Double Iteration Research Articles

On quasi-inertial double extragradient-type iterations for variational inequalities and Bregman relatively asymptotically nonexpansive mapping

On quasi-inertial double extragradient-type iterations for variational inequalities and Bregman relatively asymptotically nonexpansive mapping

Numerical Analysis of 3D Unilateral Quasi-static Contact: Effect of Coating Thickness and Mechanical Properties

In this study, a numerical model for unilateral quasi-static contact between a rigid sphere and an elastic coating has been developed. The contact problem was solved using a numerical procedure based on the FFT technique, considering various coatings with different thicknesses and mechanical properties, implemented through a Matlab code. The model calculates the contact surface deformation and pressure field through a double iteration process. The first iteration solves the contact problem for a given indenter penetration, while the second iteration refines this penetration by minimizing the difference between the fixed and calculated loads. To achieve this, influence coefficients are derived from the elasto-static equations using the Papkovich-Neuber potentials. The study discusses the influence of coating thickness and friction coefficient value on the tribological behavior of the coating. The results indicate that contact pressure increases (ranging from 1.9 to 2.5) as the coating becomes thicker or more rigid (0.02 mm to 0.2 mm). Additionally, the tribological behavior of the coated surface is affected by the coating's thickness, hardness, and friction coefficient value. Importantly, this model demonstrates versatility by being applicable to both smooth and rough surfaces.

Lopsided PMQHSS and double lopsided PMQHSS iteration methods for solving complex symmetric linear equations

Lopsided PMQHSS and double lopsided PMQHSS iteration methods for solving complex symmetric linear equations

Recursive demodulated synchro spline-kernelled chirplet extracting transform: a useful tool for non-linear behavior estimation of non-stationary signal and application to wind turbine fault detection

Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.

Study of overflow effects on flow‐induced stress in injection molding

AbstractOverflow is usually used to keep fluid continuously flowing to avoid the fluid from changing flow direction and subsequently inducing large stresses. To explore the mechanism of flow‐induced stress evolution, a flow model was established in terms of compressible, non‐isothermal, and viscoelastic flow. Based on the Leonov model and the mid‐plane approach, a pseudo‐Poisson type equation for pressure was derived to decouple the interdependence between velocity and pressure. To improve computational efficiency, a numerical approach involving double iterations was proposed to solve the flow problem. Two kinds of mold cavities, with and without overflow, were designed and manufactured. Molding experiments were conducted, and corresponding simulations based on the proposed model and algorithm were performed. The results show that the simulated contours of flow‐induced stress are in good agreement with the photoelastic streaks. The use of overflow can significantly reduce flow‐induced stresses. If the slot thickness is set to a proper value, the flow‐induced stress can be reduced by more than 80%. On the other hand, a slot that is too thick does not have significant reducing effects. Therefore, using overflow to reduce flow‐induced stresses during transparent part production is a promising approach if the material cost is worthy of the quality.Highlights A double iteration method is proposed for viscoelastic flow problem. Overflow can reduce the stress as much as 80% if the dimension is properly set. The simulated stress streaks are in good agreement with photoelasticities.

A new free-parameter double splitting iteration method for PDE-constrained optimization problems

We present a new free-parameter double splitting iteration method for solving the system of linear equations arisen from finite element discretization of a PDE-constrained optimization problem. We prove that the eigenvalues of the iteration matrix of the method are included in the interval (0,12]. So the spectral radius of the iteration matrix is less than or equal to 12, which shows that the method is unconditionally convergent. Numerical comparison with two known methods is given to show the effectiveness of the method.

Robotic organism targets regional coverage capture path planning for marine aquafarm based on value iteration network

The application of robots in the field of Marine aquatic organisms capture can greatly improve the fishing efficiency and economy of seafood. The purpose for path planning of underwater vehicle manipulator system (UVMS) is to capture the most organisms in the shortest time with no obstacle collisions. This paper has proposed a novel cost modelling algorithm for the collision avoidance, manipulation and vehicle movement during the target capture process. On the underwater unknown and blurred environment, a novel double value iteration network with long short-term memory (LSTM) network has been proposed on the basis of value iteration networks. The proposed improved double value iteration network can predict the blurred and unknown environment, plan capture strategy with the calculated collision risk and movement cost. In the value iteration module, the use of LSTM network can improve the stability of training and the performance of the model. Moreover, a novel cooperative path planning model for task allocation has been proposed to improve capture efficiency. Simulations on robot and multiple robots capture planning have verified the network performance.

A Shifted Double Wavelet Iteration Algorithm for Detecting the Gross Errors in Shipborne Gravity

Detecting the discrete gross error and regional gross error in shipborne gravity is a key step for improving the measurement quality. The traditional wavelet threshold shrinkage (WTS) method misidentifies discrete gross errors and cannot detect regional gross errors. Here we propose a shifted double wavelet iteration (SDWI) method. This method decomposes the measurement sequence by shifted double wavelet to obtain wavelet coefficients, and then apply threshold processing to the obtained coefficients to accurately detect gross errors. After that, we reconstruct the identified gross errors, and perform iterative processing to the regional gross errors. Finally, the difference of the gravity before and after gross error detection is used for gross error positioning. In the simulation experiment, we use the proposed SDWI method to detect the discrete gross error, the regional gross error and discrete-region gross error in the shipborne gravity, separately. The results show that the detection success rate is 100% and failure rate is 0 for all three kinds of gross errors, better than that of the traditional WTS method. The real data experiment also confirms that the proposed method is effective in detecting gross errors.

Optimal design and operation method of integrated energy system based on stochastic scenario iteration and energy storage attribute evaluation

Integrated energy system servicing buildings brings economic, energy, and environmental benefits, which relies on advanced operation and design methods. However, incomplete and inaccurate initial information leads to significant design errors. In this study, an attribute evaluation method of the stored energy is given, and an optimal design and operation method based on double loop optimization and stochastic scenario iteration is proposed. By completing initial parameters and constraints, the accuracy and reliability of the design and operation of integrated energy system are improved. Component capacity and energy attribute are optimized in the outer loop, in which various annual operation scenario is continuously updated to enhance the adaptability to the uncertain customer demand and stochastic renewable energy. Real time energy dispatch is optimized in the inner loop, in which the benefits from the charging and discharging of storage component are calculated in time through these attributes to improve the overall performance of the system. A business vacation resort with variable demands and resources is created as potential project of integrated energy system. Four cases are simulated to compare different system design methods. The proposed methods decreased the difference between the actual and predicted performance from 11.72% to 0.90%; furthermore, the consumption, cost, and emissions were reduced by 1.41%, 3.86%, and 1.51%, respectively, with an average reduction of 2.26%.

Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service

Long-span cable-stayed bridge is a typical wind-sensitive structure because of its light weight and great flexibility. The reliability evaluation of the cable-stayed bridge under wind action becomes a key issue during bridge design and operation. In this paper, taking Xiangshan Harbor highway cable-stayed bridge in Ningbo City in China as the engineering background, ANSYS 15.0 is used to build a finite element model of this bridge to explore its aerostatic stability and influencing parameters, then the static wind reliability of this bridge is analyzed. Firstly, considering the structural nonlinearity and the nonlinearity of static wind load comprehensively, the critical wind speed of aerostatic instability of the Xiangshan Harbor cable-stayed bridge is calculated by the internal and external double iteration method. Then the influence of the initial wind attack angle, steel wire breakage rate of the stay cables, static three-component force coefficients, and cable broken position on the static wind stability of this bridge are discussed. Thus, the variation rule of the critical aerostatic instability wind speed is obtained, and the parameter that has the greatest impact on the static wind stability of the cable-stayed bridge in service is determined. Finally, considering the randomness of these key parameters, they are treated as random variables, Latin hypercube sampling (LHS) method is used to obtain samples of random variables to calculate aerostatic instability wind speed for a given sample by self-written calculation program, and on this basis, static wind reliability analysis is conducted by the checking point method, which provides strong support for the static wind risk evaluation of the existing long-span cable-stayed bridge.

Integrated Energy Management Strategy Based on Finite Time Double Consistency Under Non-Ideal Communication Conditions

Collaborative energy management is a critical issue in integrated energy system (IES). Aiming at the problem, a novel finite time double consistency algorithm is proposed to solve the energy management problem of IES. By implementing this algorithm, the energy equipment units only need to perform local communication and local calculation, effectively solving the nonlinear coupling optimization problem in the system. Moreover, the algorithm enables the completion of optimal economic dispatch quickly in a finite time. The algorithm utilizes double consistency iteration of the incremental cost price and the estimated value of equipment output to effectively coordinate the relationship among energy equipment outputs. On this basis, an effective combined event trigger mechanism is designed by introducing measurement error, which enables energy equipment to communicate only when necessary, thereby avoiding the need for continuous information exchange. The proposed algorithm considers non-ideal com-munication environments such as communication delay and disturbance. To address these issues, the consistency theory and the model reduction theory are combined to provide a sufficient condition for the system's stability. Finally, the convergence and stability of the algorithm are verified using the Lyapunov method, and simulation experiments confirm the effectiveness of the designed algorithm.

Simulation and optimization of the waste heat recovery system of the ship power system based on the heat current method

AbstractWaste heat recovery technique is of great importance to improve the coefficient and reduce the emission of greenhouse gas for ship power system. This paper builds the model of a waste heat recovery system of single pressure Rankine cycle with heat current method. Combining with the thermo‐electrical analogy method, the global heat current model of waste heat recovery system is obtained, which indicates the direction of heat flow, the distribution of thermal resistance and the relationship of temperatures. Applying the law of Kirchhoff builds a mathematical model of a system with no intermediate nodes temperatures. Considering the variation of physical property and distribution of thermal resistance in phase change heat exchanger, a double nested iteration solving algorithm is proposed. The results show that the heat transfer rates of economizer, superheater evaporator, condenser, and steam receivers are 9.1, 70.42, 143.89, 19.03, 171.98, and 17.28 kW, respectively. With the objective of maximum output of turbine, applying the genetic algorithm obtains the optimal mass flow rate of cycle water, which is 0.53 kg/s and improves the output of turbine from 53.25 to 147.45 kW. Discussions on the optimization model show that decreasing extraction ratio and increasing the mass flow rates of air discharge of compressor will increase the output work of turbine. Both the optimal mass flow rate of cycle water and maximum output work decrease rapidly at first and then change little while the circulation ratio increases. Particularly, the relative variations of mass flow rate and output work are less than 2% while the circulation ratio is larger than 4.

Adaptive task offloading of rechargeable UAV edge computing network based on double decision value iteration

With the development of Internet of Things technology, unmanned aerial vehicles (UAVs) have attracted tremendous attentions due to its broad applications in the conditions of high rate and low delay communication. In this paper, we consider a wireless-powered and UAV-enabled MEC system. Because of the limitation of UAV’s battery capacity, we deploy microwave power stations next to the base stations to provide microwave energy to the UAV from ground microwave antenna array. The UAV receives tasks from mobile users and processes the task locally or offloads task to edge servers for processing, or charges its battery through microwave stations when its battery is low. Due to the uncertainty of the UAV’s trajectory and the working status of the microwave power stations, we build the offloading decision process as a finite Markov Decision Process(MDP) model. In the MDP model, we solve the optimization problem of minimizing the total consumption of time and energy through an optimal offloading algorithm. The algorithm we proposed is based on the fast-converging value iteration algorithm. Furthermore, extensive numerical results shows that the proposed algorithm outperforms other four conventional baseline schemes.

A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies

In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development.

Value Iteration Networks with Double Estimator for Planetary Rover Path Planning.

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.

UAVs Assisted Intelligent Reflecting Surfaces SWIPT System With Statistical CSI

This paper investigates an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer (SWIPT) system. Multiple IRSs deployed on unmanned aerial vehicles (UAVs) and ground building are considered in the proposed system for enhancing transmission of information and energy simultaneously. The optimization problem is formulated to maximize the average achievable rate over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> time slots by jointly optimizing power splitting (PS) ratio, transmit beamforming, phase shifts and trajectories of UAVs. To tackle the non-convex optimization problem which can be decomposed into different subproblems, block successive upper bound maximization/minimization (BSUM) method is proposed for optimizing subproblems alternately. Considering the difficulty brought by LoS components in optimizing UAVs’ trajectories, double iteration algorithm is proposed which BSUM with fixed LoS components is applied in the inner layer and LoS components are updated in the outer layer. Meanwhile, feasible initial trajectories of UAVs are also analyzed for improving the efficiency in optimizing UAVs’ trajectories by using successive convex approximation (SCA). In order to provide theoretical foundation for further study, perfect channel state information (CSI) is considered firstly in solving the non-convex optimization problem. Furthermore, considering the limited ability in acquiring CSI at both IRSs and UAVs, optimization problem under statistical CSI is reformulated. Accordingly, with the aid of Jensen's inequality, double iteration algorithm can be also applied to tackle the optimization problem. Simulation results are provided to evaluate performances of proposed designs under different setups.

Direct position determination with phase error compensation for multiple targets using spatial sparsity

Measuring the positions of multiple targets is key in many positioning applications. Utilizing compressed sensing theory, measurements using the sparse direct position determination (DPD) methods are more accurate than the two-step methods and the traditional DPD methods. Position measurement with mobile access points (APs) expands the network coverage, increases information throughput and reduces implementation costs, which motivates us to extend sparse DPD (SDPD) to the mobility-assisted positioning system. However, one of the most critical problems in mobility-assisted positioning is the model errors coming from the AP position and velocity uncertainty. The paper proposes a phase error compensation algorithm for the SDPD algorithm based on mobile APs. The algorithm makes two key technical contributions. First, a double iteration for -norm optimization is designed to compensate phase errors and recover target distribution maps simultaneously. Second, when the data collection is incomplete or irregular, the algorithm jointly integrates the global information of target distribution maps at different APs to improve the measurement accuracy. Experimental results indicate the effectiveness of the algorithm.

Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

Alternating stationary iterative methods based on double splittings

Matrix double splitting iterations are simple in implementation while solving real non-singular (rectangular) linear systems. In this paper, we present two Alternating Double Splitting (ADS) schemes formulated by two double splittings and then alternating the respective iterations. The convergence conditions are then discussed along with comparative analysis. The set of double splittings used in each ADS scheme induces a preconditioned system which helps in showing the convergence of the ADS schemes. We also show that the classes of matrices for which one ADS scheme is better than the other, are mutually exclusive. Numerical experiments confirm that the proposed ADS schemes have several computational advantages over the existing methods. Though the problems are considered in the rectangular matrix settings, the same problems are even new in non-singular matrix settings.

Исследование динамических эффектов при микромасштабном импульсном нагружении

Рассматривается осесимметричная задача облучения тепловым импульсом цилиндрического тела. Для описания нелинейного поведения материала используется модель Боднера-Партома, обобщенная на случай многофазового состава материала. Сущность обобщения заключается в использовании правила смесей для определения параметров модели, отвечающих за предел текучести и временное сопротивление. Рассмотренная модель позволяет более точно оценить остаточное напряженно-деформированное состояние, которое при дальнейшей эксплуатационной нагрузке цилиндрических деталей существенно влияет на временное сопротивление элементов конструкций. Задача решается численно методом пошагового неявного интегрирования по времени, итерационным методом и методом конечных элементов. На каждом временном шаге реализуется двойной итерационный процесс. Первый – внутренний – связан с интегрированием системы нелинейных уравнений течения, второй – внешний – с решением уравнений движения и теплопроводности. Расчеты проводятся для конечно-элементной сетки, сгущенной в области облучения, для более точного моделирования термомеханического поведения материала. Параметры сетки выбираются по критерию практической сходимости решений. На основе численного моделирования исследуется напряженно деформированное состояние неупругого материала при учете зависимости параметров модели от фазового состава материала. Основной результат: установлены качественные и количественные эффекты влияния фазового состава на неупругие характеристики; изменение растягивающих остаточных напряжений на сжимающие. Полученные результаты могут использоваться в инженерных расчетах на прочность элементов конструкций, а также параметров технологий поверхностного укрепления.