volcanoes exhibit evidence of long-term deformation driven by gravity. This deformation influences the development of magmatic plumbing systems and volcanic vent locations, and it manifests as earthquakes and ground surface motions. Two end-member deformation styles are volcano sagging (flexure) and volcano spreading. Previous modeling studies indicated that the mechanical and geometric properties of basement rocks below the volcano can control whether a volcano spreads or sags, but these works differed on exactly how. Furthermore, evidence in nature that a volcano may progress from sagging to spreading as it grows has not been tested experimentally. Using Digital Image Correlation (DIC) and a revised scaling approach, we here revisit the classic experiments in which a sand–plaster cone is emplaced upon a basement comprising an upper brittle layer and a lower ductile layer. In a first experiment series, we emplaced the cone instantaneously. By normalizing both brittle and ductile layer thicknesses to cone height, we provide new insight into the dimensionless geometric controls on spreading and sagging. In a novel second experiment series, we emplaced the cone incrementally. We show that as a growing cone migrates through the dimensionless geometric parameter space, its deformation style changes successively in time from sagging to spreading. DIC shows that the horizontal velocity of the cone flank increases linearly or non-linearly during cone construction, but it decreases exponentially after construction ceases. Despite their geometric simplification and the omission of several sedimentological, magmatic, and regional-tectonic processes, our models' results are compatible with a range of observations of volcanoes on Earth and Mars. In particular, they help to explain: (i) why lithosphere-scale loading results in sagging rather than spreading, and (ii) why ocean island volcanoes of >2-3 km in height develop stellate forms and rift zones.

The development of multi-physics approach with Monte Carlo and computational fluid dynamics coupling for reactor cores

Highly accurate solutions for neutronic and thermal hydraulic phenomena in reactor cores certainly improve the safety conclusions about the reactors. The coupling methodology of governing physics of these two fields by using Monte Carlo and computational fluid dynamics (CFD) methods was described in this study with its application to Istanbul Technical University (ITU) TRIGA Mark II Research Reactor. As a different approach, instead of not modelling top and bottom grid plates of the reactor that is the common practice for geometrical simplification, the effect of grid plates on flow hydrodynamics was included into the conjugate heat transfer calculations as boundary conditions. The investigation on the effect of core element temperature, cross sections, and S(α,β) thermal scattering data showed the need of proper coupling of neutronic and thermal hydraulic fields since 30–45% relative error on excess reactivity value of the ITU research reactor was observed. With the developed loose coupling model, 483 individual cross section and 483 different S(α,β) thermal scattering data were generated by using NJOY nuclear data generation code and MAKSXF and OTF utilization tools of MCNP6.2 code. The reactivity loss against power increase in research reactor value of this study showed 6% relative error with loose coupling comparing to maximum 50% without coupling.