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The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.

The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm.

An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.

The algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas.

The proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.

This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

The purpose of this paper is to improve and to extend the use of original rank‐niche evolution strategy (RNES) algorithm to solve constrained and unconstrained multiobjective optimization problems.

A new mutation step size is developed for evolution strategy. A mixed ranking procedure is used to improve the quality of the fitness function. A self‐adaptive sharing radius is developed to save computational time. Four constraint‐treating methods are developed to solve constrained optimization problems. Two of them do not use penalty function approach.

The improved RNES algorithm finds better quality Pareto‐optimal solutions more efficiently than the previous version. For most test problems, the solutions obtained by improved RNES are better than, or at least can be compared with, results from other papers.

The application of any evolutionary algorithm to real structural optimization problems would face a problem of spending huge computational time. Some approximate analysis method needs to be incorporated with RNES to solve practical problems.

This paper provides an easier approach to find Pareto‐optimal solutions using an evolutionary algorithm. The algorithm can be used to solve both unconstrained and constrained problems.

Philip Wadsworth, who specialises in robotic welding at Loughborough University, reports on arc welding robots

This study presents a novel hp-version adaptive finite element method (FEM) to investigate the high-precision eigensolutions of the free vibration of moderately thick circular cylindrical shells, involving the issues of variable geometrical factors, such as the thickness, circumferential wave number, radius and length.

An hp-version adaptive finite element (FE) algorithm is proposed for determining the eigensolutions of the free vibration of moderately thick circular cylindrical shells via error homogenisation and higher-order interpolation. This algorithm first develops the established h-version mesh refinement method for detecting the non-uniform distributed optimised meshes, where the error estimation and element subdivision approaches based on the superconvergent patch recovery displacement method are introduced to obtain high-precision solutions. The errors in the vibration mode solutions in the global space domain are homogenised and approximately the same. Subsequently, on the refined meshes, the algorithm uses higher-order shape functions for the interpolation of trial displacement functions to reduce the errors quickly, until the solution meets a pre-specified error tolerance condition. In this algorithm, the non-uniform mesh generation and higher-order interpolation of shape functions are suitable for addressing the problem of complex frequencies and modes caused by variable structural geometries.

Numerical results are presented for moderately thick circular cylindrical shells with different geometrical factors (circumferential wave number, thickness-to-radius ratio, thickness-to-length ratio) to demonstrate the effectiveness, accuracy and reliability of the proposed method. The hp-version refinement uses fewer optimised meshes than h-version mesh refinement, and only one-step interpolation of the higher-order shape function yields the eigensolutions satisfying the accuracy requirement.

The proposed combination of methodologies provides a complete hp-version adaptive FEM for analysing the free vibration of moderately thick circular cylindrical shells. This algorithm can be extended to general eigenproblems and geometric forms of structures to solve for the frequency and mode quickly and efficiently.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

A domain‐adaptive technique which maps the unknown, time‐dependent, curvilinear geometry of annular liquid jets into a unit square is used to determine the steady state mass absorption rate and the collapse of annular liquid jets as functions of the Froude, Peclet and Weber numbers, nozzle exit angle, initial pressure and temperature of the gas enclosed by the liquid, gas concentration at the nozzle exit, ratio of solubilities at the inner and outer interfaces of the annular jet, pressure of the gas surrounding the liquid, and annular jet's thickness‐to‐radius ratio at the nozzle exit. The domain‐adaptive technique yields a system of non‐linearly coupled integrodifferential equations for the fluid dynamics of and the gas concentration in the annular jet, and an ordinary differential equation for the time‐dependent convergence length. An iterative, block‐bidiagonal technique is used to solve the fluid dynamics equations, while the gas concentration equation is solved by means of a line Gauss‐Seidel method. It is shown that the jet's collapse rate increases as the Weber number, nozzle exit angle, temperature of the gas enclosed by the annular jet, and pressure of the gas surrounding the jet are increased, but decreases as the Froude and Peclet numbers and annular jet's thickness‐to‐radius ratio at the nozzle exit are increased. It is also shown that, if the product of the inner‐to‐outer surface solubility ratio and the initial pressure ratio is smaller than one, mass is absorbed at the outer surface of the annular jet, and the mass and volume of the gas enclosed by the jet increase with time.

Numerical simulation of the single point incremental forming (SPIF) processes can be very demanding and time consuming due to the constantly changing contact conditions between the tool and the sheet surface, as well as the nonlinear material behaviour combined with non-monotonic strain paths. The purpose of this paper is to propose an adaptive remeshing technique implemented in the in-house implicit finite element code LAGAMINE, to reduce the simulation time. This remeshing technique automatically refines only a portion of the sheet mesh in vicinity of the tool, therefore following the tool motion. As a result, refined meshes are avoided and consequently the total CPU time can be drastically reduced.

SPIF is a dieless manufacturing process in which a sheet is deformed by using a tool with a spherical tip. This dieless feature makes the process appropriate for rapid-prototyping and allows for an innovative possibility to reduce overall costs for small batches, since the process can be performed in a rapid and economic way without expensive tooling. As a consequence, research interest related to SPIF process has been growing over the last years.

In this work, the proposed automatic refinement technique is applied within a reduced enhanced solid-shell framework to further improve numerical efficiency. In this sense, the use of a hexahedral finite element allows the possibility to use general 3D constitutive laws. Additionally, a direct consideration of thickness variations, double-sided contact conditions and evaluation of all components of the stress field are available with solid-shell and not with shell elements. Additionally, validations by means of benchmarks are carried out, with comparisons against experimental results.

It is worth noting that no previous work has been carried out using remeshing strategies combined with hexahedral elements in order to improve the computational efficiency resorting to an implicit scheme, which makes this work innovative. Finally, it has been shown that it is possible to perform accurate and efficient finite element simulations of SPIF process, resorting to implicit analysis and continuum elements. This is definitively a step-forward on the state-of-art in this field.

This paper aims to present the impact analysis of payload rendezvous with tethered satellite system and the design of an adaptive sliding mode controller which can deal with mass parameter uncertainty of targeted payload, so that the proposed cislunar transportation scheme with spinning tether system could be extended to a wider and more practical range.

In this work, dynamical model is first derived based on Langrangian equations to describe the motion of a spinning tether system in an arbitrary Keplerian orbit, which takes the mass of spacecraft, tether and payload into account. Orbital design and optimal open-loop control for the payload tossed by the spinning tether system are then presented. The real payload rendezvous impact around docking point is also analyzed. Based on reference acceleration trajectory given by optimal theories, a sliding mode controller with saturation functions is designed in the close-loop control of payload tossing stage under initial disturbance caused by actual rendezvous error. To alleviate the influence of inaccurate/unknown payload mass parameters, the adaptive law is designed and integrated into sliding mode controller. Finally, the performance of the proposed controller is evaluated using simulations. Simulation results validate that proposed controller is found effective in driving the spinning tether system to carry payload into desired cislunar transfer orbit and in dealing with payload mass parameter uncertainty in a relatively large range.

The results show that unideal rendezvous manoeuvres have significant impact on in-plane motion of spinning tether system, and the proposed adaptive sliding mode controller with saturation functions not only guarantees the stability but also provides good performance and robustness against the parameter and unstructured uncertainties.

This work addresses the analysis of actual impact on spinning tether system motion when payload is docking with system within tolerated docking window, rather than at the particular ideal docking point, and the robust tracking control of deep-space payload tossing missions with the spinning tether system using the adaptive sliding mode controller dealing with parameter uncertainties. This combination has not been proposed before for tracking control of multivariable spinning tether systems.

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.