Accuracy Of Calculations Research Articles

A refined nonlinear theoretical model for mechanical analysis of tunnels subjected to strike-slip faulting with multiple fault planes

During strike-slip fault dislocation, multiple fault planes are commonly observed. The resulting permanent ground deformation can lead to profound structural damage to tunnels. However, existing analytical models do not consider multiple fault planes. Instead, they concentrate the entire fault displacement onto a single fault plane for analysis, thereby giving rise to notable errors in the calculated results. To address this issue, a refined nonlinear theoretical model was established to analyze the mechanical responses of the tunnels subjected to multiple strike-slip fault dislocations. The analytical model considers the number of fault planes, nonlinear soil‒tunnel interactions, geometric nonlinearity, and fault zone width, leading to a significant improvement in its range of applicability and calculation accuracy. The results of the analytical model are in agreement, both qualitatively and quantitatively, with the model test and numerical results. Then, based on the proposed theoretical model, a sensitivity analysis of parameters was conducted, focusing on the variables such as the number of fault planes, fault plane distance (d), fault displacement ratio (η), burial depth (C), crossing angle (β), tunnel diameter (D), fault zone width (Wf), and strike-slip fault displacement (Δfs). The results show that the peak shear force (Vmax), bending moment (Mmax), and axial force (Nmax) decrease with increasing d. The Vmax of the tunnel is found at the fault plane with the largest fault displacement. C, D, and Δfs contribute to the increases in Vmax, Mmax, and Nmax. Additionally, increasing the number of fault planes reduces Vmax and Mmax, whereas the variation in Nmax remains minimal.

Conjugate heat transfer simulations of a radially cooled gas turbine blade leading edge using a vortex-based fluidic oscillator for sweeping jet impingement

The turbine blades of aero engines are subjected to extremely high temperatures, particularly at the leading edge, where temperatures can reach approximately 1800–2000 K. Therefore, effective heat load management is crucial. A vortex-based fluidic oscillator for sweeping jet impingement was proposed as an innovative cooling method to enhance heat transfer at leading edge of high-pressure gas turbine blades. This numerical investigation evaluates the cooling performance of a vortex-based sweeping jet compared to steady and conventional sweeping jets in a radially cooled high-pressure turbine blade. In this study, a conjugate heat transfer model based on three-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) equations is employed. The shear stress transport (SST k–ω) model is specifically selected to predict the flow field and heat transfer characteristics of a vortex-based fluidic oscillator applied to the leading edge. To verify the accuracy of numerical calculations, two sets of experimental data were used as benchmark. The results demonstrated strong qualitative and quantitative agreement with experimental data. Various parameters, including coolant mass flow rates (0.171, 0.514, and 0.857 g/s), aspect ratios (0.5, 0.65, and 1), jet-to-wall spacings (H/D = 2, 4, and 6), and pressure drop, were examined to assess overall cooling effectiveness and heat transfer performance. Time-averaged and time-resolved flow field measurements revealed that vortex-based fluidic oscillator significantly enhanced cooling effects and covered a larger impinging area compared to a steady jet. Notably, the vortex-based fluidic oscillator achieved a 24.3% higher heat transfer performance than the steady jet at H/D = 2, with an average temperature decrease in approximately 21 K at leading edge.

Investigation and Optimization of Wave Suppression Baffles in Automobile Integrated Water Tanks

The risk of liquid agitation in pump-driven tanks within integrated tanks has significantly escalated due to the growing demands for tank integration in new-energy vehicles. In order to solve the problem of liquid sloshing in integrated tanks, this paper presents the design of a baffle structure aimed at reducing waves in integrated water tanks. The numerical simulation method of combining the level-set function with the volume of fluid (CLSVOF) has been employed, significantly enhancing the accuracy of numerical calculations related to a two-phase flow field inside an integrated tank. A comparison was made by analyzing different factors, notably baffle length (L), baffle depth (H), and baffle angle (θ), to investigate their influences in suppressing liquid agitation within the integrated water tank. Numerical computations were conducted utilizing design points acquired by the Latin hypercube sampling technique. The Kriging approximation modelling method was employed to hold down computing time. The Pareto solution was obtained by means of the non-dominated sorting genetic algorithm II, while the optimal solution set was evaluated and ranked using the multi-criteria decision-making algorithm (MCDM). The results show that increasing the baffle depth within a certain range can effectively suppress the wave height in the tank. When the baffle depth is increased to a certain value, the effect on wave-height suppression in the water tank is limited. When the baffle length and angle of the baffle exceed a certain value, it will also have the effect of suppressing the wave height in the tank. After comparing various factors of the baffle, it was ultimately found that the wave suppression effect is maximal when the length of the baffle is 13 millimeters, the depth of the baffle is 49 millimeters, and the angle of the baffle is -20 degrees. The main contribution of this study is the proposed wave-suppressing baffle structure, which provides new insights for the future structural design of integrated water tanks.

Modeling and analysis of fluid-solid coupling heat transfer and fluid flow in transonic nozzle

Solid rocket motors (SRMs) are the power units of modern aerospace transportation, and it is of great significance to predict their flow and heat transfer characteristics by numerical simulation. This numerical study is based on a self-developed code framework MHT (Multi-X Heat Transfer, where X represents the region, scale, physical field, and other aspects) and presents an effective method of analyzing fluid–solid coupling heat transfer problem in an axisymmetric supersonic nozzle. For the nozzle wall heat conduction and nozzle-in fluid flow there are two kinds of solution methods, the detached (solved separately in solid and fluid region and them coupling at the interface) and the whole field method (solve the conduction and convection simultaneously). The whole field method is more efficient but few scholars use it to solve the nozzle coupled problem. This paper introduces the numerical details of the whole field solution method, and provides a new idea for the coupling solution of the nozzle. In addition, a mixed grid system is proposed around the interface region of fluid and solid, in which the quadrilateral structured mesh is used at the fluid side, and the triangular unstructured mesh is used for the outer fluid region and the whole solid region to ensure the accuracy of the 1st-oder normal derivatives calculation. The self-developed code is validated by comparison with well-known commercial soft-ware. Transient results of temperature and velocity are presented for the instants of 1st 20 and 40 s of a practical nozzle.

Predictive ability of the Cancer and Aging Research Group chemotherapy toxicity calculator in hematologic malignancy

IntroductionChemotherapy toxicity tools are rarely studied in patients with hematologic malignancy (HM). The primary aim of this pilot study was to determine the predictive ability of the Cancer and Aging Research Group (CARG) chemo-toxicity calculator in estimating grade 3–5 toxicity in patients with HM. Materials and MethodsPatients 60 years and older with HM were prospectively evaluated using the CARG chemo-toxicity calculator. Discrimination and calibration were checked by applying the published model in our data. Additionally, a full geriatric assessment (GA), the Short Physical Performance Battery (SPPB), and health related quality of life (HRQoL) were captured longitudinally at the start of treatment and at end of study. Secondary aims explored the association of GA metrics with chemo-related toxicities and survival. ResultsOne hundred forty-five patients were approached, 118 patients consented, and 97 patients were evaluable. Most patients were newly diagnosed (n = 91). The median CARG score was 9 (range 4–18). The CARG score was not validated in our cohort of older patients with HM, with area under the receiver operation characteristic curve being 0.53 (95 % CI: 0.41–0.65). In multivariable analysis, after controlling for disease type, risk factors associated with grade 3–5 toxicity included living alone (hazard ratio [HR] 4.24, 95 %CI: 2.07–8.68, p < 0.001), increase in body mass index (HR 1.06, 95 %CI: 1.01–1.12, p = 0.03) and a higher social activities score (HR 1.27, 95 %CI: 1.06–1.51, p = 0.01). In multivariable analysis of overall survival, the only prognostic factor was an objective marker of physical function (SPPB score HR = 0.85, 95 %CI:0.78–0.93, p < 0.001). DiscussionThe CARG chemo-toxicity calculator was not predictive of grade 3–5 toxicity in patients with hematologic malignancy. The SPPB was associated with overall survival in multivariable analysis, suggesting future use as an objective biomarker in HM. We also report a comprehensive trajectory of function, QoL, psychosocial well-being, and cognition among older adults with HM. The predictive accuracy of the CARG chemo-toxicity calculator may be affected by the diverse range of HM treatment options that are not traditional chemotherapy.

Prediction of Melting and Solid Phase Transitions Temperatures and Enthalpies for Triacylglycerols using Artificial Neural Networks

Triacylglycerols (TAG) are the main components of vegetable oils and any attempt to simulate vegetable oils processes will demand knowledge of their properties. However, experimental values are scarce, considering that several TAG in their pure forms are unavailable or too expensive for experimental measurements. On the other hand, correlating physical properties with TAG molecular structure is not simple. TAG is a molecule composed of 3 fatty acids (FA) esterified to a glycerol (GL) backbone, making properties dependent on carbon number (CN) of each FA, number of unsaturations (UN) of each FA, and position of the FA in the GL backbone. Few models are available in literature for prediction of TAG melting properties, with a special attention to melting temperature (Tfus) and enthalpy (ΔHfus) and solid-solid transition properties of the TAG polymorphic forms. Wesdorp's, Moorthy's et al. and Zeberg-Mikkelsen and Stenby's works present models based on the Group-Contribution theory nowadays used, despite some flaws, particularly considering the polymorphic transitions. Therefore, this work was aimed at evaluating Artificial Neural Network (ANN) models for prediction of TAG's Tfus and ΔHfus (β-form) as well as temperature and enthalpy transitions of molecule polymorphic forms (α and β’). Database was composed of temperature and enthalpy experimental data from literature. For each TAG, 7 input data were provided: total CN, as well as CN and UN at sn-1, 2 and 3 TAG position. The Multilayer Perceptron Feed Forward (MPL) model was used, and the topology was evaluated for number of hidden layers (HL), number of neurons (NN) and activation function at each hidden layer, and convergence algorithm. Number of HL and NN was screened by using a Central Composite Rotatable Design (CCRD). Models were further evaluated by Explainable Artificial Intelligence (XAI) and feature evaluation strategies. Architectures showed a significant higher accuracy for calculation and prediction of TAG's melting properties of the 3 polymorphic forms, with R2 higher to 0.91 for all databases when compared to literatures’ models (excepted for the prediction of the melting temperature of the β form, where Wesdorp's model presented a better predictive ability, despite great similarity). Good results were probably related to the well-defined physicochemical relationship between input (molecular structure descriptors) and output (melting properties), that could be described by XAI evaluation. This is an important advantage considering the improvement of the performance of process and products design including TAG molecules.

Evaluation of Dose Calculation Algorithms Accuracy for ISOgray Treatment Planning System in Motorized Wedged Treatment Fields

Abstract Background: Different dose calculation methods vary in accuracy and speed. While most methods sacrifice precision for efficiency Monte Carlo (MC) simulation offers high accuracy but slower calculation. ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. This study’s primary goal is to evaluate the dose calculation accuracy for ISOgray TPS algorithms in the presence of a wedge. Methods: This study evaluates the dose calculation algorithms using the ISOgray TPS in the context of radiation therapy. The authors compare ISOgray TPS algorithms on an Elekta Compact LINAC through MC simulations. The study compares MC simulations for open and wedge fields with ISOgray algorithms by using gamma index analysis for validation. Results: The percentage depth dose results for all open and wedge fields showed a more than 98% pass rate for points. However, there were differences in the dose profile gamma index results. Open fields passed the gamma index analysis in the in-plane direction, but not all points passed in the cross-plane direction. Wedge fields passed in the cross-plane direction, but not all in the in-plane direction, except for the Clarkson algorithms. Conclusion: In all investigated algorithms, error increases in the penumbra areas, outside the field, and at cross-plane of open fields and in-plane direction of wedged fields. By increasing the wedge angle, the discrepancy between the TPS algorithms and MC simulations becomes more pronounced. This discrepancy is attributed to the increased presence of scattered photons and the variation in the delivered dose within the wedge field, consequently impacts the beam quality. While the CCC and FFT algorithms had better accuracy, the Clarkson algorithm, particularly at larger effective wedge angles, exhibited greater effectiveness than the two mentioned algorithms.

Investigation of the effect of three parameters in the GEKO model on the calculation results for backward facing step flow

Abstract. Computational fluid dynamics (CFD) is a field which is attracting more and more focus today for its ability to simulate comprehensive conditions. Reynolds-Averaged Navier-Stokes (RANS), which needs low cost, is one of the most popular methods used in engineering. However, RANS method has insufficient ability to simulate separated flow, which is a major challenge at present. One reason for this is that the model parameters introduce uncertainty into the simulation. This research investigates the effect of three parameters in the Generalized k- (GEKO) two-equation turbulence model on the calculations in backward facing step flow. It is found that C_sep has the greatest influence on the calculation of pressure coefficient and friction coefficient. C_nw has the greatest influence on the reattachment calculation through eddy-viscosity. The calculation results of the three variables show that there is correlation between them. Besides, a better set of parameters is obtained, which will improve the calculation accuracy for these three variables.

On the impact of improved dose calculation accuracy in clinical treatment planning for superficial high-dose-rate brachytherapy of extensive scalp lesions

On the impact of improved dose calculation accuracy in clinical treatment planning for superficial high-dose-rate brachytherapy of extensive scalp lesions

Prediction of aircraft fuel consumption based on machine learning algorithm

Abstract The shortcomings of traditional aircraft fuel consumption calculation methods include insufficient theoretical analysis, complex input parameters, and poor calculation accuracy. Through the analysis of aircraft motion equations and engine working characteristics, this paper screens out the key parameters affecting aircraft fuel consumption. Based on the real flight data, three machine learning methods, BP neural network, RBF neural network, and Support Vector Machine(SVM), are used for training, and a fuel consumption prediction model is established with key parameters as inputs. The prediction accuracy and characteristics of the three models are analyzed and compared. The aircraft fuel consumption prediction model achieves the operational requirements, and the accuracy in the flight profile reaches 5%. The trained model was applied to the planning of the transport aircraft mission profile, achieving a fuel consumption error of only 1.1% across the entire mission profile, resulting in a relatively accurate outcome.

Study of thermal and humidity environment and prediction model in impinging jet ventilation rooms based on thermal and moisture coupling

Variations of indoor humidity have significant influence on thermal comfort, calculation accuracy of air-conditioning load and selection of design parameters. As one of the most promising advanced ventilation strategies, impinging jet ventilation (IJV) has received much attention. However, few study concerned its coupled indoor thermal and humidity environment and there is also no complete mathematical model for the IJV to synchronously predict its indoor temperature and humidity distributions. Therefore, this study first simulated the thermal and humidity environment in IJV rooms with considering the coupled thermal and moisture transfer effect. The results demonstrate a significant correlation between temperature and humidity distributions in IJV, with a correlation coefficient of approximately 0.4. Then, a simplified theoretical model for predicting temperature and humidity distributions in the IJV was established based on the theory of nodal model, and the key parameters within the model that are influenced by indoor airflow pattern were also identified. Next, the model results were compared with the simulation ones and showed that the proposed model is with a mean absolute error of 0.4°C for the prediction of temperature distribution and of 0.21g/kg for the prediction of humidity distribution. These discrepancies are sufficiently minor to validate the accuracy of the proposed model. Last, the potential application value of the proposed model was discussed. Overall, the current study contributes to extend the application of the existing coupled thermal and humidity models to IJV scenarios, and provides a robust theoretical foundation for the more rational design and practical application of the IJV.

Detection of clavicle fracture after martial arts training based on 3D segmentation and multi-perspective fusion

To improve the diagnosis efficiency of clavicle fractures, a Diagnosis Model of Clavicle Fracture Based on 3D Segmentation and Multi Perspective Fusion (3Ds MPF) is proposed in the experiment. By performing 3D segmentation on clavicle images, key layer images are extracted, and multi-perspective image data are fused and output to achieve accurate classification and diagnosis results. The results show that on the relevant dataset, the loss function value of the constructed model is less than 0.01, and the calculation accuracy is as high as 0.999. The average classification accuracy in actual use exceeds 90%, and it can capture the majority of fracture features in clavicle computed tomography images. The above results indicate that the model constructed by the research institute can significantly improve the judgment efficiency of computed tomography images, which helps to enhance the detection and diagnosis of clinical fracture conditions. The experimental results have helped improve the clinical diagnostic efficiency of fracture detection work.

Research on 3D Model Calculation of Green and low-carbon New Buildings

Due to the widespread use of solid clay bricks in building houses in China, the annual destruction of farmland by burning bricks amounts to about 100000 acres. Faced with these problems, China has an extremely urgent demand for green and low-carbon buildings. Therefore, this article conducts computational research on the 3D model of a new type of green and low-carbon building. Due to the significant differences in the geometric form and texture representation of a large number of green and low-carbonl new buildings, it is difficult to uniformly quantify their level of detail. This article considers the usefulness and accessibility of 3D model calculation data separately. The shadow length is generally the distance from the selected building's facade roof to the end of the shadow, which can avoid the influence of building shape on its length. The determination of these two points and the calculation of the distance between them will directly affect the accuracy of shadow length calculation. Therefore, when users need to add an application subsystem to the system, they must add information such as the input and output data structures of the subsystem to the module. The height information calculation method uses the building and shadow imaging geometric modeling to calculate the building height by establishing the relationship between the shadow length and the corresponding building height.

Automatic Algorithm Applied for Calculating Thermal Conductivity by Transient Plane Source Method

As a thermal conductivity measurement method, Transient Plane Source (TPS) method has gained much popularity because of its broad applicability, short measurement times, high precision and simple sample preparation. However, the accuracy of thermal conductivity calculations based on temperature rise data is often hindered by factors such as probe thickness, contact thermal resistance, and input power. Currently, there is no standardized criteria for selecting effective temperature rise data for thermal conductivity calculation. Consequently, the accuracy of results are limited by the operator's understanding of the TPS methods, and repeatability of the results is often poor. To address this issue, an automatic algorithm based on the international standard (ISO22007-2:2008) is proposed in this paper. By applying this algorithm to the measurement of different materials, it has been demonstrated that the proposed algorithm can produce more precise and consistent results than the conventional method. Additionally, the integration of the time window function , typically utilized solely for result validation in conventional methods, further enhances the objectivity and reproducibility of the results obtained by the automatic algorithm.

GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

Borel Summation Can Be Controlled by Critical Indices

We consider application of the self-similarity principle in approximation theory under the conditions of asymptotic scale-invariance. For the effective summation of the asymptotic series methods, an iterative Borel summation with self-similar iterated roots is applied. The approximants follow from the self-similarity considerations and behave asymptotically as a power-law satisfying the asymptotic scale invariance. Optimal conditions on convergence of the sequence of approximants are imposed through the critical indices defined from the approximants. The indices are understood as control parameters for the optimal convergence of the asymptotic series. Such interpretation of the indices leads to an overall improvement of accuracy in calculations of the indices. The statement is supported by fifteen examples from condensed matter physics, quantum mechanics and field theory.

Application of Numerical Integration in Analysing the Volume of Reinforcement Particles in Algorithms for Generating Representative Volume Elements (RVEs)

Abstract The paper focuses on spatial modelling of composites with discontinuous reinforcement. The algorithm for creating a representative volume element (RVE) must consider random distribution and size of reinforcing particles (RP), prevention of RP interpenetration, and maintaining the desired volume fraction of the reinforcing phase (Vp) in the composite microstructure. Assuming fixed RVE dimensions and randomly determined RP size, the actual Vp value needs to be continuously determined. If the assumed (desired) Vp is lower than the current value, additional reinforcement is added to the RVE. As the RP location is random, some particles may extend beyond the RVE limits, affecting Vp calculation. The research aims to determine the RP volume within the RVE boundaries when RP extends outside. The RVE was discretized with N points, and the number of Ni points within the area occupied by RP was determined. The sought value was calculated using the ratio Ni /N = Vp /VRVE, where VRVE, is the volume of the RVE. Two discretisation methods, systematised (RI) and random (Monte Carlo (MC)), were employed. The study investigated the effects of discretisation type and number N points on calculation accuracy and microstructure generation time for particle-reinforced composites in sphere, cylinder, and ellipsoid shapes. Systematised discretisation yielded higher accuracy/stability, with number N dependent on RP dimensions. The MC method reduced generation time but introduced instability and significant errors.

Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model

This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.

Analysis of the Accuracy of Calculation of Production Costs and Cost of Products for Micro Businesses in the Food &amp; Beverage Sector

This research provides an overview of the cost of a product for a business which is one of the main things and affects the business plan in the long term. Many of them determine the cost of a product only based on a rough estimate without clarity on the basic elements in the calculation of production costs such as direct labor costs and overhead costs. The research method is descriptive using literature studies and field surveys. The results of this study seek to help micro businesses engaged in the food &amp; beverage sector calculate production costs and cost of products through a simple but more accurate calculation formula than what they have calculated so far. The managerial implication that can be done is that micro industries can apply cost of goods calculations more accurately.

Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals

The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%.