Quantum CFD Simulations for Heat Transfer Applications

2020 ◽  
Author(s):  
Chao Lu ◽  
Zhao Hu ◽  
Bei Xie ◽  
Ning Zhang
Keyword(s):  
Heat Transfer ◽  
Quantum Computing ◽  
Linear System ◽  
Linear Equation ◽  
Linear Systems ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Phase ◽  
Cfd Simulations ◽  
Analytical Result

Abstract In this paper, computational heat transfer (CHT) equations were solved using the state-of-art quantum computing (QC) technology. The CHT equations can be discretized into a linear equation set, which can be possibly solved by a QC system. The linear system can be characterized by Ax = b. The A matrix in this linear system is a Hermitian matrix. The linear system is then solved by using the HHL algorithm, which is a quantum algorithm to solve a linear system. The quantum circuit requires an Ancilla qubit, clock qubits, qubits for b and a classical bit to record the result. The process of the HHL algorithm can be described as follows. Firstly, the qubit for b is initialized into the phase as desire. Secondly, the quantum phase estimation (QPE) is used to determine the eigenvalues of A and the eigenvalues are stored in clock qubits. Thirdly, a Rotation gate is used to rotate the inversion of eigenvalues and information is passed to the Ancilla bit to do Pauli Y-rotation operation. Fourthly, revert the whole processes to untangle qubits and measure all of the qubits to output the final results for x. From the existing literature, a few 2 × 2 matrices were successfully solved with QC technology, proving the possibility of QC on linear systems [1]. In this paper, a quantum circuit is designed to solve a CHT problem. A simple 2 by 2 linear equation is modeled for the CHT problem and is solved by using the quantum computing. The result is compared with the analytical result. This result could initiate future studies on determining the quantum phase parameters for more complicated QC linear systems for CHT applications.

Analyzing Heuristic-based Randomized Search Strategies for the Quantum Circuit Compilation Problem

2020 ◽  
Vol 174 (3-4) ◽  
pp. 259-281
Author(s):  
Angelo Oddi ◽  
Riccardo Rasconi
Keyword(s):  
Quantum Computing ◽  
Nearest Neighbor ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Quantum Gates ◽  
Ranking Functions ◽  
Solution Quality ◽  
Circuit Realization ◽  
Definition Of

In this work we investigate the performance of greedy randomised search (GRS) techniques to the problem of compiling quantum circuits to emerging quantum hardware. Quantum computing (QC) represents the next big step towards power consumption minimisation and CPU speed boost in the future of computing machines. Quantum computing uses quantum gates that manipulate multi-valued bits (qubits). A quantum circuit is composed of a number of qubits and a series of quantum gates that operate on those qubits, and whose execution realises a specific quantum algorithm. Current quantum computing technologies limit the qubit interaction distance allowing the execution of gates between adjacent qubits only. This has opened the way to the exploration of possible techniques aimed at guaranteeing nearest-neighbor (NN) compliance in any quantum circuit through the addition of a number of so-called swap gates between adjacent qubits. In addition, technological limitations (decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized. One core contribution of the paper is the definition of two lexicographic ranking functions for quantum gate selection, using two keys: one key acts as a global closure metric to minimise the solution makespan; the second one is a local metric, which favours the mutual approach of the closest qstates pairs. We present a GRS procedure that synthesises NN-compliant quantum circuits realizations, starting from a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class tailored for the MaxCut problem. We propose a comparison between the presented meta-heuristics and the approaches used in the recent literature against the same benchmarks, both from the CPU efficiency and from the solution quality standpoint. In particular, we compare our approach against a reference benchmark initially proposed and subsequently expanded in [1] by considering: (i) variable qubit state initialisation and (ii) crosstalk constraints that further restrict parallel gate execution.

Faster phase estimation

2014 ◽  
Vol 14 (3&4) ◽  
pp. 306-328
Author(s):  
Krysta M. Svore ◽  
Matthew B. Hastings ◽  
Michael Freedman
Keyword(s):  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Phase ◽  
Phase Estimation ◽  
Post Processing ◽  
Minimum Number ◽  
Circuit Depth ◽  
Lower Depth ◽  
The Cost ◽  
Random Measurements

We develop several algorithms for performing quantum phase estimation based on basic measurements and classical post-processing. We present a pedagogical review of quantum phase estimation and simulate the algorithm to numerically determine its scaling in circuit depth and width. We show that the use of purely random measurements requires a number of measurements that is optimal up to constant factors, albeit at the cost of exponential classical post-processing; the method can also be used to improve classical signal processing. We then develop a quantum algorithm for phase estimation that yields an asymptotic improvement in runtime, coming within a factor of $\log^*$ of the minimum number of measurements required while still requiring only minimal classical post-processing. The corresponding quantum circuit requires asymptotically lower depth and width (number of qubits) than quantum phase estimation.

Comparison of DSMC and CFD Models of Heat Transfer in a Rarefied Two-Dimensional Geometry

2018 ◽  
Author(s):  
Dilesh Maharjan ◽  
Mustafa Hadj-Nacer ◽  
Miles Greiner ◽  
Stefan K. Stefanov
Keyword(s):  
Heat Transfer ◽  
Rarefied Gas ◽  
Complex Geometry ◽  
Direct Simulation Monte Carlo ◽  
Accurate Method ◽  
Vacuum Drying ◽  
Helium Pressure ◽  
Two Dimensional ◽  
Dsmc Method ◽  
Cfd Simulations

During vacuum drying of used nuclear fuel (UNF) canisters, helium pressure is reduced to as low as 67 Pa to promote evaporation and removal of remaining water after draining process. At such low pressure, and considering the dimensions of the system, helium is mildly rarefied, which induces a thermal-resistance temperature-jump at gas–solid interfaces that contributes to the increase of cladding temperature. It is important to maintain the temperature of the cladding below roughly 400 °C to avoid radial hydride formation, which may cause cladding embrittlement during transportation and long-term storage. Direct Simulation Monte Carlo (DSMC) method is an accurate method to predict heat transfer and temperature under rarefied condition. However, it is not convenient for complex geometry like a UNF canister. Computational Fluid Dynamics (CFD) simulations are more convenient to apply but their accuracy for rarefied condition are not well established. This work seeks to validate the use of CFD simulations to model heat transfer through rarefied gas in simple two-dimensional geometry by comparing the results to the more accurate DSMC method. The geometry consists of a circular fuel rod centered inside a square cross-section enclosure filled with rarefied helium. The validated CFD model will be used later to accurately estimate the temperature of an UNF canister subjected to vacuum drying condition.

scholarly journals Practical Quantum Computing

2021 ◽  
Vol 2 (1) ◽  
pp. 1-35
Author(s):  
Adrien Suau ◽  
Gabriel Staffelbach ◽  
Henri Calandra
Keyword(s):  
Wave Equation ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Equation Solving ◽  
Small Quantum ◽  
Quantum Wave ◽  
Variational Algorithms ◽  
The One

In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

The Linear System Solver of Programs Named CORTH and KYLIN2

2017 ◽  
Author(s):  
Pingzhou Ming ◽  
Junjie Pan ◽  
Xiaolan Tu ◽  
Dong Liu ◽  
Hongxing Yu
Keyword(s):  
Linear System ◽  
Linear Systems ◽  
Nuclear Power ◽  
Experimental Results ◽  
Gmres Method ◽  
Thermal Hydraulics ◽  
Lattice Calculation ◽  
Minimal Residual

Sub-channel thermal-hydraulics program named CORTH and assembly lattice calculation program named KYLIN2 have been developed in Nuclear Power Institute of China (NPIC). For the sake of promoting the computing efficiency of these programs and achieving the better description on fined parameters of reactor, the programs’ structure and details are interpreted. Then the characteristics of linear systems of these programs are analyzed. Based on the Generalized Minimal Residual (GMRES) method, different parallel schemes and implementations are considered. The experimental results show that calculation efficiencies of them are improved greatly compared with the serial situation.

Adjoint Optimization of Heat Transfer Within a Stirling Engine

2021 ◽  
Author(s):  
Anthony A. DiCarlo ◽  
Rickey A. Caldwell
Keyword(s):  
Heat Transfer ◽  
Heat Sink ◽  
Convective Flow ◽  
Transfer Rate ◽  
External Heat ◽  
Stirling Engine ◽  
Flow Dynamics ◽  
Optimization Approach ◽  
Cfd Simulations ◽  
Hot Air

Abstract This work aims to determine the optimal heat sink fin shape to promote the efficient rise of hot air away from the heat sink. The heat transfer and convective flow dynamics external to a commercial Stirling engine are investigated. In particular, this study employs an adjoint optimization approach based on CFD simulations to determine the sensitivity of the objective function to the shape of the heat sink and influence on the natural convection heat flow away from the external heat sink. This deterministic optimization approach increases the heat transfer rate of the heat sink by nearly 20% in this study when performing a small number of design iterations.

Multiphase Flow in Circular and Triangular Pipes: Examining Flow Characteristics, Sand Erosion and Heat Transfer Via CFD and Experimental Work

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Thiana A. Sedrez ◽  
Siamack A. Shirazi ◽  
Gabriel Silva
Keyword(s):  
Heat Transfer ◽  
Multiphase Flow ◽  
Experimental Work ◽  
Heat Exchangers ◽  
Flow Patterns ◽  
Cfd Simulations ◽  
Liquid Distribution ◽  
Circular Pipes ◽  
Sand Erosion ◽  
Sand Flow

Abstract Air-water two-phase flow in circular pipes has been studied by many investigators. However, investigations of multiphase flow in non-circular pipes are still very rare. Triangular pipes have found a number of applications, such as multiphase flow conditioning, erosion mitigation in elbows, compact heat exchanges, solar heat collectors, and electronic cooling systems. This work presents a survey of air-water and air-water-sand flow through circular and triangular pipes. The main objective of this investigation is to study the potential effects of triangular pipe geometry on flow patterns, slug frequency, sand erosion in elbows, and heat transfer in multiphase flow. Firstly, twenty-three experiments were performed for horizontal air-water flow. Detailed videos and slug frequency measurements were collected through circular and triangular clear pipes to identify flow patterns and create a database for these pipe configurations. The effect of corners of the triangular pipe on the liquid distribution was investigated using two different orientations of triangular pipe: apex upward and downward and results of triangular pipes were compared to round tubes. Secondly, ultrasonic wall thickness erosion measurements, paint removal studies, and CFD simulations were carried out to investigate the erosion patterns and magnitudes for liquid-sand and liquid-gas-sand flows in circular and triangular elbows with the same radius of curvature and cross-sectional area. Thirdly, heat transfer rates for liquid flows were also simulated for both circular and triangular pipe cross-sections. Although similar flow patterns are observed in circular and triangular pipe configurations, the orientation of the triangular pipes seems to have an effect on the liquid distribution and slug frequency. For higher liquid rates, slug frequencies are consistently lower in the triangular pipe as compared to the circular pipe. Similarly, the triangular elbow offers better flow behavior as compared to circular elbows when investigated numerically with similar flow rates for erosion patterns for both liquid-sand flow and liquid-gas-sand flows. Experimental and CFD results show that erosion in the circular elbow is about three times larger than in the triangular elbow. Paint studies results validated erosion patterns and their relations with particle impacts. Finally, heat transfer to/from triangular pipes is shown to be more efficient than in circular pipes, making them attractive for compact heat exchangers and heat collectors. This paper represents a novel experimental work and CFD simulations to examine the effects of pipe geometries on multiphase flow in pipes with several practical applications. The present results will help to determine the efficiency of utilizing triangular pipes as compared to circular pipes for several important applications and field operations such as reducing slug frequencies of multiphase flow in pipes, and reducing solid particle erosion of elbows, and also increasing the efficiency of heat exchangers.

Study of an Iterative Solution for Boltzmann Transport Equation and Calculation of Thermal Conductivity

2018 ◽  
Vol 777 ◽  
pp. 421-425 ◽  
Author(s):  
Chhengrot Sion ◽  
Chung Hao Hsu
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Numerical Calculation ◽  
Iterative Method ◽  
Transport Equation ◽  
Linear Systems ◽  
Heat Transport ◽  
Boltzmann Transport Equation ◽  
Iterative Solution ◽  
Boltzmann Transport

Many methods have been developed to predict the thermal conductivity of the material. Heat transport is complex and it contains many unknown variables, which makes the thermal conductivity hard to define. The iterative solution of Boltzmann transport equation (BTE) can make the numerical calculation and the nanoscale study of heat transfer possible. Here, we review how to apply the iterative method to solve BTE and many linear systems. This method can compute a sequence of progressively accurate iteration to approximate the solution of BTE.

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