Thermal description of hypoeutectic Al-Si-Cu alloys using silicon equivalency

The modeling of casting processes has remained a topic of active interest for several decades, and availability of numerous software packages on the market is a good indication of the interest that the casting industry has in this field. Most of the data used in these software packages are read or estimated from the binary or multi-component phase diagrams. Unfortunately, except for binary diagrams, many of ternary or higher order phase diagrams are still not accurate enough. Having in mind that most of the aluminum binary systems are very well established, it has been tried to transfer a multi-component system into one well known Al-Xi pseudo binary system (in this case the Al-Si phase diagram was chosen as a reference system). The new Silicon Equivalency (SiEQ) algorithm expresses the amounts of major and minor alloying elements in the aluminum melts through an 'equivalent' amount of silicon. Such a system could be used to calculate several thermo-physical and solidification characteristics of multi component as cast aluminum alloys. This lends the model the ability to make predictions of solidification characteristics of cast parts, where cooling rates are slow and the solidification process has to be known in great detail in order to avoid problems in the casting. This work demonstrates how the SiEQ algorithm can be used to calculate characteristic solidification temperatures of the multi-component hypoeutectic Al-Si-Cu alloys as well as their latent heats. SA statistical analysis of the results obtained for a wide range of alloy chemical compositions shows a very good correlation with the experimental data and the SiEQ calculations

Quantum Computing with Continuous-Variable Clusters

Continuous-variable cluster states offer a potentially promising method of implementing a quantum computer. This paper extends and further refines theoretical foundations and protocols for experimental implementation. We give a cluster-state implementation of the cubic phase gate through photon detection, which, together with homodyne detection, facilitates universal quantum computation. In addition, we characterize the offline squeezed resources required to generate an arbitrary graph state through passive linear optics. Most significantly, we prove that there are universal states for which the offline squeezing per mode does not increase with the size of the cluster. Simple representations of continuous-variable graph states are introduced to analyze graph state transformations under measurement and the existence of universal continuous-variable resource states.Comment: 17 pages, 5 figure

Single-shot quantum memory advantage in the simulation of stochastic processes

Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as atomic decay feature intrinsic randomness, whereas other complex processes, e.g. traffic congestion, are effectively probabilistic because we cannot track all relevant variables. To simulate a stochastic system's future behaviour, information about its past must be stored and thus memory is a key resource. Quantum information processing promises a memory advantage for stochastic simulation that has been validated in recent proof-of-concept experiments. Yet, in all past works, the memory saving would only become accessible in the limit of a large number of parallel simulations, because the memory registers of individual quantum simulators had the same dimensionality as their classical counterparts. Here, we report the first experimental demonstration that a quantum stochastic simulator can encode the relevant information in fewer dimensions than any classical simulator, thereby achieving a quantum memory advantage even for an individual simulator. Our photonic experiment thus establishes the potential of a new, practical resource saving in the simulation of complex systems

A reduced complexity numerical method for optimal gate synthesis

Although quantum computers have the potential to efficiently solve certain problems considered difficult by known classical approaches, the design of a quantum circuit remains computationally difficult. It is known that the optimal gate design problem is equivalent to the solution of an associated optimal control problem, the solution to which is also computationally intensive. Hence, in this article, we introduce the application of a class of numerical methods (termed the max-plus curse of dimensionality free techniques) that determine the optimal control thereby synthesizing the desired unitary gate. The application of this technique to quantum systems has a growth in complexity that depends on the cardinality of the control set approximation rather than the much larger growth with respect to spatial dimensions in approaches based on gridding of the space, used in previous literature. This technique is demonstrated by obtaining an approximate solution for the gate synthesis on $SU(4)$- a problem that is computationally intractable by grid based approaches.Comment: 8 pages, 4 figure

Application of thermal analysis in ferrous and nonferrous foundries

This paper is devoted to the memory of Professor Ljubomir Nedeljkovic (1933-2020), Head of the Department of Iron and Steel Metallurgy University of Belgrade, Serbia. Assessment of the melt quality is one of the most important casting process parameters, which allowed sound production of intricated cast parts. At the present time, various devices have been applied at foundry floors to control melt quality. Thermal analysis is one of them, widely used for melt quality control in ferrous and non-ferrous casting plants. During solidification, metal and alloys released latent heat, which magnitude is dependent on the type of phases that form during the solidification process. Plotting temperature versus time data during solidification provides useful information related to the actual solidification process. The applied technique is called thermal analysis, whereas the cooling curve is the name of such a plot. The main aim of this paper is to give a short overview of the present thermal analysis application in various foundries and to indicate the future potential use of this technique

Gate complexity using dynamic programming

The relationship between efficient quantum gate synthesis and control theory has been a topic of recent interest in the quantum computing literature. Motivated by this work, we describe how the dynamic programming technique from optimal control may be used in principle to determine gate complexity and for the optimal synthesis of quantum circuits. We illustrate the dynamic programming methodology using a simple example on the Lie group SU(2)

Implementation of Video Compression Standards in Digital Television

In this paper, a video compression standard used in digital television systems is discussed. Basic concepts of video compression and principles of lossy and lossless compression are given. Techniques of video compression (intraframe and interframe compression), the type of frames and principles of the bit rate compression are discussed. Characteristics of standard-definition television (SDTV), high-definition television (HDTV) and ultra-high-definition television (UHDTV) are given. The principles of the MPEG-2, MPEG-4 and High Efficiency Video Coding (HEVC) compression standards are analyzed. Overview of basic standards of video compression and the impact of compression on the quality of TV images and the number of TV channels in the multiplexes of terrestrial and satellite digital TV transmission are shown. This work is divided into six sections

Quantification of Feeding Regions of Hypoeutectic Al-(5, 7, 9)Si-(0-4)Cu (wt.%) Alloys Using Cooling Curve Analysis

This chapter presents the potential of the cooling curve analysis to characterize the solidification path of the cast hypoeutectic series of Al-Si-Cu alloys and to quantify their feeding regions. The aim of this work is to examine how variations in the chemical composition of Si (5, 7 and 9 wt.%) and Cu (from 0 to 4 wt.%) might affect the characteristic solidification temperatures, their corresponding fraction solid, and feeding regions of investigated alloys. These parameters collected from the cooling curve analysis can be used for better understanding of the solidification paths of Al-Si-Cu alloys and could easily be incorporated into existing simulation software packages to improve their accuracy

Thermodynamic Overfitting and Generalization: Energetic Limits on Predictive Complexity

Efficiently harvesting thermodynamic resources requires a precise understanding of their structure. This becomes explicit through the lens of information engines -- thermodynamic engines that use information as fuel. Maximizing the work harvested using available information is a form of physically-instantiated machine learning that drives information engines to develop complex predictive memory to store an environment's temporal correlations. We show that an information engine's complex predictive memory poses both energetic benefits and risks. While increasing memory facilitates detection of hidden patterns in an environment, it also opens the possibility of thermodynamic overfitting, where the engine dissipates additional energy in testing. To address overfitting, we introduce thermodynamic regularizers that incur a cost to engine complexity in training due to the physical constraints on the information engine. We demonstrate that regularized thermodynamic machine learning generalizes effectively. In particular, the physical constraints from which regularizers are derived improve the performance of learned predictive models. This suggests that the laws of physics jointly create the conditions for emergent complexity and predictive intelligence

Impact of major alloying elements on the solodification parameters of cast hypoeutectic AlSi6Cu (1–4 wt.%) and AlSi8Cu(1−4 wt.%) alloys

The present work displays the potential of cooling curve analysis to characterize the solidification path of cast hypoeutectic series of Al-Si6-Cu(1−4 wt.%) and Al-Si8- Cu(1−4 wt.%) alloys. The aim of this work was to examine how variation in chemical composition of silicon and copper may affect characteristic solidification temperatures, fraction solid, and thermal freezing range of investigated alloys. Eight different Al−Si−Cu alloys (Al-Si6-Cu1, Al-Si6-Cu2, Al-Si6-Cu3, Al-Si6-Cu4, Al-Si8-Cu1, AlSi8-Cu2, Al-Si8-Cu3 and Al-Si8-Cu4) have been analyzed applying cooling curve analysis technique. Characteristic solidification temperatures have been determined using cooling curves or their corresponding first derivative curves along with ΔT curves. Fraction solid curves determined from recorded cooling curves have been used to calculate terminal freezing range and estimate crack susceptibility coefficient for each alloy. Theoretical mode for prediction of the cracking susceptibility coefficient developed by Clyne and Davies has been considered in this work. In addition, a novel mathematical model for prediction of crack susceptibility coefficient based on data collected from cooling curve analysis has been proposed. http://dx.doi.org/10.5937/metmateng1404235