Composition optimization and performance prediction for ultra-stable water-based aerosol based on thermodynamic entropy theory

Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.

Concurrent multi-scale design optimization of composite frame structures using the Heaviside penalization of discrete material model

This paper deals with the concurrent multi-scale optimization design of frame structure composed of glass or carbon fiber reinforced polymer laminates. In the composite frame structure, the fiber winding angle at the micro-material scale and the geometrical parameter of components of the frame in the macro-structural scale are introduced as the independent variables on the two geometrical scales. Considering manufacturing requirements, discrete fiber winding angles are specified for the micro design variable. The improved Heaviside penalization discrete material optimization interpolation scheme has been applied to achieve the discrete optimization design of the fiber winding angle. An optimization model based on the minimum structural compliance and the specified fiber material volume constraint has been established. The sensitivity information about the two geometrical scales design variables are also deduced considering the characteristics of discrete fiber winding angles. The optimization results of the fiber winding angle or the macro structural topology on the two single geometrical scales, together with the concurrent two-scale optimization, is separately studied and compared in the paper. Numerical examples in the paper show that the concurrent multi-scale optimization can further explore the coupling effect between the macro-structure and micro-material of the composite to achieve an ultralight design of the composite frame structure. The novel two geometrical scales optimization model provides a new opportunity for the design of composite structure in aerospace and other industries.

Almost Sure Convergence of Proximal Stochastic Accelerated Gradient Methods

Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.

Composition Optimization and Microstructure Design in MOFs-Derived Magnetic Carbon-Based Microwave Absorbers:A Review

Magnetic carbon-based composites are the most attractive candidates for electromagnetic(EM)absorption because they can terminate the propagation of surplus EM waves in space by interacting with both electric and magnetic branches.Metal-organic frameworks(MOFs)have demonstrated their great potential as sacrificing precursors of magnetic metals/carbon composites,because they provide a good platform to achieve high dispersion of magnetic nanoparticles in carbon matrix.Nevertheless,the chemical composition and microstructure of these composites are always highly dependent on their precursors and cannot promise an optimal EM state favorable for EM absorption,which more or less discount the superiority of MOFs-derived strategy.It is hence of great importance to develop some accompanied methods that can regulate EM properties of MOFs-derived magnetic carbon-based composites e ectively.This review comprehensively introduces recent advancements on EM absorption enhancement in MOFs-derived magnetic carbon-based composites and some available strategies therein.In addition,some challenges and prospects are also proposed to indicate the pending issues on performance breakthrough and mechanism exploration in the related field.

Study on the Optimal Composition of Chitosan/Nano-TiO_2 Composite Film and Its Application in the Preservation of Jinqiu Pear

Water permeability can be used as a parameter to evaluate the effects of chitosan / nano-TiO2 composite film on the preservation of fruits and vegetables. In this study, using water permeability as the objective function, an L9 （3）3 orthogonal experiment was conducted with addition amounts of chitosan, nano-TiO2 and glycerin as three factors at three levels. According to the experimental results, when the percentages of chitosan, nano-TiO2 and glycerin in chitosan / nano-TiO2 composite film was 2%, 0.03% and 2.5%, respectively （ i. e. , there were 2.0 g of chitosan, 0.03 g of nano-TiO2 and 2.5 g of glycerin per 1130 g composite film solution）, the water permeability of chitesan / nano-TiO2 composite fdm reached the minimum. Chitosan / nano-TiO2 composite film prepared with the optimal composition was used for preservation of Jinqiu pear. The water loss rate, respiratory intensity and good fruit rate of preserved Jinqiu pear were determined, which indicated that the prepared chitosan / nano-TiO2 composite film exhibited good preservative effects.

DEVELOPING A NEW GENERATION OF HIGH PERFORMANCE COMPOSITE CEMENT

This paper proposed a new generation of high performance composite cement which is designed according to the optimization of composition and structure of cement paste and is manufactured by blending the differ ent components with special composite techniques. Each of these components has its different special property, and should be compatible with each other and match each other, and the properties of them are complementary mutually. At present, such kind of high performance composite cement can be manufactured with high reactivity cement clinker, ground granulated blast - furnace slag, high grade fly ash, silica fume etc..

STACKING SEQUENCE OPTIMIZA-TION OF LAMINATED COMPOSITE CYLINDER SHELL FOR MAXIMAL BUCKLING LOAD

A new optimization method for the optimization of stacking of composite glass fiber laminates is developed. The fiber orientation and angle of the layers of the cylindrical shells are sought considering the buckling load. The proposed optimization algorithm applies both finite element analysis and the mode-pursuing sampling （MPS）method. The algorithms suggest the optimal stacking sequence for achieving the maximal buckling load. The procedure is implemented by integrating ANSYS and MATLAB. The stacking sequence designing for the symmetric angle-ply three-layered and five-layered composite cylinder shells is presented to illustrate the optimization process, respectively. Compared with the genetic algorithms, the proposed optimization method is much faster and efficient for composite staking sequence plan.

Service composition based on discrete particle swarm optimization in military organization cloud cooperation

This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.

A New Nonmonotonic Trust Region Algorithm for A Class of Unconstrained Nonsmooth Optimization

This paper presents a new trust region algorithm for solving a class of composite nonsmooth optimizations. It is distinguished by the fact that this method does not enforce strict monotonicity of the objective function values at successive iterates and that this method extends the existing results for this type of nonlinear optimization with smooth, or piecewise smooth, or convex objective functions or their composition. It is proved that this algorithm is globally convergent under certain conditions. Finally, some numerical results for several optimization problems are reported which show that the nonmonotonic trust region method is competitive with the usual trust region method.

利用遗传算法和神经网络响应面来实现复合材料结构优化设计(英文)

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.

Approximate Optimality Conditions for Composite Convex Optimization Problems

The purpose of this paper is to study the approximate optimality condition for composite convex optimization problems with a cone-convex system in locally convex spaces,where all functions involved are not necessarily lower semicontinuous.By using the properties of the epigraph of conjugate functions,we introduce a new regularity condition and give its equivalent characterizations.Under this new regularity condition,we derive necessary and sufficient optimality conditions ofε-optimal solutions for the composite convex optimization problem.As applications of our results,we derive approximate optimality conditions to cone-convex optimization problems.Our results extend or cover many known results in the literature.

Topology optimization of compliant adaptive wing leading edge with composite materials

An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error（LSE） between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization（SIMP） model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle（UAV） field.

A single-level composite structure optimization method based on a blending tapered model

In order to decrease the number of design variables and improve the efficiency of com- posite structure optimal design, a single-level composite structure optimization method based on a tapered model is presented. Compared with the conventional multi-level composite structure opti- mization method, this single-level method has many advantages. First, by using a distance variable and a ply group variable, the number of design variables is decreased evidently and independent with the density of sub-regions, which makes the single-level method very suitable for large-scale composite structures. Second, it is very convenient to optimize laminate thickness and stacking sequence in the same level, which probably improves the quality of optimal result. Third, ply con-tinuity can be guaranteed between sub-regions in the single-level method, which could reduce stress concentration and manufacturing difficulty. An example of a composite wing is used to demonstrate the advantages and competence of the single-level method proposed.

APPLICATION OF SURROGATE BASED PARTICLE SWARM OPTIMIZATION TO THE RELIABILITY-BASED ROBUST DESIGN OF COMPOSITE PRESSURE VESSELS

A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.

Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony Algorithm

Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony Algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

Composition optimization and electrochemical characteristics of Co-free Fe-containing AB_5-type hydrogen storage alloys through uniform design

In order to reduce the cost of ABs-type hydrogen storage alloys, effects of substitution of Ce for La （A side） and Fe, Mn, Al for Ni （B side） on structural and electrochemical properties of （LaCe）;（NiFeMnAl）s alloys were studied systematically. To make component uniform and operation easy, uniform design （UD） method was introduced into the study of composition optimization of Co-free Fe-containing ABs-type alloys for the first time. X-ray diffraction （XRD） results showed that the designed alloys were of single CaCus-type structure phase. The replacement of Fe had a severe effect on electrochemical capacity, and the substitution of Fe and A1 had a synergetic action among the unit cell volume, cycling stability and high rate discharge property. Interestingly, it was found that the hydrogen storage alloys with excessively high plateau pressure showed a tilted line in Nyquist plot instead of the semicircle, and the current decayed rapidly to near zero at the beginning of the step in constant potential step （CPS）, indicating that electrochemical impedance spectra （EIS） and CPS cannot accurately measure the electrochemical kinetics process of the hydrogen storage alloys with excessively high plateau pressure.

An Optimization Algorithm for Service Composition Based on an Improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

Pseudomonas sp.ZXY-1,a newly isolated and highly efficient atrazine-degrading bacterium,and optimization of biodegradation using response surface methodology

Atrazine, a widely used herbicide, is increasing the agricultural production effectively, while also causing great environmental concern. Efficient atrazine-degrading bacterium is necessary to removal atrazine rapidly to keep a safe environment. In the present study, a new atrazine-degrading strain ZXY-1, identified as Pseudomonas, was isolated. This new isolated strain has a strong ability to biodegrade atrazine with a high efficiency of 9.09 mg/L/hr.Temperature, p H, inoculum size and initial atrazine concentration were examined to further optimize the degradation of atrazine, and the synthetic effect of these factors were investigated by the response surface methodology. With a high quadratic polynomial mathematical model（R^2= 0.9821） being obtained, the highest biodegradation efficiency of 19.03 mg/L/hr was reached compared to previous reports under the optimal conditions（30.71°C, pH 7.14, 4.23%（V/V） inoculum size and 157.1 mg/L initial atrazine concentration）.Overall, this study provided an efficient bacterium and approach that could be potentially useful for the bioremediation of wastewater containing atrazine.

Computer-assisted optimization of mobile phase compositions and development distance selectivity in gradient two-step development high-performance thin-layer chromatography

A computer-assisted method is presented for optimization of mobile phase compositions and development distance in gradient two-step development HPTLC. The method is based on a system which can predict the final R(f) values for gradient two-step development from values measured using five preliminary runs. The statistical scanning method is then used for optimization, using R(f) difference as the selection criterion. The method was evaluated using a mixture of eight components. Excellent agreement was obtained between predicted and experimental results.

A Mini-Batch Proximal Stochastic Recursive Gradient Algorithm with Diagonal Barzilai–Borwein Stepsize

Many machine learning problems can be formulated as minimizing the sum of a function and a non-smooth regularization term.Proximal stochastic gradient methods are popular for solving such composite optimization problems.We propose a minibatch proximal stochastic recursive gradient algorithm SRG-DBB,which incorporates the diagonal Barzilai–Borwein(DBB)stepsize strategy to capture the local geometry of the problem.The linear convergence and complexity of SRG-DBB are analyzed for strongly convex functions.We further establish the linear convergence of SRGDBB under the non-strong convexity condition.Moreover,it is proved that SRG-DBB converges sublinearly in the convex case.Numerical experiments on standard data sets indicate that the performance of SRG-DBB is better than or comparable to the proximal stochastic recursive gradient algorithm with best-tuned scalar stepsizes or BB stepsizes.Furthermore,SRG-DBB is superior to some advanced mini-batch proximal stochastic gradient methods.