Twin roll casting and melt conditioned twin-roll casting of magnesium alloys

Recently, BCAST at Brunel University has developed a MCAST (melt conditioning by advanced shear technology) process for conditioning liquid metal at temperature either above or bellow the alloy liquidus using a high shear twin-screw mechanism. The MCAST process has now been combined with the twin roll casting (TRC) process to form an innovative technology, namely, the melt conditioned twin roll casting (MC-TRC) process for casting Al-alloy and Mg-alloy strips. During the MC-TRC process, liquid alloy with a specified temperature is continuously fed into the MCAST machine. By intensive shearing under the high shear rate and high intensity of turbulence, the liquid is transformed into conditioned melt with uniform temperature and composition throughout the whole volume. The conditioned melt is then fed continuously into the twin-roll caster for strip production. The experimental results show that the AZ91D MC-TRC strips with different thicknesses have fine and uniform microstructure. The strip consists of equiaxed grains with a mean size of 60-70μm. The strip displays extremely uniform grain size and composition throughout the whole cross-section. Investigation also shows that both TRC and MC-TRC processes with reduced deformation are effective to reduce the formation of defects, particularly the formation of the central line segregations

Measurement of surface potential decay of corona-charged polymer films using the pulsed electroacoustic method

In this paper, the pulsed electroacoustic (PEA) technique that allows the determination of space charge in a dielectric material has been used to monitor the electrical potential decay of corona-charged polyethylene films of different thicknesses. To prevent possible disturbance on the surface charge during the PEA measurements, two thin polyethylene films were placed on both sides of the corona-charged sample. Charge profiles measured at different times were used to calculate the potential across the sample. The obtained potential decay was compared with the potential measured using the conventional method. A good agreement has been obtained. More importantly, the charge profile obtained using the PEA technique indicates that bipolar charge injection has taken place

Octupole degree of freedom for the critical-point candidate nucleus 152^{152}Sm in a reflection-asymmetric relativistic mean-field approach

The potential energy surfaces of even-even $^{146-156}$Sm are investigated in the constrained reflection-asymmetric relativistic mean-field approach with parameter set PK1. It is shown that the critical-point candidate nucleus $^{152}$Sm marks the shape/phase transition not only from U(5) to SU(3) symmetry, but also from the octupole-deformed ground state in $^{150}$Sm to the quadrupole-deformed ground state in $^{154}$Sm. By including the octupole degree of freedom, an energy gap near the Fermi surface for single-particle levels in $^{152}$Sm with $\beta_2 = 0.14 \sim 0.26$ is found, and the important role of the octupole deformation driving pair $\nu 2f_{7/2}$ and $\nu 1i_{13/2}$ is demonstrated.Comment: 11 pages, 3 figure

Assessing the effects of technological progress on energy efficiency in the construction industry: A case of China

Energy-saving technologies in buildings have received great attention from energy efficiency researchers in the construction sector. Traditional research tends to focus on the energy used during building operation and in construction materials production, but it usually neglects the energy consumed in the building construction process. Very few studies have explored the impacts of technological progress on energy efficiency in the construction industry. This paper presents a model of the building construction process based on Cobb-Douglas production function. The model estimates the effects of technological progress on energy efficiency with the objective to examine the role that technological progress plays in energy savings in China's construction industry. The modeling results indicated that technological progress improved energy efficiency by an average of 7.1% per year from 1997 to 2014. Furthermore, three main technological progress factors (the efficiency of machinery and equipment, the proportion change of the energy structure, and research and development investment) were selected to analyze their effects on energy efficiency improvement. These positive effects were verified, and results show the effects of first two factors are significant. Finally, recommendations for promoting energy efficiency in the construction industry are proposed

Discussion on Event Horizon and Quantum Ergosphere of Evaporating Black Holes in a Tunnelling Framework

In this paper, with the Parikh-Wilczek tunnelling framework the positions of the event horizon of the Vaidya black hole and the Vaidya-Bonner black hole are calculated respectively. We find that the event horizon and the apparent horizon of these two black holes correspond respectively to the two turning points of the Hawking radiation tunnelling barrier. That is, the quantum ergosphere coincides with the tunnelling barrier. Our calculation also implies that the Hawking radiation comes from the apparent horizon.Comment: 8 page

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science