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Functional redundancy optimization is one of the most effective ways to improve robotic posture and performance. However, owing to the trade-off between smoothness of the robot's motion and other important aspects of performance, the optimization problem is too nonlinear to be solved in robotic milling processes. In this article, a derivation is shown that the robot's motion will be approximately smooth when the functional redundancies for each cutter location are smoothing variables. On this basis, a Legendre surrogate model is constructed to plan the overall optimal functional redundancy by remarkably reduced computation. Moreover, a trimmed sequential linear programming method is proposed to improve the solution further. From the outcomes of a 3D S-shaped grooves real milling experiment, the robot motion paths planned by the proposed methods achieve more stable tool-tip milling performance than that of comparable methods, while being several times or even 10 times faster in computational efficiency.

The novel octahedral spherical hohlraum can provide an ideal and practical approach for the next generation of laser systems to support both direct and indirect drive to achieve predictable and reproducible fusion gain via multiple schemes. To demonstrate its advantage in a naturally high symmetry at a cylindrically configured laser facility, it requires to repoint the laser beams to approach as close as possible the ideal octahedral beam configuration with an injection angle (the angle between a beam and the normal direction of its laser entrance hole (LEH)) ranging from 50° to 60°. We report our investigation and experiment on the optimum repointing scheme at the SGIII facility, which uses 32 beams, with 8 beams entering each polar LEH at 49.5° and 55°, and 4 beams entering each equatorial LEH at 61.5° and 62.1°. It contains residual imbalance between the polar and equatorial beams, leading to an asymmetry dominated by the spherical harmonic Y20 mode, which can be remarkably reduced by the stronger backscatters of equatorial beams. Our experiment demonstrated the feasibility of the 32-beam optimum repointing scheme and generation of 175 eV under 86 kJ inside a 2.4-mm-radius octahedral hohlraum with 0.7-mm-radius LEHs, which provided a strong support for the later experiment on proof-of-concept of octahedral spherical hohlraum [Lan et al., Phys. Rev. Lett. 127, 245001 (2021)]. 2D simulations on LEH closure agree well with the observations. This work opens a novel way of realization of a quasi-spherical irradiation at a cylindrically configured laser facility without supplementary symmetry control.

Although deep learning algorithms have been widely used, their performance depends heavily on a good set of hyperparameters. This article presents an efficient Hyperband and trust-region-based mode...

We present for the first time characterization of the time-dependent radiation drive on the capsule by measuring the localized re-emitted flux at Shenguang-III prototype laser facility. The drive flux was obtained with measured re-emitted flux from the capsule and radiation fluxes from the hohlraum wall, in combination with radiation hydrodynamic simulations. It revealed that the temporal behavior of the drive flux was quite distinguished from the radiation flux from the hohlraum wall, and the drive flux was approximately 6 eV (12 eV) lower than the measured flux at up 55° (up 30°). This technique presents a novel way for the assessment of the drive flux, both in cylindrical hohlraums and novel hohlraums with more than two laser entrance holes. Pre-processed radiation hydrodynamic simulations indicate that this technique can also be applied in integrated implosion experiments utilizing standard fusion capsule with carbon-hydrogen ablators.

Product optimization requires many times of simulation which is often time-consuming. The sparse response surface, which is constructed over single orthogonal polynomial bases and sparse coefficients from a few samplings, is employed to reduce simulation times. However, it still requires many samplings for response surface of complex products. In this paper, a High Sparse Response Surface (HSRS) method based on combined bases is proposed with the following main contributions: (1) compared with a single base, a base dictionary is combined with a variety of different base functions, and maybe construct sparser response surface by less expressive bases, which reduced the number of sampling and improved the approximation accuracy, (2) lp-norm (p=1/2) minimum solution, which is calculated by the Conjugate Gradient-FOCal Underdetermined System Solver (CG-FOCUSS) method, is used to approximate the sparest solution through calculating cost and coefficient sparsity trade-off, and (3) cross-validation is employed to select good initial value to obtain approximation optimal solution, which reduces the influence of the initial value on the CG-FOCUSS algorithm result. Finally, HSRS is applied to three benchmark test functions and two engineering problem, and the results are compared with the single base sparse response surface. The results show that (1) about 14.3% to 44.4% sample points can be reduced for HSRS to achieve the same accuracy of single base sparse response surface, (2) the accuracy of HSRS with cross-validation can be increased by about 20.31% to 40.81%.

To accommodate the diverse users demands for consumer products, enterprises need to design and develop different lines of products according to different groups of users. Dynamic internet data, inc...

A new method for measuring the time-dependent drive flux at the hohlraum center is proposed as a better alternative to conventional wall-based techniques. The drive flux here is obtained by simultaneous measurement of the reemitted flux and shock velocity from a three-layered "cakelike" sample. With these two independent observables, the influence induced by the uncertainty of the material parameters of the sample can be effectively decreased. The influence from the closure of the laser entrance hole, which was the main challenge in conventional wall-based techniques, was avoided through localized reemitted flux measurement, facilitating drive flux measurement throughout the entire time history. These studies pave a new way for probing the time-dependent drive flux, for both cylindrical hohlraums and novel hohlraums with six laser entrance holes.

Radiation computation is very important for high energy density experiments design in the laser-driven Inertial Confinement Fusion. The view-factor based models are often used to calculate the radiation on the capsule inside a hohlraum. However, it usually takes much time to solve them when the number of equations is very large. In this paper, an efficient iteration approach GPU is presented. The core idea is: (1) guaranteed symmetry, strictly diagonally dominant, and positive definite properties underlying the models are described, (2) a preconditioned conjugate gradient iteration approach is presented to compute the radiation based on such guaranteed properties, and (3) such approach is then parallelized and implemented for GPU so that the large scale models, especially for the non-linear model, can be efficiently solved in reasonable time. Finally, two experimental targets for Shenguang laser facilities built in China are demonstrated and compared to validate the efficiency of the presented approach. The results show that, the models’ computation (1) can be speeded up with successive over-relax iteration method by eight times as compared with Cholesky factorization based direct approach, (2) can be accelerated more with the preconditioned conjugate gradient iteration approach by almost eight times, and (3) can be further accelerated about 2 to 4 times as it parallelized and run on the GPU, which enables the large scale models, can be efficiently solved in reasonable time on the usual desktop computers.

The response surface method is always used in optimization problems such as the optimization of vehicle suspensions because of its efficiency. A sparse response surface (SRS) is proposed to represent a double wishbone suspension model constructed from a few non-adaptive sampling points. One set of sampling points can be applied to all response surfaces. In this problem, four variations of the front wheel positioning parameters are considered as the optimization objectives, and four parameters are considered as the design variables. Four SRS models with four variables are constructed from 60 sampling points. The interior point method is employed to optimize the problem. The optimization results show that the objective of the camber angle reduces from to , the objective of the caster angle increases from to , the objective of the kingpin inclination angle reduced from to , and the objective of the toe angle increases from to .

An effective quasi-sparse response surface based on a few sampling points is proposed.The regularization of the combination of l1 norm and l2 norm is used to increase the robustness of response surface.Cross-validation is employed to calculate the parameters of response surface.The numerical test results show the approaching performance of quasi-sparse response surface is better than some other test models. Response surface method is often employed in simulation based design and optimization for complex products. The sparsity of response surface on the mathematic basis has been explored to accurately represent the variation between design variables and performance response with only a few design points, which is very beneficial to efficient design optimization. Due to the selected basis, it may lead to a large deviation, or under-fitting of the reconstructed response surface since the number of sampling points is often smaller than its sparseness.In this paper, a quasi-sparse response surface is presented to trade-off the sparsity and variation of response surface by introducing coefficient shrinkage regularization and uniformly sampling for the design points, which enables more atoms in the basis included to accurately and robustly reconstruct the surface. The group of basis atoms which are correlated with sampling points instead of the most correlated one are all selected to uniform express the sampling points, and the coefficients of basis atoms are shrunk to improve the prediction performance of the model.Finally, 9 benchmark functions and 1 engineering applications are utilized to demonstrate the significance of the presented approach by comparing with other normally used response surface models, The results shows that the accuracy and robustness of the reconstructed response surface is superior than those of other response surface approaches.

Nowadays, the society is paying more and more attention to the issues that plague the environment. There is a growing need for enterprises to design and manufacture environmentally friendly products, in order to take up the responsibility of sustainable development of the society. The entire product life cycle consists of design, manufacturing, usage and recycling phases. These phases involve various economic and environmental factors that affect the recyclability of products. Previous researchers mainly focused on the single phase of the product’s life cycle. Moreover, they did not take into account the dynamic change of the recyclable material’s cost. In this paper, a time-series forecasting methodology is proposed to evaluate the product’s recyclability at the product design phase. It considers various economic and environmental factors of different stages of the product’s life cycle. In addition, a time-series forecasting method is utilised for predicting the cost of the recycled material at the produ...

We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.

Recent J-TEXT research has highlighted the significance of the role that non-axisymmetric magnetic perturbations, so called three-dimensional (3D) magnetic perturbation (MP) fields, play in a fundamentally 2D concept, i.e. tokamaks. This paper presents the J-TEXT results achieved over the last two years, especially on the impacts of 3D MP fields on magnetohydrodynamic instabilities, plasma disruptions and plasma turbulence transport. On J-TEXT, the resonant MP (RMP) system, capable of providing either a static or a high frequency (up to 8 kHz) rotating RMP field, has been upgraded by adding a new set of 12 in-vessel saddle coils. The shattered pellet injection system was built in J-TEXT in the spring of 2018. The new capabilities advance J-TEXT to be at the forefront of international magnetic fusion facilities, allowing flexible study of 3D effects and disruption mitigation in a tokamak. The fast rotating RMP field has been successfully applied for avoidance of mode locking and the prevention of plasma disruption. A new control strategy, which applies pulsed RMP to the tearing mode only during the accelerating phase region, was proved by nonlinear numerical modelling to be efficient in accelerating mode rotation and even completely suppresses the mode. Remarkably, the rotating tearing mode was completely suppressed by the electrode biasing. The impacts of 3D magnetic topology on the turbulence has been investigated on J-TEXT. It is found that the fluctuations of electron density, electron temperature and plasma potential can be significantly modulated by the island structure, and a larger fluctuation level appears at the X-point of islands. The suppression of runaway electrons during disruptions is essential to the operation of ITER, and it has been reached by utilizing the 3D magnetic perturbations on J-TEXT. This may provide an alternative mechanism of runaway suppression for large-scale tokamaks and ITER.

In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors.

Response surface-based simulation optimization method is widely used in the design of complex products for its low-cost in optimization target valuation. However, when design variables increase, it often takes considerable time for high-dimensional response surface to search the optimal point, which falls easily into the local optimum due to the large search space. To solve these problems, a GPU (Graphic Processing Unit) parallel optimization based on branch and bound is proposed in this paper, of which the main algorithm flow can be described as the following steps: the optimization space is branched to subsets and mapped to different GPU threads; the Chebyshev response surface is constructed within the threads; the compact convex hull of the subsets are obtained through the interval operation, and the optimization space is reduced on a large scale by pruning; all subsets that may contain optimal design points are efficiently obtained by repeating spatial subdivision and demarcation; finally, all the reserved subsets are mapped to different GPU threads, and all global optimization design points are obtained through sequential quadratic programming and comparative analysis.

Physical experiment design and optimization is very essential for laser driven inertial confinement fusion due to the high cost of each shot. However, only limited experiments with simple structure or shape on several laser facilities can be designed and evaluated in available codes, and targets are usually defined by programming, which may lead to it difficult for complex shape target design and optimization on arbitrary laser facilities. A unified modeling approach for physical experiment design and optimization on any laser facilities is presented in this paper. Its core idea includes: (1) any laser facility can be flexibly defined and included with two scripts, (2) complex shape targets and laser beams can be parametrically modeled based on features, (3) an automatically mapping scheme of laser beam energy onto discrete mesh elements of targets enable targets or laser beams be optimized without any additional interactive modeling or programming, and (4) significant computation algorithms are additionally presented to efficiently evaluate radiation symmetry on the target. Finally, examples are demonstrated to validate the significance of such unified modeling approach for physical experiments design and optimization in laser driven inertial confinement fusion.

Camera calibration is essential for accurate product visual inspection. In this paper, a novel efficient camera calibration approach based on K-Singular Value Decomposition (K-SVD) sparse dictionary learning is presented, in which, (1) the nonlinear optimization model with ten calibration parameters is formulated, (2) a large amount of images of checkerboards are acquired offline at different locations in the working volume of the camera to construct a sparse dictionary through K-SVD sparse dictionary learning, which enables such non-linear optimization model appropriately initialized and efficiently converges, and (3) in addition, the sparse dictionary can be further updated with the new acquired image so that it can be used to timely characterize the camera variation due to lens aging, and still be used to obtain appropriate initial values. Finally, three experiments are demonstrated to validate the efficiency of the presented approach. The results show that (1) the camera can be accurately calibrated with only one image, which can save almost more than 60 percent calibration time, and is very beneficial to online calibration for quality inspection, and (2) the calibration is much more accurate than that of traditional approaches with only one image having identical grid pixels.

Radiation flux computation on the target is very important for laser driven Inertial Confinement Fusion, and view-factor based equation models (MacFarlane, 2003; Srivastava et al., 2000) are often used to compute this radiation flux on the capsule or samples inside the hohlraum. However, the equation models do not lead to sparse matrices and may involve an intensive solution process when discrete mesh elements become smaller and the number of equations increases. An efficient approach for the computation of radiation flux is proposed in this paper, in which, (1) symmetric and positive definite properties are achieved by transformation, and (2) an efficient Cholesky factorization algorithm is applied to significantly accelerate such equations models solving process. Finally, two targets on a laser facility built in China are considered to validate the computing efficiency of present approach. The results show that the radiation flux computation can be accelerated by a factor of 2.

Purpose – The purpose of this paper is to present a novel strategy used for acceleration of free-vibration analysis, in which the hierarchical matrices structure and Compute Unified Device Architecture (CUDA) platform is applied to improve the performance of the traditional dual reciprocity boundary element method (DRBEM). Design/methodology/approach – The DRBEM is applied in forming integral equation to reduce complexity. In the procedure of optimization computation, ℋ-Matrices are introduced by applying adaptive cross-approximation method. At the same time, this paper proposes a high-efficiency parallel algorithm using CUDA and the counterpart of the serial effective algorithm in ℋ-Matrices for inverse arithmetic operation. Findings – The analysis for free-vibration could achieve impressive time and space efficiency by introducing hierarchical matrices technique. Although the serial algorithm based on ℋ-Matrices could obtain fair performance for complex inversion operation, the CUDA parallel algorithm would further double the efficiency. Without much loss in accuracy according to the examination of the numerical example, the relative error appeared in approximation process can be fixed by increasing degrees of freedoms or introducing certain amount of internal points. Originality/value – The paper proposes a novel effective strategy to improve computational efficiency and decrease memory consumption of free-vibration problems. ℋ-Matrices structure and parallel operation based on CUDA are introduced in traditional DRBEM.

Computation-intensive analyses/simulations are becoming increasingly common in engineering design problems. To improve the computation efficiency, surrogate models are used to replace expensive simulations of engineering problems. This paper proposes a new high-fidelity surrogate modeling approach which is called the Sparsity-promoting Polynomial Response Surface (SPPRS). In the SPPRS model, a series of Legendre polynomials is selected as basis functions, and its number is compatible with the sample size so as to enhance the expression ability for complex functional relationships. The coefficients associated with basis functions are estimated using a “sparsity-promoting” regression approach which is an ensemble of two techniques: least squares and l 1 -norm regularization. As a result, only these basis functions relevant to explain the function relationship are picked out, and that dedicates to ease the problem of overfitting for training points. With the sparsity-promoting regression approach, such a surrogate model intends to capture both the global trend of the functional variation and a reasonable local accuracy in the neighborhood of training points. Additionally, Latin hypercube design (LHD) is proved conducive to improving the predictive capability of our model. The SPPRS is applied to seven benchmark test functions and a complex engineering problem. The results illustrate the promising benefits of this novel surrogate modeling technique.

Major plasma disruptions in tokamaks often generate runaway currents, which contain electrons with energies of several tens of megaelectron-volts (MeV). These currents can cause substantial damage when control is lost and the current hits the limiters or the vessel wall. The interaction between the runaway electrons and the impurities inside the plasma results in soft X-ray emission, which can provide detailed information about the runaway generation process and the confinement of runaway electrons. A vertical soft X-ray array at the top of Joint Texas Experimental Tokamak (J-TEXT) was used to study the runaway beams resulting from major disruptions. Runaway electron production and confinement of runaway current were observed by using soft X-ray images.