Structure and energetics of hydroxylated silica clusters, (SiO2)M(H2O)N, M=8, 16 and N=1−4: A global optimisation study

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements.

Download Full-text

On the scaling of fragmentation and energy dissipation in collisions of dust aggregates

Granular Matter ◽

10.1007/s10035-021-01101-w ◽

2021 ◽

Vol 23 (2) ◽

Author(s):

Philipp Umstätter ◽

Herbert M. Urbassek

Keyword(s):

Energy Dissipation ◽

Collision Energy ◽

Velocity Range ◽

Collision Velocity ◽

Granular Mechanics ◽

Extra Factor ◽

Cluster Fragmentation ◽

Silica Clusters ◽

Large Clusters ◽

Small Clusters

Abstract Fragmentation of granular clusters may be studied by experiments and by granular mechanics simulation. When comparing results, it is often assumed that results can be compared when scaled to the same value of $$E/E_{\mathrm{sep}}$$ E / E sep , where E denotes the collision energy and $$E_{\mathrm{sep}}$$ E sep is the energy needed to break every contact in the granular clusters. The ratio $$E/E_{\mathrm{sep}}\propto v^2$$ E / E sep ∝ v 2 depends on the collision velocity v but not on the number of grains per cluster, N. We test this hypothesis using granular-mechanics simulations on silica clusters containing a few thousand grains in the velocity range where fragmentation starts. We find that a good parameter to compare different systems is given by $$E/(N^{\alpha }E_{\mathrm{sep}})$$ E / ( N α E sep ) , where $$\alpha \sim 2/3$$ α ∼ 2 / 3 . The occurrence of the extra factor $$N^{\alpha }$$ N α is caused by energy dissipation during the collision such that large clusters request a higher impact energy for reaching the same level of fragmentation than small clusters. Energy is dissipated during the collision mainly by normal and tangential (sliding) forces between grains. For large values of the viscoelastic friction parameter, we find smaller cluster fragmentation, since fragment velocities are smaller and allow for fragment recombination. Graphic abstract

Download Full-text

A novel method of global optimisation for wavefront shaping based on the differential evolution algorithm

Optics Communications ◽

10.1016/j.optcom.2020.126541 ◽

2021 ◽

Vol 481 ◽

pp. 126541

Author(s):

Yingzi Hua ◽

Xiubao Sui ◽

Shenghang Zhou ◽

Qian Chen ◽

Guohua Gu ◽

...

Keyword(s):

Differential Evolution ◽

Differential Evolution Algorithm ◽

Global Optimisation ◽

Novel Method ◽

Wavefront Shaping ◽

Evolution Algorithm

Download Full-text

A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Journal of High Energy Physics ◽

10.1007/jhep05(2021)108 ◽

2021 ◽

Vol 2021 (5) ◽

Author(s):

Csaba Balázs ◽

◽

Melissa van Beekveld ◽

Sascha Caron ◽

Barry M. Dillon ◽

...

Keyword(s):

Random Sampling ◽

Detailed Comparison ◽

Global Optimisation ◽

Adaptive Memory ◽

Test Functions ◽

Bee Colony ◽

Covariance Matrix Adaptation ◽

Global Fit ◽

Adaptation Evolution ◽

Optimisation Algorithms

Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.

Download Full-text