Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Fiber Bundle Topology Optimization for Surface Flows

This paper presents a topology optimization approach for the surface flows on variable design domains.Via this approach,the matching between the pattern of a surface flow and the 2-manifold used to define the pattern can be optimized,where the 2-manifold is implicitly defined on another fixed 2-manifold named as the base manifold.The fiber bundle topology optimization approach is developed based on the description of the topological structure of the surface flow by using the differential geometry concept of the fiber bundle.The material distribution method is used to achieve the evolution of the pattern of the surface flow.The evolution of the implicit 2-manifold is realized via a homeomorphous map.The design variable of the pattern of the surface flow and that of the implicit 2-manifold are regularized by two sequentially implemented surface-PDE filters.The two surface-PDE filters are coupled,because they are defined on the implicit 2-manifold and base manifold,respectively.The surface Navier-Stokes equations,defined on the implicit 2-manifold,are used to describe the surface flow.The fiber bundle topology optimization problem is analyzed using the continuous adjoint method implemented on the first-order Sobolev space.Several numerical examples have been provided to demonstrate this approach,where the combination of the viscous dissipation and pressure drop is used as the design objective.

A novel triple periodic minimal surface-like plate lattice and its data-driven optimization method for superior mechanical properties

Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment,along with the advances in additive manufacturing(AM)technologies.In this work,a novel design of plate lattice structures described by a parametric model is proposed to enrich the design space of plate lattice structures with high connectivity suitable for AM processes.The parametric model takes the basic unit of the triple periodic minimal surface(TPMS)lattice as a skeleton and adopts a set of generation parameters to determine the plate lattice structure with different topologies,which takes the advantages of both plate lattices for superior specific mechanical properties and TPMS lattices for high connectivity,and therefore is referred to as a TPMS-like plate lattice(TLPL).Furthermore,a data-driven shape optimization method is proposed to optimize the TLPL structure for maximum mechanical properties with or without the isotropic constraints.In this method,the genetic algorithm for the optimization is utilized for global search capability,and an artificial neural network(ANN)model for individual fitness estimation is integrated for high efficiency.A set of optimized TLPLs at different relative densities are experimentally validated by the selective laser melting(SLM)fabricated samples.It is confirmed that the optimized TLPLs could achieve elastic isotropy and have superior stiffness over other isotropic lattice structures.

Process Parameters Optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface analysis.We built a regression prediction model and analyzed the ANOVA with the inspection results.With a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process parameters.The microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness instrument.The experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed rate.The HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in sequence.The optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks

Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.

Design Optimization of a Lattice Tower: Structure and Foundations

Produced in power plants, electrical energy is transported to places of consumption via the electricity network. At the heart of this network are the supports that allow electricity to be efficiently transported over long distances, guaranteeing the security and supply of energy to the various centers of use. In the construction of a line, supports occupy an important part in terms of safety and construction cost. It is therefore essential to optimize their use to reduce the cost of transmission lines. This work addresses this problem, which focuses on the optimal utilization of X-lattice towers in the construction of overhead power lines. The challenge is to reconcile the search for optimal cost and respect for the design, resistance and service constraints of the structure. To do this, a parameter having a strong correlation with the weight, foundation and construction cost of the X-lattice tower for 161 kV lines is determined as an important cost variable. This parameter is the wheelbase of the towers. The junction point between the structure and the foundations is obtained by measuring the forces at the base of the tower following the lowering of the loads. These efforts make it possible to size foundations which are of the inverted or isolated sole type. The results obtained reveal that from 8 meters in width, the wheelbase gradually changes until the optimum is obtained at 6.29 meters. With this wheelbase, the production cost is optimal. It clearly emerges from this study that the construction of lattice pylons with a wheelbase of approximately 6.29 meters makes it possible to optimize the cost of construction of 161 kV lines in the Republic of Benin.

Optimization of Methylene Blue Dye Adsorption onto Coconut Husk Cellulose Using Response Surface Methodology: Adsorption Kinetics, Isotherms and Reusability Studies

In this study, coconut husk cellulose was employed as a cost-effective and environmentally friendly adsorbent to eliminate methylene blue (MB) dye from aqueous solutions. The successful development of response surface methodology paired with a central composite design (RSM-CCD) enabled the optimization and modelling of the adsorption process. The study investigated the individual and combined effects of three variables (pH, contact time, and initial MB dye concentration) on the adsorption of MB dye onto coconut husk cellulose. The developed RSM-CCD model exhibited a remarkable degree of precision in predicting the removal efficiency of MB dye within the specified experimental parameters. This was demonstrated by the strong regression parameters, with an R2 value of 99.79% and an adjusted R2 value of 99.6%. The study depicted that the optimal parameters for attaining a 98.8827% removal of MB dye using coconut husk cellulose were as follows: an initial MB dye concentration of 30 mg∙L−1, contact time of 120 minutes, and pH 7 at a fixed adsorbent dose of 0.5 g. The Freundlich isotherm model provided the most satisfactory description of the equilibrium adsorption isotherms, suggesting that MB dye adsorption onto coconut husk cellulose occurs on a heterogeneous surface. The experimental results demonstrated a strong agreement with the pseudo-second-order kinetics model, indicating that the number of active sites present on the cellulose adsorbent predominantly influences the adsorption process of MB dye. Additionally, the adsorbent made from coconut husk cellulose exhibited the potential to be reused, as it retained its efficiency for a maximum of three cycles of adsorption of MB dye. The results of this study show that coconut husk cellulose has the potential to be an effective and sustainable adsorbent for removing MB dye from aqueous solutions.

Optimization of Cellulose Nanocrystal Isolation from Ayous Sawdust Using Response Surface Methodology

This study focuses on the extraction of cellulose nanocrystals (CNC), from microcrystalline cellulose (MCC), derived from Ayous sawdust. The process involves multiple steps and a large amount of chemical products. The objective of this research was to determine the effects of factors that impact the isolation process and to identify the optimal conditions for CNC isolation by using the response surface methodology. The factors that varied during the process were the quantity of MCC, the concentration of sulfuric acid, the hydrolysis time and temperature, and the ultrasonic treatment time. The response measured was the yield. The study found that with 5.80 g of microcrystalline cellulose, a sulfuric acid concentration of 63.50% (w/w), a hydrolysis time of 53 minutes, a hydrolysis temperature of 69˚C, and a sonication time of 19 minutes are the ideal conditions for isolation. The experimental yield achieved was (37.84 ± 0.99) %. The main factors influencing the process were the sulfuric acid concentration, hydrolysis time and temperature, with a significant influence (p < 0.05). Infrared characterization results showed that nanocrystals were indeed isolated. With a crystallinity of 35.23 and 79.74, respectively, for Ayous wood fiber and nanocrystalline cellulose were observed by X-ray diffraction, with the formation of type II cellulose, thermodynamically more stable than native cellulose type I.

INTEGRATION SHAPE AND SIZING OPTIMIZATION OF COMPOSITE WING STRUCTURE BASED ON RESPONSE SURFACE METHOD

An effective optimization method for the shape/sizing design of composite wing structures is presented with satisfying weight-cutting results. After decoupling, a kind of two-layer cycled optimization strategy suitable for these integrated shape/sizing optimization is obtained. The uniform design method is used to provide sample points, and approximation models for shape design variables. And the results of sizing optimization are construct- ed with the quadratic response surface method （QRSM）. The complex method based on QRSM is used to opti- mize the shape design variables and the criteria method is adopted to optimize the sizing design variables. Compared with the conventional method, the proposed algorithm is more effective and feasible for solving complex composite optimization problems and has good efficiency in weight cutting.

SCRAMJET INLET MULTI-OBJECTIVE OPTIMIZATION BASED ON RESPONSE SURFACE METHODOLOGY

The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.

Optimization of the biphasic release of indomethacin from HPMC/pectin/calcium chloride matrix tablet by response surface methodology

We studied the effect of two independent variables, the pectin/calcium chloride weight ratio and the overall matrix weight in HPMC/pectin/calcium matrix tablet, on the release of indomethacin. A two-factor 5-level central composite experimental design was employed. Responses of the Peppas correlation parameters n and K and the 10% release time （T0.1） were optimized by response surface methodology. Significant effect of the independent variables on the biphasic release parameters, n and K, was observed. N, K and T0.1 were well fitted with the second-order quadratic equations rather than linear equations. Moreover, the mathematic fitting and the response surfaces showed significant cross-interaction between the pectin/calcium chloride ratio and the overall matrix weight. The optimal formulation with larger n, longer T0.1 and smaller K consisted of medium pectin/calcium chloride ratio around 1.0 and medium matrix weight around 200 mg. Validation studies on the optimal formulations showed good predictability of the n, K and T0.1 values with biases within the range of-7.33% and 6.26%. Our results support that central composite design can be used to optimize drug release from HPMC/pectin/calcium matrix tablet with high predictability.

An Analytical Model for the Surface Electrical Field Distribution and Optimization of Bulk-Silicon Double RESURF Devices

A new 2D analytical model for the surface electrical field distribution and optimization of bulk-silicon double RESURF devices is presented. Based on the solution to the 2D Poisson＇s equation, the model gives the influence on the surface electrical field of the drain bias and structure parameters such as the doping concentration,the depth and the position of the p-top region, the thickness and the doping concentration of the drift region, and the substrate doping concentration. The dependence of breakdown voltage on the length and doping concentration of the drift region is also calculated. Further more,an effective way to gain the optimum high-voltage is also proposed. All analytical results are verified by simulation results obtained by MEDICI and previous experimental data,showing the validity of the model presented here.

Optimization design of foundation excavation for Xiluodu super-high arch dam in China

With better understanding of the quality and physico-mechanical properties of rocks of dam foundation,and the physico-mechanical properties and structure design of arch dam in association with the foundation excavation of Xiluodu arch dam,the excavation optimization design was proposed for the foundation surface on the basis of feasibility study.Common analysis and numerical analysis results demonstrated the feasibility of using the weakly weathered rocks III1and III2as the foundation surface of super-high arch dam.In view of changes in the geological conditions at the dam foundation along the riverbed direction,the design of extending foundation surface excavation area and using consolidating grouting and optimizing structure of dam bottom was introduced,allowing for harmonization of the arch dam and foundation.Three-dimensional(3D)geomechanics model test and fi nite element analysis results indicated that the dam body and foundation have good overload stability and high bearing capacity.The monitoring data showed that the behaviors of dam and foundation correspond with the designed patterns in the construction period and the initial operation period.

Accurate Evaluation of Free-form Surface Profile Error Based on Quasi Particle Swarm Optimization Algorithm and Surface Subdivision

Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline（NURBS） for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system（DCS） and measurement coordinate system（MCS） for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization（QPSO） is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.

Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling

Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.

Study on Deep Well Dewatering Optimization Design in Deep Foundation Pit and Engineering Application

Based on analyses of the theories of groundwater unsteady flow in deep well dewatering in the deep foundation pit, Theis equations are chosen to calculate and analyze the relationship between water level drawdown of confined aquifer and dewatering duration. In order to reduce engineering cost and diminish detrimental effect on ambient surrounding, optimization design target function based on the control of confined water drawdown and four restriction requisitions based on the control of safe water level, resistance to throwing up from the bottom of foundation pit, avoiding excessively great subsidence and unequal surface subsidence are proposed. A deep well dewatering project in the deep foundation pit is optimally designed. The calculated results including confined water level drawdown and surface subsidence are in close agreement with the measured results, and the optimization design can effectively control both surface subsidence outside foundation pit and unequal subsidence as a result of dewatering.

SIX SIGMA OPTIMIZATION IN SHEET METAL FORMING BASED ON DUAL RESPONSE SURFACE MODEL

Iterations in optimization and numerical simulation for the sheet metal forming process may lead to extensive computation. In addition, uncertainties in materials or processing parameters may have great influence on the design quality. A six sigma optimization method is proposed, by combining the dual response surface method （DRSM） and six sigma philosophy, to save computation cost and improve reliability and robustness of parts. Using this method, statistical technology, including the design of experiment and analysis of variance, approximate model and six sigma philosophy are integrated together to achieve improved quality. Two sheet metal forming processes are provided as examples to illustrate the proposed method.

Optimization of process parameters for the bioconversion of activated sludge by Penicillium corylophilum, using response surface methodology

The optimization of process parameters for the bioconversion of activated sludge by Penicillium corylophilum was investigated using response surface methodology （RSM）. The three parameters namely temperature of 33℃, agitation of 150 r/min, and pH of 5 were chosen as center point from the previous study of fungal treatment. The experimental data on chemical oxygen demand （COD） removal （%） were fitted into a quadratic polynomial model using multiple regression analysis. The optimum process conditions were determined by analyzing response surface three-dimensional surface plot and contour plot and by solving the regression model equation with Design Expert software. Box-Behnken design technique under RSM was used to optimize their interactions, which showed that an incubation temperature of 32.5℃, agitation of 105 r/min, and pH of 5.5 were the best conditions. Under these conditions, the maximum predicted yield of COD removal was 98.43%. These optimum conditions were used to evaluate the trail experiment, and the maximum yield of COD removal was recorded as 98.5%.

Parametric Optimization to Minimise the Surface Roughness on the Machining of GFRP Composites

The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.

Robust Collaborative Optimization Method Based on Dual-response Surface

A novel method for robust collaborative design of complex products based on dual-response surface （DRS-RCO） is proposed to solve multidisciplinary design optimization （MDO） problems under uncertainty. Collaborative optimization （CO） which decomposes the whole system into a double-level nonlinear optimization problem is widely accepted as an efficient method to solve MDO problems. In order to improve the quality of complex product in design process, robust collaborative optimization （RCO） is developed to solve those problems under uncertain conditions. RCO does optimization on the linear sum of mean and standard deviation of objective function and gets an optimal solution with high robustness. Response surfaces method is an important way to do approximation in robust design. DRS-RCO is an improved RCO method in which dual-response surface replaces system uncertainty analysis module of CO. The dual-response surface is the approximate model of mean and standard deviation of objective function respectively. In DRS-RCO, All the information of subsystems is included in dual-response surfaces. As an additional item, the standard deviation of objective function is added to the subsystem optimization. This item guarantee both the mean and standard deviation of this subsystem is reaching the minima at the same time. Finally, a test problem with two coupled subsystems is conducted to verify the feasibility and effectiveness of DRS-RCO.