Computational modeling of mold filling and related free-surface flows in shape casting: an overview of the challenges involved

Accurate representation of the coupled effects between turbulent fluid flow with a free surface, heat transfer, solidification, and mold deformation has been shown to be necessary for the realistic prediction of several defects in castings and also for determining the final crystalline structure. A core component of the computational modeling of casting processes involves mold filling, which is the most computationally intensive aspect of casting simulation at the continuum level. Considering the complex geometries involved in shape casting, the evolution of the free surface, gas entrapment, and the entrainment of oxide layers into the casting make this a very challenging task in every respect. Despite well over 30 years of effort in developing algorithms, this is by no means a closed subject. In this article, we will review the full range of computational methods used, from unstructured finite-element (FE) and finite-volume (FV) methods through fully structured and block-structured approaches utilizing the cut-cell family of techniques to capture the geometric complexity inherent in shape casting. This discussion will include the challenges of generating rapid solutions on high-performance parallel cluster technology and how mold filling links in with the full spectrum of physics involved in shape casting. Finally, some indications as to novel techniques emerging now that can address genuinely arbitrarily complex geometries are briefly outlined and their advantages and disadvantages are discussed.

An economical route to one-way quantum computation

We assess the effects of a realistic intrinsic model for imperfections in cluster states by introducing noisy cluster states and characterizing their role in the one-way computational model. A suitable strategy to counter-affect these non-idealities is represented by the use of small clusters, stripped of any redundancy, which leads to the search for compact schemes for one-way quantum computation. In light of this, we quantitatively address the behavior of a simple four-qubit cluster which simulates a controlled-NOT under the influences of our model for decoherence. Our scheme can be particularly useful in an all-optical setup and the strategy we address can be directly applied in those, experimental situations where small cluster states can be constucted.

Natural three-qubit interactions in one-way quantum computing

We address the effects of natural three-qubit interactions on the computational power of one-way quantum computation. A benefit of using more sophisticated entanglement structures is the ability to construct compact and economic simulations of quantum algorithms with limited resources. We show that the features of our study are embodied by suitably prepared optical lattices, where effective three-spin interactions have been theoretically demonstrated. We use this to provide a compact construction for the Toffoli gate. Information flow and two-qubit interactions are also outlined, together with a brief analysis of relevant sources of imperfection.

Computational challenges in atomic, molecular and optical physics

Six challenges are discussed. These are the laser-driven helium atom; the laser-driven hydrogen molecule and hydrogen molecular ion: electron scattering (with ionization) from one-electron atoms; the vibrational and rotational structure of molecules such as H-3(+) and water at their dissociation limits; laser- heated clusters; and quantum degeneracy and Bose-Einstein condensation. The first four concern fundamental few-body systems where use of high-performance computing (HPC) is currently making possible accurate modelling from first principles. This leads to reliable predictions and support for laboratory experiment as well as true understanding of the dynamics. Important aspects of these challenges addressable only via a terascale facility are set out. Such a facility makes the last two challenges in the above list meaningfully accessible for the first time, and the scientific interest together with the prospective role for HPC in these is emphasized.

Noise resilience and entanglement evolution in two nonequivalent classes of quantum algorithms

The speedup provided by quantum algorithms with respect to their classical counterparts is at the origin of scientific interest in quantum computation. However, the fundamental reasons for such a speedup are not yet completely understood and deserve further attention. In this context, the classical simulation of quantum algorithms is a useful tool that can help us in gaining insight. Starting from the study of general conditions for classical simulation, we highlight several important differences between two nonequivalent classes of quantum algorithms. We investigate their performance under realistic conditions by quantitatively studying their resilience with respect to static noise. This latter refers to errors affecting the initial preparation of the register used to run an algorithm. We also compare the evolution of the entanglement involved in the different computational processes.

Quantum circuits for spin and flavor degrees of freedom of quarks forming nucleons

We discuss the quantum-circuit realization of the state of a nucleon in the scope of simple simmetry groups. Explicit algorithms are presented for the preparation of the state of a neutron or a proton as resulting from the composition of their quark constituents. We estimate the computational resources required for such a simulation and design a photonic network for its implementation. Moreover, we highlight that current work on three-body interactions in lattices of interacting qubits, combined with the measurement-based paradigm for quantum information processing, may also be suitable for the implementation of these nucleonic spin states.

Quantum annealing of an ising spin-glass by Green's function Monte Carlo

We present an implementation of quantum annealing (QA) via lattice Green's function Monte Carlo (GFMC), focusing on its application to the Ising spin glass in transverse field. In particular, we study whether or not such a method is more effective than the path-integral Monte Carlo- (PIMC) based QA, as well as classical simulated annealing (CA), previously tested on the same optimization problem. We identify the issue of importance sampling, i.e., the necessity of possessing reasonably good (variational) trial wave functions, as the key point of the algorithm. We performed GFMC-QA runs using such a Boltzmann-type trial wave function, finding results for the residual energies that are qualitatively similar to those of CA (but at a much larger computational cost), and definitely worse than PIMC-QA. We conclude that, at present, without a serious effort in constructing reliable importance sampling variational wave functions for a quantum glass, GFMC-QA is not a true competitor of PIMC-QA.

High Performance Real-time Pre-Processing for Fixed-Complexity Sphere Decoder

Pre-processing (PP) of received symbol vector and channel matrices is an essential pre-requisite operation for Sphere Decoder (SD)-based detection of Multiple-Input Multiple-Output (MIMO) wireless systems. PP is a highly complex operation, but relative to the total SD workload it represents a relatively small fraction of the overall computational cost of detecting an OFDM MIMO frame in standards such as 802.11n. Despite this, real-time PP architectures are highly inefficient, dominating the resource cost of real-time SD architectures. This paper resolves this issue. By reorganising the ordering and QR decomposition sub operations of PP, we describe a Field Programmable Gate Array (FPGA)-based PP architecture for the Fixed Complexity Sphere Decoder (FSD) applied to 4 × 4 802.11n MIMO which reduces resource cost by 50% as compared to state-of-the-art solutions whilst maintaining real-time performance.

Interacções intermoleculares fracas em materiais funcionais avançados: um estudo espectroscópico e computacional

A utilização combinada de espectroscopia vibracional e de cálculos envolvendo a teoria do funcional de densidade (DFT) possibilita o estudo de ligações de hidrogénio em fase condensada, assim como a análise da estrutura molecular dos sistemas em estudo. Por um lado, a espectroscopia vibracional permite a detecção de associações moleculares, enquanto os métodos computacionais auxiliam na obtenção de informação referente aos mecanismos de associação, nomeadamente no que diz respeito à possível estrutura de dímeros e compostos de inclusão em ciclodextrinas e às energias de interacção e de inclusão. O estudo que originou a presente dissertação pretende contribuir para o reforço da aplicação de estudos espectroscópicos e computacionais na elucidação de diversos fenómenos químicos, com especial destaque para o papel desempenhado por interacções intermoleculares fracas na estrutura e propriedades de materiais moleculares. No âmbito desta tese foram investigados os seguintes tópicos: polimorfismo e pseudopolimorfismo em sólidos farmacêuticos, transições de fase em misturas binárias de ácidos gordos, inclusão em ciclodextrinas, interacção de compostos farmacêuticos com superfícies metálicas e formação de agregados de água em materiais híbridos orgânicos-inorgânicos. Os sistemas foram analisados utilizando a espectroscopia vibracional – particularmente a espectroscopia de difusão de Raman – como técnica fundamental. Para uma melhor caracterização de processos envolvendo transições de fase, foram efectuados estudos com variação de temperatura, variação de humidade relativa e substituição isotópica. O estudo da interacção com superfícies metálicas foi realizado por espectroscopia de Raman intensificada à superfície. Dada a complexidade dos sistemas em estudo, a informação obtida por espectroscopia vibracional foi complementada por resultados de cálculos mecânico-quânticos. Em particular, os cálculos DFT foram utilizados para a optimização de geometrias e previsão de frequências vibracionais de moléculas e associações moleculares, permitindo assim a análise e interpretação de espectros vibracionais e a caracterização da estrutura de materiais.

Application of computational intelligence to engineering

Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. This article shows how CI techniques overpass the strict limits of Artificial Intelligence field and can help solving real problems from distinct engineering areas: Mechanical, Computer Science and Electrical Engineering.