Protection of Aluminum Foils against Environmental Corrosion with Graphene-Based Coatings

Commercial aluminum foils were coated by graphene oxide, and its functionalized derivatives and the corrosion performance of the coated specimens were examined in acidic conditions (lithium perchlorate and sulfuric acid). Electrochemical experiments have shown that all graphene oxide-coated specimens provided up to 96% corrosion inhibition efficiency with a corresponding lower corrosion rate compared to the bare aluminum foil. Our results clearly show that graphene-related materials offer viable alternatives for the protection of aluminum, and this opens up a number of possibilities for its use in a number of commercial applications

European banking M&As:The role of financial advisors

We investigate the puzzle of banks contracting the services of external advisors for deals they can self-manage and the role of financial advisors in mergers and acquisitions among European banking firms. We also study the determinants of the choice by bank acquirers and bank targets to either appoint external advisors or manage in-house, as well as between appointing either top or lower tier advisors. Top tier advisors are more likely to be employed in debt financed and cross-border deals. We also find that most European bank mergers are managed in-house, contrary to prior findings reporting mostly externally managed deals attributed to the certification effect. Targets fail to benefit from deals where they do not match acquirer’s decision to appoint external advisors. However, there is an overall propensity to match the counter party’s tier of advisor

Development of the Turgo Impulse turbine:past and present

The Turgo Impulse turbine provides a unique and novel solution to increasing the capacity of a hydraulic impulse turbine while maintaining the nozzle and spear injector system (as used in Pelton turbines) for flow regulation. This has produced a turbine which operates in the higher flow ranges usually reserved for Francis machines while maintaining a relatively flat efficiency curve, characteristic of impulse machines. Since its invention nearly 100 years ago, the Turgo turbine has been installed in thousands of locations across the globe. The majority of the development of the Turgo turbine design has been through the use of paper based and experimental studies however recent advances in computational fluid dynamics (CFD) tools have allowed the simulation of the complex, highly turbulent, multiphase flows associated with impulse turbines and some work has been done in applying this to the Turgo design. This review looks at the development of the of the Turgo turbine since its invention in 1919 and includes the paper-based analyses, experimental studies and the more recent CFD analyses carried out on the design

Fault tolerant computer control for a Maglev transportation system

Magnetically levitated (Maglev) vehicles operating on dedicated guideways at speeds of 500 km/hr are an emerging transportation alternative to short-haul air and high-speed rail. They have the potential to offer a service significantly more dependable than air and with less operating cost than both air and high-speed rail. Maglev transportation derives these benefits by using magnetic forces to suspend a vehicle 8 to 200 mm above the guideway. Magnetic forces are also used for propulsion and guidance. The combination of high speed, short headways, stringent ride quality requirements, and a distributed offboard propulsion system necessitates high levels of automation for the Maglev control and operation. Very high levels of safety and availability will be required for the Maglev control system. This paper describes the mission scenario, functional requirements, and dependability and performance requirements of the Maglev command, control, and communications system. A distributed hierarchical architecture consisting of vehicle on-board computers, wayside zone computers, a central computer facility, and communication links between these entities was synthesized to meet the functional and dependability requirements on the maglev. Two variations of the basic architecture are described: the Smart Vehicle Architecture (SVA) and the Zone Control Architecture (ZCA). Preliminary dependability modeling results are also presented

Flow modeling in Pelton turbines by an accurate Eulerian and a fast Lagrangian evaluation method

The recent development of Computational Fluids Dynamics (CFD) has allowed the flow modeling in impulse hydro turbines that includes complex phenomena like free surface flow, multi fluid interaction, and unsteady, time dependent flow. Some commercial and open-source CFD codes, which implement Eulerian solving methods, have been validated against experimental results showing satisfactory accuracy. Nevertheless, further improvement of the flow analysis accuracy is still a challenge, while the computational cost is very high and unaffordable for multi-parametric design optimization of the turbine’s runner. In the present work, a CFD Eulerian approach is applied at first, in order to simulate the flow in the runner of a Pelton turbine model installed at the laboratory. Then, a particulate method, the Fast Lagrangian Simulation (FLS), is used for the same case, which is much faster than the Eulerian approach, and hence potentially suitable for numerical design optimization, providing that it can achieve adequate accuracy. The results of both methods for various operation conditions of the turbine, as also for modified runner and bucket designs, are presented and discussed in the paper. In all examined cases the FLS method shows very good accuracy in predicting the hydraulic efficiency of the runner, although the computed flow evolution and torque curve during the jet-runner interaction exhibit some systematic differences from the Eulerian results

Learning in PINNs: Phase transition, total diffusion, and generalization

We investigate the learning dynamics of fully-connected neural networks through the lens of gradient signal-to-noise ratio (SNR), examining the behavior of first-order optimizers like Adam in non-convex objectives. By interpreting the drift/diffusion phases in the information bottleneck theory, focusing on gradient homogeneity, we identify a third phase termed ``total diffusion", characterized by equilibrium in the learning rates and homogeneous gradients. This phase is marked by an abrupt SNR increase, uniform residuals across the sample space and the most rapid training convergence. We propose a residual-based re-weighting scheme to accelerate this diffusion in quadratic loss functions, enhancing generalization. We also explore the information compression phenomenon, pinpointing a significant saturation-induced compression of activations at the total diffusion phase, with deeper layers experiencing negligible information loss. Supported by experimental data on physics-informed neural networks (PINNs), which underscore the importance of gradient homogeneity due to their PDE-based sample inter-dependence, our findings suggest that recognizing phase transitions could refine ML optimization strategies for improved generalization