Calculation Process Research Articles

Multi-receptive-field physics-informed neural network for complex electromagnetic media

Acquiring the electromagnetic response of intricate media at the nanoscale constitutes a pivotal phase in the design intricacies of nanophotonic apparatuses. Conventional numerical algorithms often necessitate intricate and specialized treatments to accommodate the unique properties of the medium, coupled with substantial computational time and resource demands. In recent years, the advent of deep learning technology has heralded numerous advancements in the domain of computational electromagnetics, albeit with a scarcity of solvers tailored for versatile complex media. Consequently, this study introduces an innovative multi-receptive-field physics-informed neural network (MRF-PINN) designed to tackle nano optical scattering predicaments inherent in media exhibiting dispersion, inhomogeneity, anisotropy, nonlinearity, and chirality. This framework adeptly captures electromagnetic perturbations surrounding scatterers via variable-scale receptive fields, thereby enhancing prediction precision. Within the training regimen, a scale balancing algorithm is proposed to expedite network convergence. Empirical findings demonstrate that a fully trained MRF-PINN proficiently reconstructs electromagnetic field distributions within complex nanomaterials within a mere tens of milliseconds of inference time. Such quasi real-time capabilities herald a novel approach to supplant the arduous forward calculation processes inherent in nanomaterial design workflows.

Evaluating the Economic Impact of the PedAMINES App in Reducing Medication Errors in Pediatric Emergency Care: Cost-Effectiveness Analysis.

The administration of drugs in pediatric emergency care is a time-consuming process and is associated with a higher occurrence of medication errors compared with adult care. This is attributed to the intricacies of administration, which involve calculating doses based on the child's weight or age. To mitigate the occurrence of adverse drug events (ADEs), the PedAMINES (Pediatric Accurate Medication in Emergency Situations; Geneva University Hospitals) mobile app has been developed. This app offers a step-by-step guide for preparing and administering pediatric drugs during emergency interventions by automating the dose calculation process. Although previous simulation-based randomized controlled trials conducted in emergency care have demonstrated the efficacy of the PedAMINES app in reducing drug administration errors, there is a lack of evidence regarding its economic implications. This study aims to evaluate the cost-effectiveness of implementing the PedAMINES app for 4 emergency drugs: epinephrine, norepinephrine, dopamine, and midazolam. The economic evaluation was conducted by combining hospital data from 2019, previous trial outcomes, information extracted from existing literature, and PedAMINES maintenance costs. The cost per avoided medication error was calculated, along with the number of administrations needed to achieve a positive return on investment. Subsequently, Monte Carlo simulations were used to identify the key parameters contributing to result uncertainty. The study revealed the number of preventable errors per administration for the 4 examined drugs: 0.513 for epinephrine, 0.484 for norepinephrine, 0.500 for dopamine, and 0.671 for midazolam. The cost-effectiveness ratios per ADE prevented were computed as follows: US $4808 for epinephrine, US $9705 for norepinephrine, US $6957 for dopamine, and US $2074 for midazolam. Accounting for the economic impact of ADEs, the analysis estimated that 16 administrations of epinephrine, 17 of norepinephrine and dopamine, and 13 of midazolam would be required to attain a positive return on investment. This corresponds to roughly one-third of the annual administrations at a major university hospital in Switzerland. The primary factors influencing the uncertainty in the estimated cost per ADE include the cost of maintenance of the app, the likelihood of an ADE resulting from an administration error, and the frequency of underdosing in the trial's control group. A dedicated mobile app presents an economically viable solution to alleviate the health and economic burden of drug administration errors in in-hospital pediatric emergency care. The widespread adoption of this app is advocated to pool costs and extend the benefits on a national scale in Switzerland.

GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS–assisted intelligent communication system is ensured in the complex wireless transmission scenario.

A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method’s accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method’s advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method’s 5.1% and 8.3%.

Monte Carlo dose calculation for photon and electron radiotherapy on dynamically deforming anatomy.

Dose calculation in radiotherapy aims to accurately estimate and assess the dose distribution of a treatment plan. Monte Carlo (MC) dose calculation is considered the gold standard owing to its ability to accurately simulate particle transport in inhomogeneous media. However, uncertainties such as the patient's dynamically deforming anatomy can still lead to differences between the delivered and planned dose distribution. Development and validation of a deformable voxel geometry for MC dose calculations (DefVoxMC) to account for dynamic deformation in the dose calculation process of photon- and electron-based radiotherapy treatment plans for clinically motivated cases. DefVoxMC relies on the subdivision of a regular voxel geometry into dodecahedrons. It allows shifting the dodecahedrons' corner points according to the deformation in the patient's anatomy using deformation vector fields (DVF). DefVoxMC is integrated into the Swiss Monte Carlo Plan (SMCP) to allow the MC dose calculation of photon- and electron-based treatment plans on the deformable voxel geometry. DefVoxMC is validated in two steps. A compression test and a Fano test are performed in silico. Delta4 (for photon beams) and EBT4 film measurements in a cubic PMMA phantom (for electron beams) are performed on a TrueBeam in Developer Mode for clinically motivated treatment plans. During these measurements, table motion is used to mimic rigid dynamic patient motion. The measured and calculated dose distributions are compared using gamma passing rate (GPR) (3% / 2mm (global), 10% threshold). DefVoxMC is used to study the impact of patient-recorded breathing motion on the dose distribution for clinically motivated lung and breast cases, each prescribed 50Gy to 50% of the target volume. A volumetric modulated arc therapy (VMAT) and an arc mixed-beam radiotherapy (Arc-MBRT) plan are created for the lung and breast case, respectively. For the dose calculation, the dynamic deformation of the patient's anatomy is described by DVFs obtained from deformable image registration of the different phases of 4DCTs. The resulting dose distributions are compared to the ones of the static situation using dose-volume histograms and dose differences. DefVoxMC is successfully integrated into the SMCP to enable the MC dose calculation of photon- and electron-based treatments on a dynamically deforming patient anatomy. The compression and the Fano test agree within 1.0% and 0.1% with the expected result, respectively. Delta4 and EBT4 film measurements agree with the calculated dose by a GPR>95%. For the clinically motivated cases, breathing motion resulted in areas with a dose increase of up to 26.9Gy (lung) and up to 7.6Gy (breast) compared to the static situation. The largest dose differences are observed in high-dose-gradient regions perpendicular to the beam plane, consequently decreasing the planning target volume coverage (V95%) by 4.2% for the lung case and 2.0% for the breast case. A novel method for MC dose calculation for photon- and electron-based treatments on dynamically deforming anatomy is successfully developed and validated. Applying DefVoxMC to clinically motivated cases, we found that breathing motion has non-negligible impact on the dosimetric plan quality.

An improved generalized flexibility matrix-based damage identification technique for derrick steel structures

An improved generalized flexibility matrix-based method using matrix partitioning technique for structure damage identification is proposed. As an effective damage identification method, the generalized flexibility matrix requires fewer vibration frequencies and corresponding modal shapes compared with the traditional flexibility matrix method. By introducing a new partitioning technique of the matrices involved in the constructive process of the governing equation, the improved method simplifies the process of calculation with a dramatic reduction in computational cost while maintaining accuracy of identification results. In the case of incomplete measured modal data, an improved matrix partitioning technique based on the model expansion method is applied and the damaged indicators can be identified using partial measured modal data. A K-type derrick steel structure in service is considered as a numerical example for inspection the validity and feasibility of the proposed method. Numerical results show better accuracy and efficiency of this method for various damage scenarios, and can be used for damage diagnosis or health monitoring of other large structures.

Research on working mechanism and structural optimization of high speed bearing of tricone bits based on finite difference method

Abstract In order to analyze the working mechanism of the floating sleeve bearing of the cone bit, the life of floating sleeve bearing was tested. The experimental results show that adhesive wear will form on the bearing surface with the increase of temperature, which is the main cause of bearing failure. And this paper establishes the Reynolds equation and energy equation of the oil film based on the theory of dynamic pressure lubrication of sliding bearings. The equation was numerically solved based on the finite difference method. It determines the rotational speed ratio at the equilibrium based on the trend of the frictional torque. And then corrects the eccentricity in the iterative calculation process so as to analyze the tribological performance of the bearings in equilibrium under different clearance ratios. Among them, the improper configuration of the internal and external clearance ratio leads to the increase of the friction torque and the reduction of the bearing capacity. For floating sleeve bearings, the configuration with large internal clearance and small external clearance should be adopted. Finally, the bearing structure is optimized and numerical calculations show that the tribological performance of the new type bearings is significantly improved compared with that of ordinary cone bit bearings.

Global buckling bearing capacity of corroded H-section steel beams

To understand the effect of seawater corrosion on the stability of steel beams, a global buckling bearing capacity test is conducted on steel beams corroded via an electrochemical method while considering the initial geometric imperfections. The equivalent remaining thickness of the corroded steel beam is calculated based on the yield load obtained from material property tests. Subsequently, this novel approach for calculating the corrosion thickness is utilized to establish a numerical model to simulate the global buckling bearing capacity of the steel beam. Employing this method enables a parameter analysis to be conducted. Furthermore, a new formula for the global buckling of the ultimate bearing capacity of steel beams is proposed based on the differential equilibrium equation of the microdeformation of steel members subjected to bending and torsion. The virtual work principle is employed to develop this formula, which not only simplifies the calculation process, but also assigns clear physical significance to each term.

An LQR‐Lagrange Algorithm Generated by Interdisciplinary Integration With Optimal Control and Optimization

ABSTRACTInterdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. Based on the premise of optimal control, the state equation corresponding to Lagrange Algorithm is established with the Karush–Kuhn–Tucker (KKT) conditions as the objective. As an optimal control method, linear quadratic regulator (LQR) is utilized to control the calculation process, and an innovative LQR‐Lagrange Algorithm is proposed. The Lyapunov stability criterion is applied to analyze the convergence, and it is proved that the proposed LQR‐Lagrange Algorithm is bound to converge as long as the parameter matrices and are positive definite. The analysis indicates that the influence of parameters in LQR‐Lagrange Algorithm on the calculation speed is monotonic, and the elements in and has no effect on the convergence. Therefore, the proposed algorithm has a monotonic and user‐friendly parameter tuning strategy. It perfectly tackles the game between parameter tuning strategy and calculation speed, and cracks the difficulties and dilemmas of conventional algorithms in this issue, realizing a win‐win situation.

Study on the application amount optimization of water-based friction modifier based on field experiments

The utilization of friction modifiers (FMs) can reduce the adhesion coefficient of the top-of-rail to a moderate level, yielding a series of benefits. In this study, the influence of the FM application amount on the adhesion coefficient, noise, and braking distance was explored under field conditions. Results showed that when the FM application amount exceeded threshold values, the braking distance of the locomotive was greatly extended and the friction control performance of FM reached saturation. The FM application amount and application frequency were put forward through a simplified calculation process, which gave values of 0.33 mL/axle and 10axles/application, respectively. These application parameters achieve the desired intermediate friction level along with appropriate reductions in the noise and the lateral force.

Proposing New Standard and ANFIS Calculation Approaches for Precast Concrete Connections with Steel Tube Elements: Lessons from Experimental Studies

The importance of establishing a proper method for calculating newly studied structural elements and primarily translating the results of experimental and numerical analyses into practical standards poses a significant challenge for structural researchers. In this study, which focuses on the importance of previously studied connections for the precast industry as a case study, two approaches are proposed for calculating the capacity of elements connected by rectangular steel tubes. The first approach involves a step-by-step calculation for analyzing the forces and capacities of different steel tube or concrete section parts under bending and shear, aiming to establish a standard calculation approach. Despite its complexity, the standard calculation approach has proven its accuracy by successfully solving examples with features similar to those of the experimental tests describing the process. The second approach relies on a look-up table generated from experimental data, developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for interpreting the data. ANFIS not only facilitates the evaluation of the capacity of non-experimentally tested elements but also resembles the calculation process. Evaluating ANFIS’s performance concerning the original results underscores its remarkable capacity to analyze experimental data. With a maximum calculation error of only 13% when compared to the experimental tests, ANFIS demonstrates considerable accuracy and user-friendliness. Following the initial internal force evaluations, the proposed standard calculation method requires eight specific control inputs, and comparing these inputs with experimental tests further confirms the effectiveness and safety of this approach for connection design.

SISTEM INFORMASI E-KASIR PADA CAFE UNICO TEMBILAHAN

Currently, many cashier systems still use manual or traditional methods, which can waste customers' time, especially those in a hurry. Mistakes in calculating total expenditure can also be detrimental to restaurants and customers. Therefore, the latest innovations have made the calculation process at the cashier faster and more efficient. This information system was designed using the System Development Life Cycle (SDLC) method with the Waterfall model. The implementation uses the PHP programming language and XXAMP and MySql web servers for database management. The E-Cashier system offers many benefits for companies, such as making it easier to monitor current product data, speeding up the buying and selling process, processing financial reports at the cashier, and helping with archiving financial data. The conclusion of this research shows that E-Kasir is very useful for business people.

Research on the fusion imaging method of sign coherence and time reversal for Lamb wave sparse array

Time-reversal imaging struggles to detect plate-like structures due to interference from Lamb wave mode conversion and the processing demands, leading to less effective outcomes. This paper proposes a sign coherence factor and time reversal fusion (SCF-TR) imaging method based on amplitude and phase estimation. This method removes the coherence of array signals during signal reversal and refocusing. It reintroduces the sign coherence component to reduce interference from non-target scattered waves and partially overcome the constraints imposed by the Rayleigh criterion. The method allows imaging at a resolution smaller than the wavelength of Lamb and enhances the quality of the resulting images. In addition, a sparse array design utilizing the White Shark Optimisation Algorithm (WSO) is proposed to streamline the SCF-TR calculation process. This design utilizes sparse full matrix data to improve imaging efficiency. The experimental results show that for single blind hole defects, the SCF-TR method improves the array performance metrics and signal-to-noise ratio by 22.46% and 42.50%, respectively, compared to the TR method. For multiple asymmetric blind hole defects, when the defect size exceeds the resolution threshold, SCF-TR accurately reflects the position and morphology of defects smaller than the wavelength. When the defect size is below the resolution threshold, SCF-TR achieves super-resolution imaging. The sparse array designed using the White Shark Optimization algorithm demonstrates good sidelobe characteristics, effectively reducing sidelobe noise without reducing the array aperture. Moreover, the SCF-TR imaging time is reduced by approximately half while maintaining imaging accuracy.

A convolutional neural network to optimize multi-mission satellite altimeter fusion for improving the marine gravity field

Satellite altimetry is the main tool for constructing global or regional marine gravity fields. To improve the accuracy and spatial resolution, it is necessary to fuse multi-mission altimeters. How to determine the weights of multi-mission altimeters is a crucial issue, making the conventional calculation process very complex. In addition, traditional satellite inversion methods are often independent of shipborne gravity, which is used only as validation data, thus not take full advantages of high accuracy and resolution of shipborne gravity. In this study, we introduce a convolutional neural network (CNN) to merge the vertical deflections (DOVs) obtained from multi-altimeter missions to construct a marine gravity model in the South China Sea. High-accuracy shipborne gravity and a dataset comprising DOVs and geo-locations are employed as input data for neural network training. For the validation of CNN method, the gravity model is also computed by conventional Inverse Vening Meinesz (IVM) method. Independent shipborne gravity measurements and SIO V32.1, DTU17 models are used as validation data. The evaluation results show that the CNN-derived model achieves a higher level of accuracy, yielding a standard deviation (STD) of 3.21 mGal, with an improvement of 36.56% compared to IVM-derived model. More than 92% of the differences between the CNN-derived model and shipborne gravity are less than 5 mGal. In addition, spectral analysis results further show that the CNN-derived model has stronger energy at short wavelengths (less than 25 km) compared to other models. These findings reveal that CNN method is feasible for marine gravity recovery and the CNN-derived model can achieve higher accuracy. The CNN method can improve the accuracy and spectral characteristics of the constructed gravity model by taking advantage of the high accuracy and high resolution of shipborne gravity.Graphical

An Improved YOLOv8n Algorithm for Steel Surface Defect Detection

Due to the manufacturing process and other aspects, steel surface defects vary in shape and size, which is a difficult point for defect detection. For this reason, this paper proposes a steel surface defect detection algorithm based on YOLOv8n. The algorithm improves the C2f module of Backbone in this network by adding Context Guided Block (CG block), which can effectively extract the local features, surrounding context and global context, and integrate this information to improve the precision and accuracy of detection. Meanwhile, we also change the detection header of YOLOv8 to Detect_ASFF by introducing the Adaptive Spatial Feature Fusion (ASFF) technique. This improvement effectively filters out the conflicting information, which can allow the model to have a better adaptability at different scales. In this paper, Shape-IoU is adopted as the bounding box loss function to make up for the traditional IoU loss that ignores the intrinsic properties of the bounding box in the calculation process. After optimisation, mAP@0.5 are improved to 67.9% and mAP@0.5~0.95 are improved to 35.1%, which is 2.8% and 1.1% respectively. The improved algorithm in this paper has improved in detection accuracy, which proves the practicality and effectiveness of the algorithm in this paper.

Identification of optically active vibrational modes of columbite AB2O6 using the correlation method

In the modern era, the examination of molecular structure heavily relies on the application of infrared and Raman spectra within crystalline structures. These methodologies are essential in understanding the arrangement of atoms within molecules and the internal forces governing them. An essential aspect of this analysis involves identifying vibrational modes that can be detected optically. The correlation method is employed to establish rules for selecting these vibrational modes, both in crystals and molecules, through a systematic calculation process that aids in predicting their activity in Infrared (IR) and Raman spectra. The correlation method utilizes group theory to determine which vibrational modes are spectroscopically active in crystals. In our research, our aim is to employ this method to identify the irreducible representations and determine the IR and Raman active vibrational modes of orthorhombic AB2O6 compounds within the Pbcn space group. By conducting comprehensive group theory calculations, our objective is to elucidate the spectroscopic properties using the correlation method.

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.

МЕТОД ТА ДОСЛІДЖЕННЯ ТОЧНОСТІ ОБЧИСЛЮВАННЯ МАТЕМАТИЧНОЇ МОДЕЛІ ЕЛЕКТРОГІДРАВЛІЧНОГО ЕФЕКТУ

The mathematical model of the electrohydraulic effect (EHE) is a nonlinear non-stationary system of partial differential equations (PDE), which reflects the motion and contact interaction of two media, gaseous and liquid, under the action of pulsed thermal excitation. Such PDE systems generally do not have analytical solutions and are solved only by numerical methods, which are approximate in nature, i.e. they provide a solution with some error. The peculiarity of the motion of media in the case of EHE is large deformations in the form of flows and vortices. This feature imposes significant restrictions on the choice of the method for solving the PDE model. The widely known Lagrangian finite element method used to solve contact problems, in the case of EHE modeling leads to a pathological change in the shape of the finite elements, and therefore to instability of the calculation process and an unlimited increase in error. In calculation practice, a variant of the finite element method, FEM-ALE, has become widespread. It uses both Lagrangian and Eulerian grids of nodes and a special advection procedure, i.e., transfer by interpolation of the solution from the Lagrangian grid to the Eulerian one, and then vice versa from the Eulerian one to another new Lagrangian grid. Such an additional interpolation procedure, which is used repeatedly in the calculation, leads to additional (in comparison with the Lagrangian FEM) errors. If the causes and kinetics, as well as the methods of error control in the case of the Lagrangian FEM, have been sufficiently studied in the literature, then the kinetics of error development in the simulation of the EGE using the FEM-ALE method has been practically not studied. The article is devoted to the study of the development of errors in the numerical solution in this particular case. An analytical solution of the problem in the asymptotic case for a technological system with a rigid chamber is obtained. This solution is analyzed in comparison with the numerical solution using the FEM-ALE method. Conclusions are made regarding the errors in calculating pressure as the main factor of mechanical impact of EGE on the technological object and technological equipment, as well as errors in calculating deformations of gaseous and liquid media.The scientific novelty consists in the development of a method for determining the accuracy of EHE parameter calculations using the MSE-ALE method. The practical value lies in determining the accuracy of calculating the parameters of the EHE mathematical model, which makes it possible to justify the use of numerical modeling as a method of researching technological processes and systems using EHE

Evaluation of the Spatial Variability of the Mechanical Properties of Rocks Using Non‐Iterative Green's Function Approach and the FOSM Method

ABSTRACTThe present work proposes a new version of the Green‐FOSM (first‐order second moment) method, which eliminates the iterative calculation process of the original version and, simultaneously, solves the convergence problems related to the mechanical properties of rocks that form the geological formation. In this calculation scheme, the iterative process is eliminated by using a matrix that correlates the nodal displacement vector with the strain vector. Considering the same computational resources, this non‐iterative version of the Green‐FOSM method is up to 200 times faster than the original iterative process. In addition, it allows analyzing problems with more than 10,000 random variables, value that in the original method is less than 3000. To demonstrate its validity, the proposed method is applied to two hypothetical models subjected to different fluid extraction processes. For all the different levels of correlation and spatial variability, the statistical results obtained by the proposed method agree well with the results obtained via Monte Carlo Simulation (MCS). The relationship between CPU times demonstrates that the proposed method is at least 50 times faster than MCS. In the end, the non‐iterative Green‐FOSM method is used to obtain the displacement, strain, and stress fields of a geological section constructed from a seismic image of Brazilian pre‐salt oil region. The results found show that, depending on the levels of spatial variability, the analyzed fields can assume values up to 30.6% higher or lower than the values obtained deterministically.