scholarly journals Design for Additive Manufacturing: Optimization of Piping Network in Compact System With Enhanced Path-Finding Approach

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert X. Gao ◽  
J. Tang
Keyword(s):  
Additive Manufacturing ◽  
Video Game ◽  
Optimal Path ◽  
Computational Cost ◽  
Design Tool ◽  
Computational Time ◽  
Path Finding ◽  
Network Systems ◽  
Efficient Manner ◽  
Visibility Graphs

This research aims at unleashing the potential of additive manufacturing technology in industrial design that can produce structures/devices with irregular component geometries to reduce sizes/weights. We explore, by means of path-finding, the length minimization of freeform hydraulic piping network in compact space under given constraints. Previous studies on path-finding have mainly focused on enhancing computational efficiency due to the need to produce rapid results in such as navigation and video-game applications. In this research, we develop a new Focal Any-Angle A* approach that combines the merits of grid-based method and visibility graph-based method. Specifically, we formulate pruned visibility graphs preserving only the useful portion of the vertices and then find the optimal path based on the candidate vertices using A*. The reduced visibility graphs enable us to outperform approximations and maintain the optimality of exact algorithms in a more efficient manner. The algorithm proposed is compared to the traditional A* on Grids, Theta* and A* on visibility graphs in terms of path length, number of nodes evaluated, as well as computational time. As demonstrated and validated through case studies, the proposed method is capable of finding the shortest path with tractable computational cost, which provides a viable design tool for the additive manufacturing of piping network systems.

Real Gas Effects in Turbomachinery Flows: A Computational Fluid Dynamics Model for Fast Computations

2004 ◽  
Vol 126 (2) ◽  
pp. 268-276 ◽  
Author(s):  
Paolo Boncinelli ◽  
Filippo Rubechini ◽  
Andrea Arnone ◽  
Massimiliano Cecconi ◽  
Carlo Cortese
Keyword(s):  
Fluid Dynamic ◽  
Computational Cost ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Design Tool ◽  
Navier Stokes ◽  
Computational Time ◽  
Real Gas ◽  
Real Gas Effects ◽  
Low Computational Cost

A numerical model was included in a three-dimensional viscous solver to account for real gas effects in the compressible Reynolds averaged Navier-Stokes (RANS) equations. The behavior of real gases is reproduced by using gas property tables. The method consists of a local fitting of gas data to provide the thermodynamic property required by the solver in each solution step. This approach presents several characteristics which make it attractive as a design tool for industrial applications. First of all, the implementation of the method in the solver is simple and straightforward, since it does not require relevant changes in the solver structure. Moreover, it is based on a low-computational-cost algorithm, which prevents a considerable increase in the overall computational time. Finally, the approach is completely general, since it allows one to handle any type of gas, gas mixture or steam over a wide operative range. In this work a detailed description of the model is provided. In addition, some examples are presented in which the model is applied to the thermo-fluid-dynamic analysis of industrial turbomachines.

Using Kriging Surrogate Models to Predict the Vibration Responses of a Submerged Riser

2020 ◽  
Author(s):  
Marcelo Damasceno ◽  
Hélio Ribeiro Neto ◽  
Tatiane Costa ◽  
Aldemir Cavalini Júnior ◽  
Ludimar Aguiar ◽  
...  
Keyword(s):  
Surrogate Model ◽  
High Performance ◽  
Computational Cost ◽  
Offshore Structures ◽  
Surrogate Models ◽  
Computational Time ◽  
Kriging Surrogate Model ◽  
Efficient Manner ◽  
Modeling Tools ◽  
Fluid Structure

Abstract Fluid-structure interaction modeling tools based on computational fluid dynamics (CFD) produce interesting results that can be used in the design of submerged structures. However, the computational cost of simulations associated with the design of submerged offshore structures is high. There are no high-performance platforms devoted to the analysis and optimization of these structures using CFD techniques. In this context, this work aims to present a computational tool dedicated to the construction of Kriging surrogate models in order to represent the time domain force responses of submerged risers. The force responses obtained from high-cost computational simulations are used as outputs for training and validated the surrogate models. In this case, different excitations are applied in the riser aiming at evaluating the representativeness of the obtained Kriging surrogate model. A similar investigation is performed by changing the number of samples and the total time used for training purposes. The present methodology can be used to perform the dynamic analysis in different submerged structures with a low computational cost. Instead of solving the motion equation associated with the fluid-structure system, a Kriging surrogate model is used. A significant reduction in computational time is expected, which allows the realization of different analyses and optimization procedures in a fast and efficient manner for the design of this type of structure.

The Use of Additive Manufacturing Techniques in the Construction of Model-Scale Propellers

2013 ◽  
Author(s):  
Matthew Garvin ◽  
Mohammed Islam ◽  
David Molyneux ◽  
Paul Herrington
Keyword(s):  
Additive Manufacturing ◽  
Computer Aided Design ◽  
Open Water ◽  
Cost Effective ◽  
Design Tool ◽  
Efficient Manner ◽  
Model Scale ◽  
Order Of Magnitude ◽  
Manufacturing Techniques ◽  
Open Water Performance

Historically, it has been expensive for model test basins to supply custom model-scale propellers for ship propulsion experiments, due to the high cost of traditional propeller fabrication methods. This paper presents a new method of manufacturing model propellers in a cost effective and time efficient manner. A new computer aided design tool has been developed to quickly develop propeller geometry, which can then be built using additive manufacturing processes. These processes reduce the cost of manufacturing propellers by an order of magnitude compared to machined metal propellers. The results of a study comparing propeller open water and self-propulsion experiments using traditional metal propellers to newly developed plastic propellers are presented. This study also includes an evaluation of the effect of surface roughness on the open water performance coefficients. The measured propulsive performance coefficients of the plastic propellers were within 2% of that of geometrically similar metal propellers with the same blade roughness.

scholarly journals Introducing DDEC6 Atomic Population Analysis: Part 5. New Method to Compute Polarizabilities and Dispersion Coefficients

2018 ◽  
Author(s):  
Thomas Manz ◽  
Taoyi Chen ◽  
Daniel J. Cole ◽  
Nidia Gabaldon Limas ◽  
Benjamin Fiszbein
Keyword(s):  
Scaling Laws ◽  
Force Fields ◽  
Population Analysis ◽  
Computational Cost ◽  
System Size ◽  
Polarizability Tensor ◽  
Computational Time ◽  
Hiv Reverse Transcriptase ◽  
Dispersion Coefficients ◽  
Efficient Manner

Polarizabilities and London dispersion forces are important to many chemical processes. Leading terms in these forces are often modeled using polarizabilities and C<sub>n</sub> (n=6, 8, 9, 10 …) dispersion coefficients. Force fields for classical atomistic simulations can be constructed using atom-in-material dispersion coefficients and polarizabilities. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C<sub>6</sub> coefficients. Overall, MCLF has improved accuracy and lower computational cost than the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C<sub>8</sub>, C<sub>9</sub>, and C<sub>10</sub> dispersion coefficients and Quantum Drude Oscillator parameters. For sufficiently large systems, our method’s required computational time and memory scale linearly with increasing system size. This is a huge improvement over the cubic computational time of direct matrix inversion. As demonstrations, we study an ice crystal containing >250,000 atoms in the unit cell and the HIV reverse transcriptase enzyme complexed with an inhibiter molecule. This method should find widespread applications to parameterize classical force fields and DFT+dispersion methods.

scholarly journals Introducing DDEC6 Atomic Population Analysis: Part 5. New Method to Compute Polarizabilities and Dispersion Coefficients

2018 ◽  
Author(s):  
Thomas Manz ◽  
Taoyi Chen ◽  
Daniel J. Cole ◽  
Nidia Gabaldon Limas ◽  
Benjamin Fiszbein
Keyword(s):  
Scaling Laws ◽  
Force Fields ◽  
Population Analysis ◽  
Computational Cost ◽  
System Size ◽  
Polarizability Tensor ◽  
Computational Time ◽  
Hiv Reverse Transcriptase ◽  
Dispersion Coefficients ◽  
Efficient Manner

Polarizabilities and London dispersion forces are important to many chemical processes. Leading terms in these forces are often modeled using polarizabilities and C<sub>n</sub> (n=6, 8, 9, 10 …) dispersion coefficients. Force fields for classical atomistic simulations can be constructed using atom-in-material dispersion coefficients and polarizabilities. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C<sub>6</sub> coefficients. Overall, MCLF has improved accuracy and lower computational cost than the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C<sub>8</sub>, C<sub>9</sub>, and C<sub>10</sub> dispersion coefficients and Quantum Drude Oscillator parameters. For sufficiently large systems, our method’s required computational time and memory scale linearly with increasing system size. This is a huge improvement over the cubic computational time of direct matrix inversion. As demonstrations, we study an ice crystal containing >250,000 atoms in the unit cell and the HIV reverse transcriptase enzyme complexed with an inhibiter molecule. This method should find widespread applications to parameterize classical force fields and DFT+dispersion methods.

Real Gas Effects in Turbomachinery Flows: A CFD Model for Fast Computations

2003 ◽  
Author(s):  
Paolo Boncinelli ◽  
Filippo Rubechini ◽  
Andrea Arnone ◽  
Massimiliano Cecconi ◽  
Carlo Cortese
Keyword(s):  
Fluid Dynamic ◽  
Computational Cost ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Design Tool ◽  
Navier Stokes ◽  
Computational Time ◽  
Real Gas ◽  
Real Gas Effects ◽  
Low Computational Cost

A numerical model was included in a three-dimensional viscous solver to account for real gas effects in the compressible Reynolds Averaged Navier-Stokes (RANS) equations. The behavior of real gases is reproduced by using gas property tables. The method consists of a local fitting of gas data to provide the thermodynamic property required by the solver in each solution step. This approach presents several characteristics which make it attractive as a design tool for industrial applications. First of all, the implementation of the method in the solver is simple and straightforward, since it does not require relevant changes in the solver structure. Moreover, it is based on a low-computational-cost algorithm, which prevents a considerable increase in the overall computational time. Finally, the approach is completely general, since it allows one to handle any type of gas, gas mixture or steam over a wide operative range. In this work a detailed description of the model is provided. In addition, some examples are presented in which the model is applied to the thermo-fluid-dynamic analysis of industrial turbomachines.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

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