3D Model Research Articles

Mass transfer of bilirubin and bovine serum albumin in hollow fiber membrane module of artificial liver

Understanding the mass transfer behaviors in hollow fiber membrane module of artificial liver is important for improving toxin removal efficiency. A three-dimensional numerical model was established to study the mass transfer of small molecule bilirubin and macromolecule bovine serum albumin (BSA) in the hollow fiber membrane module. Effects of tube-side flow rate, shell-side flow rate, and hollow fiber length on the mass transfer of bilirubin and BSA were discussed. The simulation results showed that the clearance of bilirubin was significantly affected by both convective and diffusive solute transport, while the clearance of macromolecule BSA was dominated by convective solute transport. The clearance rates of bilirubin and BSA increasd with the increase of tube-side flow rate and hollow fiber length. With the increase of shell-side flow rate, the clearance rate of bilirubin first rose rapidly, then slowly rose to an asymptotic value, while the clearance rate of BSA gradually decreased. The results can provide help for designing structures of hollow fiber membrane module and operation parameters of clinical treatment.

Applying a 2D Variational Rigid Block Modeling Method to Rubble Stone Masonry Walls Considering Uncertainty in Material Properties

ABSTRACT The computational cost of detailed micro-modeling of masonry structures can be minimized with rigid block analysis methods, where a variational formulation is particularly interesting as it allows for nonlinear pushover analysis. This work investigates the accuracy of this modeling method in predicting the response of rubble stone masonry walls in shear compression tests. To this end, we extend the mathematical programming problem by including cohesion, compressive strength, and tensile strength in the formulation and implement an improved pushover procedure, which guards compatibility of the displaced shape and crack patterns at the intersection of elastic and rigid contact models. Additionally, we reduce model uncertainty, introduced through sampling material properties with distributions estimated based on results from tests on mortar masonry joints and on mortar specimens, by correlating the predicted crack patterns with the observed ones. The findings show that (i) model falsification based on crack pattern is an effective method for reducing the parameter uncertainty of stone masonry walls; and that (ii) the improved pushover procedure predicts the pre- and post-peak response of stone masonry walls very well with analysis times of less than 20 minutes for 2D models where stones and mortar are modeled explicitly.

A dual-modified implicit time integration method for three-dimensional impact modelling within the framework of the SBFEM

This paper presents a dual-modified implicit time integration method (DMITIM) for three-dimensional impact modelling within the framework of the scaled boundary finite element method (SBFEM). Controlled by a single parameter, this method effectively dissipates high-frequency responses in impact problems without affecting low-frequency responses, ensuring time integration stability. To address the issue of false oscillations during impacts, additional Lagrange multipliers are introduced to supplement the velocity and acceleration corrections at each time integration step. For nonmatching meshes, by leveraging the mesh flexibility of the SBFEM, a rapid and straightforward remeshing method is utilized to establish a node-to-node contact scheme. The numerical results demonstrate that the DMITIM significantly reduces false oscillations in the velocity and contact force during impact events. It also showcases the flexibility of the SBFEM in handling nonmatching meshes. The combination of the SBFEM with the DMITIM provides enhanced simulation accuracy and stability for impact problems compared to the results from ABAQUS using the HHT-α time integration algorithm.

3D numerical modelling and laboratory study of flow field induced by a group of submerged vegetations

The three-dimensional (3D) numerical modelling in an open channel flow field of a group of submerged vegetations using computational fluid dynamics (CFD) platform of FLOW-3D HYDRO was performed in this study. A set of acoustic Doppler velocimetry (ADV) measurements have been conducted as benchmark to validate the numerical model. A quantitative comparison was performed on several hydrodynamic variables that impacted the vegetated open channel flow, such as flow depth, streamwise water velocity, turbulent intensity, and Reynolds shear stress. In the numerical analysis, the flow turbulence was treated using the RANS approach (within RNG k-ε); while the Volume Of Fluid (VOF) method was used to track the air-water interface. Structured meshes with hexahedral elements were used to discretize the channel geometry. In the findings, the numerical model reasonably reproduced the flow field and presented corresponding agreement with the experimental turbulent structures. This study showed that the differences in results between various analyses were all less than 10% and concludes that the presented numerical approach can be utilised as an efficient tool for simulations of the flow field within a vegetation patch (i.e. by using the simplified RANS approach).

Assessment of combustion development and pollutant emissions of a spark ignition engine fueled by ammonia and ammonia-hydrogen blends

Ammonia is a promising carbon-free fuel for internal combustion engines, but its combustion properties are very poor if compared with conventional fuels. Thus, hydrogen enrichment can be the proper solution in order to overtake the issues related to neat ammonia fueling. In this paper, the authors use a 3D numerical model coupled with a chemical kinetic approach to assess the operation of a light-duty spark-ignition engine fueled by neat ammonia and ammonia-hydrogen blends (up to 15% of hydrogen by volume). The proposed model reproduces the analyzed experimental operating points with a good accuracy. As example, considering the in-cylinder pressure peak, the difference between the calculated maximum pressure and the mean measured one ranges between 1.6% and 6.8% for the examined cases. The 3D approach enables an in-depth analysis of flame development that also allows to detect the effect of hydrogen enrichment on both combustion development and emissions. Unburned ammonia trends are captured, reproducing the measured decrease of ammonia emissions for increasing hydrogen enrichment. The emissions of NOx are also well reproduced. Simulations also highlighted that dissociation of ammonia produces H2, which is detected in the combustion chamber also if neat ammonia is burned and, at least according to the chemical mechanism used, not all the produced hydrogen oxidizes. Only very small quantities of intermediates related to ammonia combustion are calculated at exhaust valve opening, decreasing for an increasing hydrogen content in the fuel mixture.

Effects of Fuel Bed Slope and Wind Direction on Flame Spread Characteristics Over a Bed of Pinus Silvestris Needles

ABSTRACT Important factors influencing the flame characteristics over a bed of dry pine needles from Siberian Boreal forests (Pinus silvestris) in lab-scale is provided. Factors such as slope of the fuel bed, wind velocity, and direction of spread (concurrent flow or opposed flow), which play significant roles in determining the flame spread rate, have been investigated. Systematic lab-scale experiments have been carried out in a wind tunnel with varying air velocities and on an inclined fuel bed in still air. Numerical simulations have been carried out using a three-dimensional physics-based flame spread model available in Fire Dynamics Simulator, and the results are validated against a wide range of measured flame characteristics. The validated numerical model is then used to simulate concurrent flow and opposed flow flame spread over a sloping fuel bed in the presence of wind. Gas phase temperature on and above the bed surface and total and radiative heat flux measurements for concurrent flow and opposed flow flame spread under the influence of wind as well as for upslope flame spread in still air are presented. A stationary flame propagation mode is observed for a flat fuel surface. Predicted temperature, flow, and species concentration fields have been reported for selected cases to understand the flow and flame dynamics in gas phase and within the fuel beds. These data serve as a validation of the numerical model used for simulating the experimental cases. Further, it can be used to validate popular fire models such as Fire Foam and Fire-Star 3D.

Modelling and optimization of an innovative facility for automated sorting of aluminium scraps

The growing demand for aluminium worldwide makes aluminium recycling critical to realising a circular economy and increasing the sustainability of our world. One effective way to improve the impact of aluminium recycling is to develop cost-efficient automated sorting technologies for obtaining pre-defined high-quality aluminium scrap products, thus reducing undesirable downcycling and increasing environmental/economic benefits. In this work, an innovative facility, which includes singulation, sensor scanning, and ejection, is optimised for the automated sorting of aluminium scraps. The sorting facility is computationally studied by a virtual experiment model based on the discrete element method. The model considers particle-scale dynamics of complex-shaped scraps and mimics the automated operation of the facility. Based on virtual experiment modelling, the flow of scrap is optimized by computation, with the feasible operation of the sorting facility being proposed. Accordingly, the sorting facility has been built and model predictions are confirmed in actual operation.

Numerical and experimental studies on the motion and deposition of collapsing rock clusters: Effect of rock particle sphericity

In this paper, a series of experimental and numerical tests are carried out to study the effect of rock particle sphericity on the motion and deposition of collapsing rock clusters. To this end, three kinds of typical rock particles with different sphericity (i.e., pebble, rubble and block) are used for experiments and a special laboratory setup that involves a chute with two inclined sections is designed to reproduce their natural motion. Then, a three-dimensional modelling of collapsing rock cluster incorporating rock particles with different sphericity is developed using spherical harmonics function reconstruction. The sphericity of particle effect on the motion and deposition of rock cluster is studied experimentally and numerically in terms of the average velocity and deposition zone. There has a good agreement between them. In addition, some potential factors such as rock particle size, cluster mass and component are investigated and discussed. The obtained results may facilitate the current knowledge on the dynamic behavior of collapsing rock clusters effect of particle sphericity.

Geometric changes and clinical risk factors from aortic arch to proximal internal carotid artery between normal subjects and moderate right carotid plaques.

The anatomical features spanning from the aortic arch to the proximal carotid artery and the associated cardiovascular risks might significantly influence the development of right carotid plaque. Our research aimed to compare these anatomical and risk factors between individuals with no carotid plaque and those with moderate right-side carotid plaque within a Korean cohort. We conducted a retrospective, cross-sectional analysis involving 413 participants, categorized into a normal group (n = 339) and a right moderate carotid plaque group (defined as > 50% stenosis based on NASCET criteria) (n = 74). We collected data on cardiovascular risk factors and conducted laboratory tests. A 3D model of the carotid artery was constructed using cranio-cervical computed tomography angiography (CTA) data through semi-automated software. Measurements taken on this 3D model included the common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), and carotid artery bifurcation (CAB) in terms of maximal vascular diameter, sectional area, angles of carotid bifurcation and ICA, and carotid tortuosity. When compared with the normal group, individuals in the right moderate carotid plaque group exhibited smaller angles at the carotid bifurcation, larger CCA diameter and sectional area (p < 0.01), advanced age, and a higher incidence of hypertension, diabetes, and stroke history (p < 0.05), along with reduced glomerular filtration rate (GFR) (p < 0.001). Multivariate analysis revealed that the sectional area of the bifurcation, calcification of the aortic bulb, and GFR were independently associated with the presence of right moderate carotid plaque (p < 0.01). Statistical analyses disclosed significant differences in both clinical risk factors and geometric changes in the region extending from the aortic arch to the proximal carotid artery among subjects with right moderate carotid plaque when compared to those without.

Physical field of dual-frequency ultrasonic vibration and its effect on solidification behavior of MgZnY alloy

A three-dimensional model was established for coupled simulation of the physical field in a liquid subjected to dual-frequency ultrasonic vibration. The effect of sonotrode position, tip shape, and vessel on the distributions of acoustic pressure and acoustic streaming were clarified. The influence of physical fields on the solidification behavior of Mg alloy was investigated. The simulation results show that the interaction between the two ultrasounds was weakened as the sonotrode spacing increased, and the radiating angle had little effect on the acoustic pressure distribution. The volume mean pressure in the liquid radiated by the plane tip was the smallest, but with the greatest acoustic streaming intensity. In contrast, the spherical tip radiated the highest volume mean pressure in the liquid medium, but the acoustic streaming was severely suppressed. Attributed to the smooth spherical surface that facilitated fluid flow, high flow velocity could be achieved using a round-bottomed crucible without severe acoustic pressure attenuation. The experimental results show that cavitation and acoustic flow caused the coarse dendrites to turn into fine isometric crystals. The acoustic flow accelerated the melt flow and alleviated the segregation and precipitation of Zn elements, leading to a decrease in the content and size of the second phase in the billet.

Phosphodiesterase-4 inhibitors increase pigment cells proliferation and melanization in cultured melanocytes and within a 3D skin equivalent model

Vitiligo is a common chronic autoimmune disease characterized by white macules and patches of the skin, having a negative impact on patients' life, and without any definitive cure at present. Identification of new compounds to reverse depigmentation is therefore a pressing need for this disease. The pharmacologic compounds phosphodiesterase-4 inhibitors (PDE4i) are small molecules with immunomodulatory properties, used for treatment of inflammatory dermatoses. PDE4i have shown repigmentation effects in vitiligo patients, in some case reports. We characterized the proliferative and melanogenic potential of two known PDE4i, crisaborole and roflumilast, and of a more recently designed compound, PF-07038124. We used two in vitro model systems, the primary human melanocyte culture and a 3D co-cultured skin model (MelanoDermTM), with an exploratory testing platform composed of complementary assays (spectrophotometry, melanin and proliferation assays, immunostaining, Fontana-Masson staining, qRT-PCR, western blot and whole transcriptome RNA-Sequencing). We identified that the treatment with PDE4i was associated with increased melanocyte proliferation and melanization in both in vitro models, and with increase in the melanogenic genes and proteins expression in cultured melanocytes. These effects were found to be enhanced by addition of α-MSH. Our findings support the further evaluation of PDE4i with or without α-MSH agonists in vitiligo trials.

3D young female waist-leg modelling based on a hybrid 3d-scan and 2d-image approach

To enhance the pant fit for individuals, this study proposed a method for constructing a 3D waist-leg mannequin based on body images and 3D point-clouds of young women. A total of 288 females aged 18–25 were measured using 3D body scanner and 2D image-shooting method to obtain 3D point-cloud data and body images. The 3D point-cloud data were analyzed to extract 33 sectional curves, including the cross-sectional curves and crotch curve, to identify key points on each curve. For the body images, key parameters related to curve position and shape, such as height, width, and thickness, were automatically extracted for modelling parameters. Curve generation rules were established based on correlation and regression analysis of the key points. The Individualized 3D mannequin was simulated by adjusting the curve centers at each characteristic position to align with the body images, and was validated by comparing the body sizes of the 3D mannequin with the actual measurements. The results indicated that the final simulated mannequin accurately represents the basic characteristics of the waist-leg shape, with significance values above 0.05, which showed the feasibility of the modelling method. Furthermore, 73.9% of the samples had an absolute error of less than 1 cm between the 3D mannequin and the actual measurements. This study can facilitate 3D body modelling from body images, and provide a reference to develop individualized apparel patterns for clothing customization.

A multi-view ensemble machine learning approach for 3D modeling using geological and geophysical data

Geophysical data are often integrated into geological data for 3D modeling of underground spaces. However, the existing single-view approach means it is difficult to adequately fuse the valid information between the two types of data, and the complexity of lithological decoding and classification is high. To address this issue, a multi-view ensemble machine learning (ML) framework is proposed. Initially, the original dataset of lithology prediction is constructed by aligning geological and geophysical data with different spatial scales. Next, the dataset is divided into three datasets of structural strength, density, and moisture content according to the lithology properties of the geophysical data. The proposed framework is then used to capture the lithologic characteristics under different views to achieve the prediction of lithologic labels. In this process, a self-attentive mechanism is used to adaptively fuse the valid information under each view. To validate the proposed framework, it is applied to a project in Jiaxing, Zhejiang Province, China. Compared with existing ML methods, the proposed multi-view ensemble ML framework improves modeling accuracy and constructs models with low uncertainty. The framework can be extended to other multi-source data fusion tasks across geoscience domains.

An active SLAM with multi-sensor fusion for snake robots based on deep reinforcement learning

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

Optical fiber shape reconstruction algorithm based on 3D Euler spiral model

Abstract This paper presents a reconstruction algorithm that uses a 3D Euler spiral model to construct the microsegment of a 3D space curve to improve the reconstruction efficiency. Euler spiral is a curve whose shape information changes linearly with arc length, which improves the current assumption of the reconstruction algorithms that the shape information of the micro-segment is constant, and effectively reduces the number of interpolations required, and thus improves the efficiency of the shape algorithm. To verify the effectiveness of this algorithm, a method for constructing random space curves is proposed. Three random space curves of different lengths are reconstructed and the results are compared with the reconstruction algorithms based on the homogeneous transformation matrix and Bishop frame. The results show that this model can significantly improve the efficiency of the algorithm. Under the random space curves of 1 m, 10 m, and 100 m, the efficiency is enhanced by about 15, 27, and 30 times respectively. Prepare a shape sensor with a length of 1465mm to verify the highest reconstruction accuracy of 3D Euler spiral model, which is consistent with the simulation results. The results provide a solid foundation for further research in the field of shape sensing, and show potential for promoting the development of applications that rely on real-time shape measurements.&amp;#xD;

Toll-like receptor 21 in Labeo rohita recognizes double-stranded RNA and lipopolysaccharides by engaging the critical motifs in the LRR domain and gets activated against bacterial assaults

TLR (Toll-like receptor)-21 is a non-mammalian TLR and exhibits a unique function within the innate immune systems of fishes, birds, and amphibians. Despite its important role as PRR (pattern recognition receptor), research on TLR21 in many fish species, as well as in rohu (Labeo rohita), remains relatively limited. This article describes the molecular cloning of LrTLR21 (TLR21 in L. rohita), 3D (3-dimensional) modelling of its LRR (leucine-rich repeat)-regions, prediction of LRR18 to LRR20 as the LPS (lipopolysaccharide)-binding site, and LRR1 to LRR5 and LRR21 to LRR23 as the poly I:C (polyinosinic-polycytidylic acid) binding sites. It also describes the response of LrTLR21 in response to Aeromonas hydrophila and Edwardsiella tarda infections and PAMPs (pathogen-associated molecular patterns) (LPS and poly I:C)-stimulations. The ORF (open reading frame) of the LrTLR21 comprises 2955 nucleotides, encoding 984 aa (amino acid) residues with molecular weight and isoelectric point of 113.791 kDa and ∼8.79, respectively. Domain analysis of the deduced LrTLR21 displayed the existence of a signal peptide (residues 1–24), 26 LRR regions (residues 61–685), a TM (transmembrane) domain (residues 736–758), and a TIR (Toll/interleukin-1 receptor) domain (residues 790–937). The 3D model of LrTLR21-LRR regions has parallel β-sheets and few α-helices. Phylogenetically, LrTLR21 is closely related to the Onychostoma macrolepis and Carassius gibelio TLR21, and during ontogenesis, it is expressed in most of the developmental stages. In rohu fingerlings, it is consistently expressed in all examined tissues viz., skin, liver, heart, blood, eye, muscle, kidney, intestine, brain, gill, and spleen. Upon exposure to E. tarda and A. hydrophila infections, as well as LPS and poly I:C stimulations, the expression of LrTLR21 is significantly (p < 0.05) increased in the blood, kidney, liver, and gill. In the RBCs (red blood cells), PBLs (peripheral blood leukocytes), and kidney macrophages, LrTLR21 is also significantly induced following in-vivo and in-vitro LPS and poly I: C-stimulations. These findings on LrTLR21 are the first ones showing its structural insights and PAMPs binding motifs and its key role in recognizing pathogens and their PAMPs in RBCs, PBLs, and macrophages.

Machine learning framework for high-resolution air temperature downscaling using LiDAR-derived urban morphological features

Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352 °K and MAE of 0.215 °K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

Research on cyclic behavior of precast bridge piers with ECC-socket connections

Precast bridge piers with socket connections (SCPs) have been widely studied and certainly applied in recent years. To reduce the punching failure of socket connections, decrease their construction difficulty, and improve their application in complex environment areas, the embedment depth of SCPs needs to be decreased and the cyclic behavior of SCPs needs to be improved. In this study, engineered cementitious composite (ECC) was firstly introduced to the SCPs, a novel precast bridge piers with ECC-socket connections (SCEs) were proposed. Subsequently, 3D finite element models (FEMs) were established and validated against experimental results. Then, a detailed parametric study was conducted to investigate the cyclic behaviors of SCEs, including the embedment depth, ECC height, axial compression ratio, reinforcement ratio, and concrete compressive strength. Finally, a simplified calculation model was proposed to evaluate the bearing capacity and failure mode of SCEs. The results show that the shear damage of the embedded section is more severe for traditional shallow socket connections. Compared to traditional SCPs, the cyclic behavior of SCEs is better and influenced by ECC height and embedment depth, as well as axial compression ratio, reinforcement ratio, and concrete compressive strength. The simplified calculation model is suitable to evaluate the bearing capacity and failure mode of SCEs.

Fatigue assessment of structural components through the Effective Critical Plane factor

The integrity assessment of structural components under complex loading conditions relies on the evaluation of the fatigue damage typically arising from stress concentrations, such as geometric irregularities, notches, weld beads, grooves etc.. Various methodologies, including the Notch Stress Approach (NSA), the Theory of Critical Distances (TCD), the Strain Energy Density (SED), and the Critical Plane (CP) concept, have been pivotal in assessing fatigue strength for notched and welded components. Recent works combine some of the above mentioned methodologies, while other works propose to vary the embedded parameters accounting for the loading type or the fatigue lives, trying to improve the accuracy of the fatigue assessment process. This paper introduces a novel approach, the Effective Critical Plane (ECP), which is founded on the critical plane concept. The CP factor is, however, calculated starting from an averaged, over a small volume, stress–strain field. The size of the averaging volume is assumed to be a material parameter and is determined by a best fitting procedure over different experimental data sets. The novel approach is illustrated by means of the Fatemi-Socie and the Smith-Watson-Topper CP damage factors. Its potential application to other CP formulations is straightforward, as well. Literature experimental data for low carbon steel specimens possessing different notches and loading conditions are used to validate the method’s capability in accurately determining the fatigue life and to set the radius of the averaging volume for the given material and CP parameter. A spherical volume or circular area are used in case of fully 3D or 2D numerical models, respectively. Results are compared to those of some of already existing methods, namely SED, TCD and the Modified Wöhler Curve Method.