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Posterior inference for sparse hierarchical non-stationary models
No full textGaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such models is challenging. Hierarchical models are constructed based on Gaussian Markov random fields with stochastic spatially varying parameters. Importantly, this allows for non-stationarity while also addressing the computational burden through a sparse banded representation of the precision matrix. In this setting, efficient Markov chain Monte Carlo (MCMC) sampling is challenging due to the strong coupling a posteriori of the parameters and hyperparameters. Three adaptive MCMC schemes are developed and compared making use of banded matrix operations for faster inference. Furthermore, a novel extension to higher dimensional input spaces is proposed through an additive structure that retains the flexibility and scalability of the model, while also inheriting interpretability from the additive approach. A thorough assessment of the efficiency and accuracy of the methods in nonstationary settings is presented for both simulated experiments and a computer emulation problem
Dendrites as climbing dislocations in ceramic electrolytes: Initiation of growth
No full textWe idealise dendrite growth in a ceramic electrolyte by climb of a thick edge dislocation. Growth of the dendrite occurs at constant chemical potential of Li+ at the dendrite tip: the free-energy to fracture and wedge open the electrolyte is provided by the flux of Li+ from the electrolyte into the dendrite tip. This free-energy is dependent on the Li+ overpotential at the dendrite tip and is thereby related to the imposed charging current density. The predicted critical current density agrees with measurements for Li/LLZO/Li symmetric cells: the critical current density decreases with increasing initial length of the dendrite and with increasing electrode/electrolyte interfacial ionic resistance. The simulations also reveal that a void on the cathode/electrolyte interface locally enhances the Li+ overpotential and significantly reduces the critical current density for the initiation of dendrite growth
Supply network configuration archetypes for the circular exploitation of solid waste
No full textThis research aims to use network configuration theory to propose circular supply chain archetypes for the valorisation of solid waste. The proposed network configuration archetypes are differentiated by their levels of geographic dispersion, each representing coherent clusters of waste material and supply network characteristics for the valorisation of waste streams, namely: centralised, semi-centralised and decentralised. The different types of solid waste require local (e.g., wood, organic waste), regional (e.g., glass, plastics and rubber, paper and cardboard) or pan-regional (e.g., metals and alloys) network configuration options primarily dictated by the intrinsic physico-chemical properties of the wasted material and constraints related to the processing technologies. Furthermore, the proposed network configuration archetypes dictate operational considerations, such as procurement and pre-processing options for the wasted feedstocks, along with upscale production opportunities and distribution of the value-added intermediates or end-products
Band Structure, Band Offsets, and Intrinsic Defect Properties of Few-Layer Arsenic and Antimony
No full textWe present a detailed first-principle study of few-layer arsenic and antimony electronic structures. The band structures of 2D arsenic and antimony are calculated by a hybrid functional with the spin-orbital coupling. The results show that the band gaps of arsenene (monolayer arsenic) and antimonene (monolayer antimony) are 1.93 and 1.52 eV, respectively. It is observed that the band gaps narrow in trilayer arsenic and bilayer antimony. The band alignment with HfO2 and other 2D materials is calculated to show that HfO2 is a good candidate as a gate oxide in field effect transistors. It is found that point defects such as a single vacancy or adatom will introduce several defect states in arsenene in the middle of the band gap. Meanwhile, the defect formation energy becomes negative when the Fermi level is close to the band edges. By comparison, the defect formation energy in antimonene is always positive so that the Fermi level pinning should be suppressed in contact with the reactive metal
Research Progress of Contactless Magnetization Technology: HTS Flux Pumps
No full textSuperconducting flux pump is a wireless charging technique utilizing electromagnetic induction law for exciting the superconducting magnet by pumping the magnetic flux into the superconducting closed-loop circuit without any electrical contact in order to compensate current attenuation and ensure the stability of the magnetic field. The contactless flux pump not only charges the magnet, but also quantitatively compensates the current decay of the magnet, which makes it possible for superconducting coils to work in the persistent current mode. This method of magnetizing can effectively reduce contact losses and heat losses, making the whole magnet system more compact and effective. This paper introduces the working principle and research progress of the recent flux pumping technology, reviews different types of high temperature superconducting (HTS) flux pumps proposed during the last decade, summarizes the underlying physics as well as merits and drawbacks of each type, and finally gives a potential developing trend for the future flux pumps where power engineering is largely applied
Industry 4.0: Adoption challenges and benefits for SMEs
No full textFuture industrial systems have been popularised in recent years through buzzwords such as Industry 4.0, the Internet of Things (IoT), and Cyber Physical Systems (CPS). Whilst the technologies of Industry 4.0 and likes have many conceivable benefits to manufacturing, the majority of these technologies are developed for, or by, large firms. Much of the contemporary work is therefore disconnected from the needs of small and medium-sized enterprises (SMEs), despite the fact they represent 90 % of registered companies in Europe. This study approaches the disconnect through an industrial survey of UK SMEs (n = 271, KMO = 0.701), which is the first in the UK that used to collect opinions, reinforcing the current literature on the most reported Industry 4.0 technologies (n = 20), benefits, and challenges to implementation. Flexibility, cost, efficiency, quality and competitive advantage are found to be the key benefits to Industry 4.0 adoption in SMEs. Whilst many SMEs show a desire to implement Industry 4.0 technologies for these reasons, financial and knowledge constraints are found to be key challenges
Theoretical Study on the Effects of Dislocations in Monolithic III-V Lasers on Silicon
No full textIn this work, we present an approach to modelling III-V lasers on silicon based on a travelling-wave rate equation model with sub-micrometer resolution. By allowing spatially resolved inclusion of individual dislocations along the laser cavity, our simulation results offer new insights into the physical mechanisms behind the characteristics of 980 nm In(Ga)As/GaAs quantum well (QW) and 1.3 μm quantum dot (QD) lasers grown on silicon. We identify two effects with particular importance for practical applications from studying the reduction of the local gain in carrier-depleted regions around dislocation locations and the resulting impact on threshold current increase and slope efficiency at high dislocation densities. First, a large minority carrier diffusion length is a key parameter inhibiting laser operation by enabling carrier migration into dislocations over larger areas, and secondly, increased gain in dislocation-free regions compensating for gain dips around dislocations may contribute to gain compression effects observed in directly modulated silicon-based QD lasers. We believe that this work is an important contribution in creating a better understanding of the processes limiting the capabilities of III-V lasers on silicon in order to explore suitable materials and designs for monolithic light sources for silicon photonics
Modeling packing density of granular mixtures: An artificial intelligence approach
No full textCopyright © Soil Mechanics and Geotechnical Engineering, ARC 2019.All rights reserved. The minimum and maximum packing density of soil-Scrap Tire Derived Materials are often estimated based on limited laboratory test results or to some extent, an empirical correlation. However precise modeling of void ratio characteristics of such materials is complex and usually involves many parameters might be beyond the capability of most of common physically based engineering methods. To solve this issue, Artificial Neural Network (ANN) method is used for simulating maximum and minimum packing density of Gravel-Tire Chips mixtures (GTCM). In this study, a series of maximum and minimum void ratio tests were conducted on GTCM with different fraction of gravel in mixture (GF=VG /VT ) at different mean particle size ratio of tire chips to gravel (D50,R /D50,G ). The outcome revealed that the ANN model is able to precisely predict void ratios of binary mixtures
Modeling packing density of granular mixtures: An artificial intelligence approach
No full textThe minimum and maximum packing density of soil-Scrap Tire Derived Materials are often estimated based on limited laboratory test results or to some extent, an empirical correlation. However precise modeling of void ratio characteristics of such materials is complex and usually involves many parameters might be beyond the capability of most of common physically based engineering methods. To solve this issue, Artificial Neural Network (ANN) method is used for simulating maximum and minimum packing density of Gravel-Tire Chips mixtures (GTCM). In this study, a series of maximum and minimum void ratio tests were conducted on GTCM with different fraction of gravel in mixture (GF=VG /VT ) at different mean particle size ratio of tire chips to gravel (D50,R /D50,G ). The outcome revealed that the ANN model is able to precisely predict void ratios of binary mixtures