Iterative Calculation Research Articles

Research on computationally generative design of woven braced structure based on fiber composite material in architectural design industry

ABSTRACT With the intervention of computer algorithm-assisted design in the realm of contemporary architectural design, architectural spatial forms and corresponding design methods have improved significantly. However, the limitations of material strength and plasticity impose certain constraints on the architectural design of the new century. Based on this occurrence, this paper studies the development of a computational generative design system for woven braced structure composed of fiber-reinforced materials. Through experimental data of material performance, the research firstly argues feasibility of employing fiber composite materials to braced structure. The conclusion is that the structure designed and generated could produce a support of 1 ton per ㎡. Moreover, based on the ant colony algorithm and the algorithm principle of cellular automata on the Java platform, the study designs a generative system with iterative computing of force as the major axis to achieve the self-organization and adaptability of the braced structure. Above all, this paper proposes a design strategy engaging with computational generative design approach, as well as a design attempt for the material and form of architecture.

A fast numerical method for calculating SPND space charge effect at steady state

To address the issue of low efficiency and long computation times in calculating the space charge effect in Self-Powered Neutron Detectors (SPNDs), we developed a fast and efficient numerical method to calculate the steady-state electric fields in SPND insulators. Previous calculations have shown that even when the electric field in the SPND insulator reaches 1 GV/m, its effect on electron transport is negligible. Therefore, we assume that the electric field has a negligible effect on electron transport. Based on this assumption, we developed a one-step numerical method for calculating the steady-state electric field in the insulator. This fast method is based on the charge conservation equation in equilibrium, thus requiring only a single charge deposition result without an electric field to obtain the distribution of the electric field under steady-state conditions. We validated our method by comparing it with precise iterative calculation results and experiments. The computational results indicate that, for three typical experimental scenarios, the fast method only takes up to one percent, or even less, of the time required by the iterative method. At the same time, the Mean Relative Error (MRE) in the electric field distribution between the fast method and iterative calculations are 1.48%, 0.31%, and 5.12%, respectively; whereas the relative errors in sensitivity calculations based on the electric field compared to iterative calculations are all less than 2%. Therefore, the fast method can significantly reduce computation time and ensure sufficient accuracy of steady state electric field distribution when the electric field is less than 1 GV/m. The introduction of this method has made it possible to conduct space charge effect assessments and precise sensitivity calculations for the placement of detectors throughout the entire reactor core.

Analysis of the Effect of Sampling Probe Geometry on Measurement Accuracy in Supersonic Gas Flow

The accuracy of sampling of gas components has a significant impact on the measurement of various performance parameters in the combustion chamber of an aero-engine. In order to investigate the effect of the probe geometry of a six-point gas sampling probe on sampling accuracy in supersonic gas flow, a three-dimensional probe gas flow characteristic solution model is established through numerical simulation methods of components of transport and fluid–solid coupling. Probes with three angles of 28°, 30°, and 32° and an optimized conical probe are constructed. The sampling accuracy of the probes with different geometries is compared and evaluated by the deviation of the component volume fraction before and after sampling and the resulting combustion efficiency error. This paper presents a set of calculation methods for solving the relative deviation of volume fraction by an iterative method based on the ideal gas law and the Redlich–Kwong equation (R-K equation). The method is designed to solve the exact component volume fraction problem in the simulation calculation. The study results demonstrate that the 28° and optimized conical probes improve sampling accuracy more effectively than the original 30° structure. The deviation of the volume fractions of the two structures is less than 1.7%, and the combustion efficiency error is less than 0.09%. The developed iterative calculation method can significantly reduce the theoretical calculation error to less than 0.06%. The experimental data of the test bench are in good agreement with the simulation results, thereby demonstrating the reliability and accuracy of the sampling probe following structural optimization.

Research on the mechanism of severe unsteadiness of PAT braking condition during the power failure

The Pump-As-Turbine (PAT) technology has become popular in micro hydropower stations due to its simple installation and cost-effectiveness. Nevertheless, power failures present a substantial risk to the secure and steady functioning of PAT’s braking system. The commercial CFD code (ANSYSCFX) is improved by incorporating a secondary development to model the power-off transition using Fortran accurately. This enhancement allows for real-time iterative calculations of angular momentum equations for mixed-flow PAT at different speeds. Meanwhile, the time–frequency domain analysis is utilized to analyze pressure pulsation signals and the evolution of the internal flow field in mixed-flow PAT. An investigation was conducted to have a deeper understanding of braking circumstances. The results revealed that the main frequency of the pressure pulsation aligns with the blade frequency at various flow rates, and there is a sudden change in pressure amplitude during the braking phase. The impeller experienced the majority of energy losses, with the draft tube being the subsequent area of concern. In addition, a thorough examination and comparison of the changes in the internal flow field during braking were carried out. This analysis revealed a distinct double helix structure within the draft tube, with a slower inner helix and a faster outer helix. Furthermore, it was observed that there is a strong correlation between wall shear stresses and hydraulic losses on the blade surface. This research enhanced understanding of the flow characteristics of mixed-flow PAT can help improve system safety and provide valuable guidance for future optimization efforts.

Application of efficient algorithm based on block Newton method to elastoplastic problems with nonlinear kinematic hardening

PurposeThe purpose of this study is to present the effectiveness and robustness of a numerical algorithm based on the block Newton method for the nonlinear kinematic hardening rules adopted in modeling ductile materials.Design/methodology/approachElastoplastic problems can be defined as a coupled problem of the equilibrium equation for the overall structure and the yield equations for the stress state at every material point. When applying the Newton method to the coupled residual equations, the displacement field and the internal variables, which represent the plastic deformation, are updated simultaneously.FindingsThe presented numerical scheme leads to an explicit form of the hardening behavior, which includes the evolution of the equivalent plastic strain and the back stress, with the internal variables. The features of the present approach allow the displacement field and the hardening behavior to be updated straightforwardly. Thus, the scheme does not have any local iterative calculations and enables us to simultaneously decrease the residuals in the coupled boundary value problems.Originality/valueA pseudo-stress for the local residual and an algebraically derived consistent tangent are applied to elastic-plastic boundary value problems with nonlinear kinematic hardening. The numerical procedure incorporating the block Newton method ensures a quadratic rate of asymptotic convergence of a computationally efficient solution scheme. The proposed algorithm provides an efficient and robust computation in the elastoplastic analysis of ductile materials. Numerical examples under elaborate loading conditions demonstrate the effectiveness and robustness of the numerical scheme implemented in the finite element analysis.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Optimal path calculation method of optical network under complex constraints

AbstractTo address the optimal solution problem of loop‐free paths in software‐defined optical networks with multiple complex logical relationships, a unified constraint expression is utilized to describe the constraints. The logical relationships of complex constraints are dissected and simplified, differentiating between “AND” and “OR” constraints. An optimal path calculation method is proposed, involving the transformation of the network topology based on various constraints. This transformation includes layering the topology, removing specific links, and adding necessary links to portray the different complex constraints onto the original network structure. Following the topology transformation, an enhanced K‐shortest path algorithm is employed to compute the route satisfying the combination of multiple complex constraints, resulting in the global optimal solution. Experimental results demonstrate that this method can determine the optimal path under intricate constraints in a single computational iteration without requiring prior knowledge of the optimal constraint sequence. Therefore, it offers significant practical value compared to existing algorithms.

Simultaneous measurement of solid concentration and solid moisture in gas–solid two-phase flow using a dual-modality electrical sensor

Abstract In a gas–solid two-phase flow system, simultaneous measurement of solid concentration and solid moisture is essential for some applications. However, during the measurement process, solid concentration and solid moisture are coupled in the measurement signals when the measurement signal is sensitive to both solid concentration and moisture. This makes it challenging to measure them simultaneously in the same measurement field. To solve this issue, a novel measurement method is proposed in this study, which uses a dual-modality electrical sensor to achieve simultaneous measurement of solid concentration and solid moisture in gas–solid two-phase flow. The dual-modality electrical sensor features a simple structure and captures measurement signals of capacitance and angle of dielectric loss within the same measurement section. With the above signals which are both sensitive to solid concentration and moisture, an iterative correction calculation method is adopted to achieve the simultaneous measurement of solid concentration and solid moisture. During the calculation process, the measured parameters are decoupled and continuously corrected until the related error values meet the threshold requirement. Numerical simulation experiments under three typical distributions and physical experiments under roping flow are carried out to verify the proposed measurement method. The results prove the effectiveness of the proposed measurement method in the simultaneous measurement of solid concentration and solid moisture for gas–solid two-phase flow.

Data-driven topology optimization using a multitask conditional variational autoencoder with persistent homology

Topology optimization is crucial for the mechanical design of vehicles and aircraft, allowing changes in the shape of structures and the placement of features. Recent advances have integrated deep generative models, particularly convolutional neural networks, to streamline this process.to streamline this process. However, these models struggle to preserve subtle structural features. To overcome these limitations, this study introduced a generative model adept at identifying the topological features inherent in real shapes, such as connectivity and holes, to enhance the effectiveness of topology optimization. A conditional variational autoencoder (CVAE) was employed to predict both the shape and compliance simultaneously. This model, CVAE with persistent homology, generates optimal material distributions by considering topological properties. The learning process introduced a term that minimizes the difference in topological features between true and reconstructed shapes. The proposed model can generate optimal material distributions by considering topological properties, eliminating the need for iterative calculations. This approach was validated using two numerical examples. The accuracy of the generated material distributions was compared with conventional methods using the mean-squared error. An average improvement in accuracy of approximately 36.85% was observed across the two results. This confirms that shapes considering compliance and connectivity can be accurately predicted.

Efficient Electromagnetic Compatibility Optimization Design Based on the Stochastic Collocation Method

Nowadays, in the field of electromagnetic compatibility (EMC), numerical methods such as finite element analysis are often used for simulation analysis. These numerical methods take a long time to solve some complex simulation problems, which is not conducive to the optimal design of EMC. In particular, the intelligent optimization algorithm that needs continuous iterative calculation will not be realized because of the long optimization time. This paper realizes the innovative application of the uncertainty analysis method (Stochastic Collocation Method) in EMC optimization design. Two typical EMC optimization design problems, namely, the prediction of cable crosstalk and the design of shielding performance of metal boxes, are proposed to verify the effectiveness of the optimization algorithm. Meanwhile, its performance is compared with the classical intelligent optimization algorithms such as genetic algorithms and immune algorithms.

Analysis of APB in vertical wells with evaporite layers: A 1D axisymmetric multilayer thermomechanical model

Temperature increases and creep in saline rocks can cause annular pressure buildup (APB), presenting a significant challenge in oil well operations, particularly in deep and ultra-deep pre-salt fields. This paper proposes a one-dimensional axisymmetric multilayer thermomechanical model that accounts for the influence of evaporite layers and thermomechanical effects on APB. We developed a finite element model that integrates heat transfer and thermomechanical interactions, utilizing a double deformation mechanism as a constitutive model to represent the viscous behavior of the rock formation. Additionally, this model enables the iterative calculation of fluid properties based on temperature and pressure conditions. To validate the model, we conducted case studies using widely adopted commercial finite element software. Its consistent results provide a deeper understanding of the pressure increases caused by the influence of saline rock and thermal effects, offering valuable insights into the safe and efficient operation of oil wells using a model with reduced computational complexity.

Optimisation of steel rolling schedule based on evolutionary multi-tasking transfer algorithm

Strip rolling is an important part of steel processing. The load distribution scheme in the rolling process directly affects production efficiency and product quality. A dynamic rolling process with changing speed is investigated to improve quality and reduce energy use. To balance the iterative calculation of the complex industrial evolution process and the requirements of high real-time, this study examine the rolling process by combining the multivariate, multi-constraint, and strong coupling characteristics of field measured data. On this basis, several conflicting rolling optimisation objectives in the process of rolling schedule optimisation were analysed, and a dimension reduction migration model based on transfer component analysis was established. In case of an inefficient transfer of feasible solutions, a multi-objective multifactorial evolutionary algorithm based on an explicit transfer solution strategy (MOMFEA-ETS) was proposed. The proposed algorithm obtained four average distance optimal values for eight practical rolling programming problems.

Feasible model-order reduction approach for analysis of composite rotor blades based on geometrically exact beam formulation

This paper presents a novel and practical reduced-order model (ROM) approach designed specifically for analyzing composite rotor blades, leveraging the geometrically exact beam formulation. The slender nature of rotor blades necessitates aero-structure coupled analysis to precisely predict their response, which involved numerous iterative computations. By capturing large displacements and rotations, this approach enhances the accuracy of blade behavior predictions under operational conditions. To address the computational demands of large-scale analyses, a ROM technique that effectively reduces system dimensionality while preserving critical structural features is introduced. The proposed method is then applied to the aero-structure interaction analysis of a hingeless hovering rotor trim, incorporating geometrical nonlinearity and centrifugal effects. The analysis results demonstrate that the ROM accurately captures the dynamics of these complex aeroelastic systems while significantly reducing computational costs. This approach exhibits significant potential for various aerospace applications, particularly in the design and analysis of structures undergoing geometrical nonlinear behavior and rotation-induced loads.

Development of optimization method for truss structure by quantum annealing

In this study, we developed a new method of topology optimization for truss structures by quantum annealing. To perform quantum annealing analysis with real variables, representation of real numbers as a sum of random number combinations is employed. The nodal displacement is expressed with binary variables. The Hamiltonian H is formulated on the basis of the elastic strain energy and position energy of a truss structure. It is confirmed that truss deformation analysis is possible by quantum annealing. For the analysis of the optimization method for the truss structure, the cross-sectional area of the truss is expressed with binary variables. The iterative calculation for the changes in displacement and cross-sectional area leads to the optimal structure under the prescribed boundary conditions.

Research on Calculation Method of On-Orbit Instrumental Line Shape Function for the Greenhouse Gases Monitoring Instrument on the GaoFen-5B Satellite

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important parameters characterizing the features of the instrument, and it plays a vital role in the system error analysis of the instrument’s measurements. To accurately obtain the instrumental line shape function of a spatial heterodyne spectrometer during the on-orbit period and improve the accuracy of gas concentration retrieval, this study develops a method to model and characterize the characteristics of the instrumental line shape function, including modulation loss and phase error. This study employs the solar calibration spectrum in the 1.568–1.583 μm bands to conduct iterative calculations of the instrumental line shape function error model. After the instrumental line function is updated, the average relative deviation is reduced from 1.83% to 0.756% between the theoretical and measured solar spectra. Additionally, the average relative deviation is reduced from 7.049% to 2.106% between the GMI nadir and theoretical nadir spectra. The findings demonstrate that updating the instrumental line shape function mitigates the impact of variations in the spectrometer’s instrumental line shape due to alterations in the orbital environment. This study offers a dependable reference for both the enhancement and oversight of a spectrometer’s instrumental line shape function, along with an investigation of shifts in instrument parameters.

The Renoir Dataflow Platform: Efficient Data Processing without Complexity

Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource usage, but require significant effort to develop and optimize new algorithms, to high-level platforms that hide most of the complexities of parallel and distributed processing, but often at the cost of reduced efficiency.To reconcile these requirements, we developed Renoir, a novel distributed data processing platform written in Rust. Renoir provides a high-level dataflow programming model as mainstream data processing systems. It supports static and streaming data, it enables data transformations, grouping, aggregation, iterative computations, and time-based analytics, and it provides all these features incurring in a low overhead.In this paper, we present the programming model and the implementation details of Renoir. We evaluate it under heterogeneous workloads. We compare it with state-of-the-art solutions for data analysis and high-performance computing, as well as alternative research products, which offer different programming abstractions and implementation strategies. Renoir programs are compact and easy to write: developers need not care about low-level concerns such as resource usage, data serialization, concurrency control, and communication. At the same time, Renoir consistently presents comparable or better performance than competing solutions, by a large margin in several scenarios.We conclude that Renoir offers a good tradeoff between simplicity and performance, allowing developers to easily express complex data analysis tasks and achieve high performance and scalability.

An implicit solution method for digital construction of temperature fields in concrete structures based on point temperature measurements

PurposeAt present, using the finite element method is difficult to efficiently and accurately construct the temperature field of mass concrete based on temperature measurement points. Thus, there is a need to propose a method for improvement.Design/methodology/approachThis study developed an implicit finite element method that digitally constructs the temperature field of mass concrete based on temperature measurement data. That is, in the proposed method of this paper, the temperature of the measuring point is also one of the boundary conditions, which real-time corrects the calculation error.FindingsIn this method, during the digital construction of the temperature field, the computed temperature approaches the actual measured value at the point of measurement with increasing iteration steps. Using this method and sufficient temperature measurement data, the errors in calculation conditions (such as the boundary conditions, the initial casting temperature and material parameters) can be automatically corrected during the iterative computation process.Originality/valueThis new method can improve calculation accuracy and allows the digitally constructed temperature field to converge to its true value with sufficient measurement data.

A Novel Method to Calculate Water Influx Parameters and Geologic Reserves for Fractured-Vuggy Reservoirs with Bottom/Edge Water

In practical oilfield production, the phenomenon of water influx typically shortens the water-free recovery period of wells, leading to water flooding and causing a sharp decline in the production well yields, bringing great harm to production. Water invasion usually occurs as a result of the elastic expansion of the water as well as the compaction of the aquifer pore space. However, it can be due to the special characteristics of fractured-vuggy reservoirs such as non-homogeneity and the discrete distribution of the pore spaces. It is challenging to use traditional seepage flow theories to analyze the characteristics of water influx. Also, reservoir numerical simulation methods require numerous parameters which are difficult to obtain, which significantly reduces the accuracy of the results. In this study, considering the driving energy for water influx, a water influx characteristic model was obtained by fitting a graph plate. Subsequently, an iterative calculation method was used to simultaneously obtain water influx volume and OOIP. The aquifer to hydrocarbon ratio was determined by fitting the water influx curve with the graphic plate. Results show that the calculation method is sensitive to the values of reservoir pressure and the crude oil formation volume factor. After applying the method to one field case, it was discovered that water influx performance can be characterized into two types, i.e., linear water influx and logarithmic water influx. In the early stages, the water influx rate of logarithmic water influx is greater compared to linear water influx. However, the volume and energy of waterbody are limited, and the water invasion phenomenon occurs almost exclusively within a short period after the invasion. On the other hand, the volume of waterbody invaded by linear water influx is larger, and it can maintain a stable rate of water influx. The results of the study can provide theoretical support for the waterbody energy evaluation and dynamic analysis of water influx, as well as the control and management of water in these types of reservoirs.

A game theoretic approach for spectral and energy efficient D2D communication in 5G-IoT networks

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks.

Optimization Of Sandal Production Using Linear Programming

In order to maximize income and minimize material costs, sandal manufacture involves other operational expenditures in addition to raw material costs that must be calculated. The goal of this study is to maximize earnings by optimizing sandal production costs, with a focus on the Diona Shoes home sector. By identifying the restrictions and inequalities present in the linear program, you can utilize linear programming to solve production cost optimization challenges. The simplex method is a technique for solving linear programming problems that involve numerous inequalities and variables by doing iterative calculations until the most optimal solution is found. The simplex approach (iteration) of production result optimization, data collection and observation, mathematical model creation, production result optimization employing Lindo software tools that are anticipated to yield results, and production result optimization are the stages taken to optimize overall production costs. optimally with the lowest possible manufacturing costs for sandals with heel, pansus, and back straps and can optimize the earnings from all sandal goods