Considerable Amount Of Computation Time Research Articles

Deep learning based hybrid POD-LSTM framework for laminar natural convection flow in a rectangular enclosure

Abstract Laminar natural convection in side-heated enclosures is characterized by transient phenomena of the working fluid till it reaches steady state. The side heating can done in several ways the most common way being heating one end at constant temperature and cooling the other end. One of the other ways is heating both sides of the enclosure at a constant heat flux. Mathematical modeling of such problems using Computational Fluid Dynamics (CFD) essentially involves considerable amount of computational time and power to predict the flow phenomena observed in actual experimentation. In the last few years, data driven model frameworks have proven to be anefficient way in saving both time and computational cost in several applications. In the present study, a data driven model framework using a combination of unsupervised machine learning (using Proper Orthogonal Decomposition [POD]) and supervised deep learning models (using Long Short Term Memory [LSTM]) has been developed and referred to as POD-LSTM framework. The selection of a few dominant spatial bases and accompanying temporal modes provides us with a reduced order model of the system. The flow is then reconstructed and compared with results of CFD simulations. The Rayleigh number (Ra) chosen for the study is 3.27 × 1010. The estimated time to reach stedy state for this Ra number is 15,000 s. The POD-LSTM framework is trained using data obtained from a validated CFD model for the first 1,000 s. The trained model was then tested to predict temporal dynamics for the entire 15,000 s. The predictions provided by POD-LSTM framework were found upto 98 % accurate compared to the ones predicted by CFD. The computational time and power was however an order of magnitude lower for the POD-LSTM framework than that required for the CFD model.

Construction of GVR weight windows maps from very low density transport simulations

Fusion-related facilities present relevant neutron radiation fields even after penetrating through a considerable thickness of shielding material. Neutronic analyses performed via Monte Carlo codes, then, need Global Variance Reduction (GVR) techniques so that low statistical uncertainty is reached efficiently throughout the geometry.Mesh-based Weight Windows is a flexible methodology used extensively for variance reduction purposes, both for Local and Global Variance Reduction. Purely stochastic GVR methodologies based on Weight Windows usually construct weight maps so that they are proportional to the forward particle flux, which is unknown a priori. Therefore, an iterative cycle is established. In each iteration, a weight map is obtained from the forward flux that allows the next iteration to reach further into the geometry, until all of it is populated.However, this iterative cycle may take a considerable amount of computer time, as many iterations are needed to fully populate the geometry. An alternative to achieve relevant penetration in a single iteration is to perform calculations at very low densities. However, a reconstruction method is needed to estimate the flux at the real density. This work studies a scheme to reconstruct the fluxes from low density calculations and compares it to already existing techniques.

Energy absorption capability of carbon/epoxy composite laminates: A novel numerical approach

While composite structures absorb energy well during crash events, crush-induced failure mechanisms and their effects on energy absorption characteristics are still unclear. Due to the complexity of the process, Finite Element (FE) models applied to estimate energy absorption capability of composite structures require a considerable amount of computational time. This study presents a novel numerical approach for the crushing process of AS4/8852 flat coupon plates in which computational cost is decreased by reducing the number of interfaces between plies and modifying the relevant properties. Experimental and numerical studies are conducted for cross-ply specimens [0/90]ns in order to understand and discuss the failure mechanisms occurring during the crushing process in detail and the influence of plate thickness on Specific Energy Absorption (SEA). Moreover, to demonstrate the validity of novel FE model for different stacking sequences, same approach is applied to the [0/45/0/−45]s plate geometry. By this method, run time is decreased by more than 50%.

Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique

Bayesian inference methods typically require a considerable amount of computation time in the calculation of forward models. This limitation restricts the application of Bayesian inference methods for the parameter identification of complex engineering problems. We propose a novel likelihood-free Bayesian inference method for structural parameter identification. An adaptive Gaussian surrogate model (GSM) was integrated with the transitional Markov chain Monte Carlo (MCMC) method for Bayesian inference. The log-likelihood function was approximated with GSM and the transitional MCMC method was used to generate the posterior distribution samples. A response reconstruction technique was combined with the likelihood-free Bayesian inference method for the parameter identification. Both numerical studies and experimental studies were conducted to verify the accuracy and efficiency of the proposed method. The results showed that the proposed method could be used to estimate the posterior probabilities of unknown structural parameters. Additionally, the proposed method was more efficient than the delayed rejection adaptive Metropolis and Gibbs sampling methods.

On the use of a two-layer model for large-eddy simulations of supersonic boundary layers with separation

The two-layer modeling approach has become one of the most promising and successful methodology for simulating turbulent boundary layers in the past ten years. In the present study, a mixed wall model for large-eddy simulations (LES) of high-speed flows is proposed which combine two approaches; the thin-Boundary Layer Equations (TBLE) model of Kawai and Larsson (1994) and the analytical wall-layer model of Duprat et al. (2011) for streamwise pressure gradients. The new hybrid model has been efficiently implemented into a three-dimensional compressible LES solver and validated against DNS of a spatially-evolving supersonic boundary layer (BL) under moderate and strong pressure gradients, before being employed for the prediction of nozzle flow separations at different flow conditions, ranging from weakly to highly over-expanded regimes. A good agreement is obtained in terms of mean and fluctuating quantities compared to the DNS results. Particularly, the current wall-modeled LES results are found to perfectly match the DNS data of supersonic BL with/out pressure gradient. It is also shown that the model can account for the effect of the large-scale turbulent motions of the outer layer, indicating a good interaction between the inner and the outer part of the wall layer. In terms of simulations costs and improvements of computing power, the obtained results highlight the capability of the current wall-modeling LES strategy in saving a considerable amount of computational time compared to the wall-resolved LES counterpart, allowing to push further the simulations limits. Furthermore, the application of these computationally low-costly LES simulations to nozzle flow separation allows to clearly identify the origin of the shock unsteadiness, and the existence of broadband and energetically-significant low-frequency oscillations (LFO) in the vicinity of the separation region.

Integration of optimization algorithm for component lumping in reservoir engineering applications

Reservoir oil and gas are ordinarily characterized with quite a lot of components which mainly differ in number of hydrogen and carbon atoms. Dealing with such large number of components requires considerable amount of computation time. It is required to lump these components into pseudo components in order to reduce the CPU computation timings. The main objective is to determine a proper lumping scheme in order to minimize the associated errors as a result of the lumping process. In accordance with this purpose, the optimization algorithm known as genetic algorithm is incorporated to the objective function developed to specify the deviation in the properties of oil, and gas. The variables utilized to figure out lumping scheme were defined in two different ways. The bounds on the input variables for the objective function were narrowed in line with the relevant results obtained from the previous studies. The variables indicating the group of non-hydrocarbon components were also included in the input set. Implementation of the proposed methodology resulted in having a proper lumping scheme with only a few iterations.

R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data

Background and ObjectiveRetrieving meaningful information from high dimensional dataset is an important and challenging task. Normally, medical dataset suffers from several issues such as curse of dimensionality problem, uncertainty, presence of missing values, non-relevant and redundant attributes, etc. Any machine learning technique applied on such data (without any preprocessing) by and large takes a considerable amount of computational time and may degrade the performance of the model. MethodsIn this article, R-Ensembler, a parameter free greedy ensemble attribute selection method is proposed adopting the concept of rough set theory by using the attribute-class, attribute-significance and attribute-attribute relevance measures to select a subset of attributes which are most relevant, significant and non-redundant from a pool of different attribute subsets in order to predict the presence or absence of different diseases in medical dataset. The main role of the proposed ensembler is to combine multiple subsets of attributes produced by different rough set filters and to produce an optimal subset of attributes for subsequent classification task. A novel n number of set intersection method is also proposed to reduce the biasness during the time of attribute selection process. Before selecting the minimal attribute set from a given data by the proposed R-Ensembler method, the dataset is preprocessed by the k nearest neighbour (kNN) imputation method for missing value treatment. ResultsExperiments are carried out on seven benchmark medical datasets collected from University of California at Irvine (UCI) repository. The performance of the proposed ensemble method is compared with five state-of-the-art attribute selection algorithms, results of which are measured using three benchmark classifiers viz., Naïve Bayes, decision trees and random forest. Experimental results clearly justify the superiority of the proposed R-Ensembler method over other attribute selection algorithms. Results of paired t-test performed on average accuracies produced by different classifiers simulated on the reduced data sets achieved by the proposed and counter part attribute selection methods confirm the statistical significance of the better reduced attribute subsets achieved by the proposed R-Ensembler method compared to others. ConclusionThe proposed ensemble method turned out to be very effective for selecting high relevant, high significant and less redundant attributes from a pool of different subsets of attributes.

A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy

Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the parameters often requires the modeling of high-dimensional functions with limited information, and it is challenging to choose an effective surrogate model over the unknown design space. To this end, the ensemble models—combined with different surrogate models—offer effective solutions. This paper presents a new ensemble model based on the least squares method, which is a regularization strategy and an augmentation strategy; we call the model the regularized least squares ensemble model (RLS-EM). Three individual surrogate models—Kriging, radial basis function, and support vector regression—are used to compose the RLS-EM. Further, the weight factors are estimated by the least squares method without using the global or local error metrics, which are used in most existing methods. To solve the collinearity in the least squares calculation process, a regularization strategy and an augmentation strategy are developed. The two strategies help explore the unknown regions and improve the accuracy on one hand; on the other hand, the collinearity can be reduced, and the overfitting phenomenon that may occur can be avoided. Six numerical functions, from two-dimensional to 12-dimensional, and a computer numerical control (CNC) milling machine bed design problem are used to verify the proposed method. The results of the numerical examples show that RLS-EM saves a considerable amount of computation time while ensuring the same level of robustness and accuracy compared with other ensemble models. The RLS-EM used for the CNC milling machine bed design problem also shows good accuracy characteristics compared with other ensemble methods.

A reconfiguration strategy for modular robots using origami folding

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

Hybrid All Zero Soft Quantized Block Detection for HEVC.

Transform and quantization account for a considerable amount of computation time in video encoding process. However, there are a large number of discrete cosine transform coefficients which are finally quantized into zeros. In essence, blocks with all zero quantized coefficients do not transmit any information, but still occupy substantial unnecessary computational resources. As such, detecting all-zero block (AZB) before transform and quantization has been recognized to be an efficient approach to speed up the encoding process. Instead of considering the hard-decision quantization (HDQ) only, in this paper, we incorporate the properties of soft-decision quantization into the AZB detection. In particular, we categorize the AZB blocks into genuine AZBs (G-AZB) and pseudo AZBs (P-AZBs) to distinguish their origins. For G-AZBs directly generated from HDQ, the sum of absolute transformed difference-based approach is adopted for early termination. Regarding the classification of P-AZBs which are generated in the sense of rate-distortion optimization, the rate-distortion models established based on transform coefficients together with the adaptive searching of the maximum transform coefficient are jointly employed for the discrimination. Experimental results show that our algorithm can achieve up to 24.16% transform and quantization time-savings with less than 0.06% RD performance loss. The total encoder time saving is about 5.18% on average with the maximum value up to 9.12%. Moreover, the detection accuracy of larger TU sizes, such as and can reach to 95% on average.

Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem

Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new attacks and the attackers’ methods. Since the traditional network protection methods fail to adequately protect the computer networks, the need for some sophisticated methodologies has been felt. In this paper, we develop a precise, sparse and robust methodology for multi-class intrusion detection problem based on the Ramp Loss K-Support Vector Classification-Regression, named “Ramp-KSVCR”. The main objectives of this research are to address the following issues; 1) Highly imbalanced and skewed attacks’ distribution; hence, we utilized the K-SVCR model as a core of our model; 2) Sensitivity of SVM and its extensions to the presence of noises and outliers in the training sets, to cope with this problem, Ramp loss function is implemented to our model; 3) and since the proposed Ramp-KSVCR model is a non-differentiable non-convex optimization problem, we took Concave–Convex Procedure (CCCP) to solve this model. Furthermore, we introduced Alternating Direction Method of Multipliers (ADMM) procedure to make our model well-adapted to be applicable in the large-scale setting and to reduce the training time. The performance of the proposed method has been evaluated by some artificial data and also by conducting some experiments with the NSL-KDD data set and UNSW-NB15 as a recently published intrusion detection data set. Experimental results not only demonstrate the superiority of the proposed method over the traditional approaches tested against it in terms of generalization power and sparsity but also saving a considerable amount of computational time.

Extracting the K-most Critical Paths in Multi-corner Multi-mode for Fast Static Timing Analysis

Detecting a set of longest paths is one of the crucial steps in static timing analysis and optimization. Recently, the process variation during manufacturing affects performance of the circuit design due to nanometer feature size. Measuring the performance of a circuit prior to its fabrication requires a considerable amount of computation time because it requires multi-corner and multi-mode analysis with process variations. An efficient algorithm of detecting the K-most critical paths in multi-corner multi-mode static timing analysis (MCMM STA) is proposed in this paper. The ISCAS’85 benchmark suite using a 32 nm technology is applied to verify the proposed method. The proposed K-most critical paths detection method reduces about 25% of computation time on average.

Explicit Direct Integrators of RK Type for Solving Special Fifth-Order Ordinary Differential Equations

The main contribution of this paper is the development of direct explicit methods of Runge-Kutta (RK) type for solving special fifth-Order Ordinary Differential Equations (ODEs). For this purpose, we have generalized RKD and RKT methods of special third and fourth-order ODEs. Using Taylor expansion, we have derived the algebraic equations of algebraic equations of order conditions for the proposed RKM integrators up to the eighth order. Based on these conditions, two RKM methods of orders five and six with three and four-stage are derived. Numerical implementation shows that the new methods agree well with existing RK methods, but requires less function evaluations. This is so due to the fact that RKM methods are direct; hence, they save considerable amount of computational time.

A Novel Concept for Reliability Evaluation Using Multiple Deterministic Analyses

A novel reliability analysis concept for large Structural/Mechanical systems represented by finite elements using multiple deterministic analyses is presented in this paper. The intent is to extract reliability information by conducing only tens instead of millions of deterministic analyses as an alternative to the classical Monte Carlo Simulation. It is particularly applicable when a deterministic analysis requires a considerable amount of computational time to satisfy the underlying physics. It is developed by integrating the second-order reliability method and an improved response surface method by removing its deficiencies. The efficiency of the integrated scheme is further improved by using advanced statistical and factorial schemes producing compounding beneficial effect. The concept is elaborated using two different illustrative examples. To validate the procedure, the underlying reliabilities are estimated by using the basic Monte Carlo simulation technique to develop the reference or benchmark value. Then, the accuracy and efficiency of the method are compared and verified.

Application of transient CFD-procedures for S-shape computation in pump-turbines with and without FSI

CFD has entered the product development process in hydraulic machines since more than three decades. Beside the actual design process, in which the most appropriate geometry for a certain task is iteratively sought, several steady-state simulations and related analyses are performed with the help of CFD. Basic transient CFD-analysis is becoming more and more routine for rotor-stator interaction assessment, but in general unsteady CFD is still not standard due to the large computational effort. Especially for FSI simulations, where mesh motion is involved, a considerable amount of computational time is necessary for the mesh handling and deformation as well as the related unsteady flow field resolution. Therefore this kind of CFD computations are still unusual and mostly performed during trouble-shooting analysis rather than in the standard development process, i.e. in order to understand what went wrong instead of preventing failure or even better to increase the available knowledge.In this paper the application of an efficient and particularly robust algorithm for fast computations with moving mesh is presented for the analysis of transient effects encountered during highly dynamic procedures in the operation of a pump-turbine, like runaway at fixed GV position and load-rejection with GV motion imposed as one-way FSI. In both cases the computations extend through the S-shape of the machine in the turbine-brake and reverse pump domain, showing that such exotic computations can be perform on a more regular base, even if quite time consuming. Beside the presentation of the procedure and global results, some highlights in the encountered flow-physics are also given.

Optimal analysis and design of large-scale domes with frequency constraints

Structural optimization involves a large number of structural analyses. When optimizing large structures, these analyses require a considerable amount of computational time and effort. However, there are specific types of structure for which the results of the analysis can be achieved in a much simpler and quicker way due to their special repetitive patterns. In this chapter, frequency constraint optimization of cyclically repeated space trusses is considered. An efficient technique is used to decompose the large initial eigenproblem into several smaller ones and thus to decrease the required computational time significantly (Kaveh and Zolghadr [1]).

Robust Learning-Based Camera Motion Characterization Scheme With Applications to Video Stabilization

This paper investigates a novel learning-based camera motion characterization scheme along with its application to the video stabilization problem. The proposed characterization scheme represents the compressed domain block motion vectors (MVs) using polar angle and magnitude histograms. Discriminative features from these two histograms are extracted and fed to a supervised learning-based hierarchical classifier for recognizing the six camera motion patterns. A comparative analysis with an existing scheme is carried out to support and validate the proposed characterization scheme. The proposed scheme works at the frame level by classifying the inter-frame camera motion patterns. This scheme is extended to classify the video segments and a novel application to video stabilization is investigated. An experimental analysis of a number of test sequences captured using a handheld video camera shows that by characterizing the smooth and jittery motions, selective video stabilization could be carried out only on those video segments that have been degraded. This approach of selective video stabilization saves considerable amount of computational time compared with running the stabilization algorithm on the entire video sequence, as proposed in the literature. The proposed strategy of using a classification scheme prior to applying the video stabilization routine offers a new paradigm to the conventional video stabilization problem. Experimental validation carried out using exhaustive search motion estimation obtained block MVs, and H.264/Advanced Video Coding-obtained MVs shows that by using the idea of selective video stabilization, up to 62% reduction in processing time can be achieved compared with video stabilization approaches wherein the entire video sequence is processed.

Reconfigurable Direct Allocation for Multiple Actuator Failures

Flight control systems are often equipped with reconfigurable control allocation schemes to redistribute control commands in the event of actuator failures. In this brief, a reconfiguration scheme is proposed and is based on the direct allocation method. An iterative algorithm is developed and uses the same lookup tables prepared offline for normal operations, thereby saving a considerable amount of computation time and process memory. To elucidate the effectiveness of the method, the proposed allocation algorithm is applied to a satellite launch vehicle model. Simulation results show satisfactory performance of the method in the presence of multiple actuator failures.

Anharmonic free energies and phonon dispersions from the stochastic self-consistent harmonic approximation: Application to platinum and palladium hydrides

Harmonic calculations based on density-functional theory are generally the method of choice for the description of phonon spectra of metals and insulators. The inclusion of anharmonic effects is, however, delicate as it relies on perturbation theory requiring a considerable amount of computer time, fast increasing with the cell size. Furthermore, perturbation theory breaks down when the harmonic solution is dynamically unstable or the anharmonic correction of the phonon energies is larger than the harmonic frequencies themselves.We present a stochastic implementation of the self-consistent harmonic approximation valid to treat anharmonicity at any temperature in the non-perturbative regime. The method is based on the minimization of the free energy with respect to a trial density matrix described by an arbitrary harmonic Hamiltonian. The minimization is performed with respect to all the free parameters in the trial harmonic Hamiltonian, namely, equilibrium positions, phonon frequencies and polarization vectors. The gradient of the free energy is calculated following a stochastic procedure. The method can be used to calculate thermodynamic properties, dynamical properties and anharmonic corrections to the Eliashberg function of the electron-phonon coupling. The scaling with the system size is greatly improved with respect to perturbation theory. The validity of the method is demonstrated in the strongly anharmonic palladium and platinum hydrides. In both cases we predict a strong anharmonic correction to the harmonic phonon spectra, far beyond the perturbative limit. In palladium hydrides we calculate thermodynamic properties beyond the quasiharmonic approximation, while in PtH we demonstrate that the high superconducting critical temperatures at 100 GPa predicted in previous calculations based on the harmonic approximation are strongly suppressed when anharmonic effects are included.

Efficient and Accurate Methods for Characterizing Effects of Framework Flexibility on Molecular Diffusion in Zeolites: CH4 Diffusion in Eight Member Ring Zeolites

Molecular dynamics (MD) and transition state theory (TST) methods are becoming efficient tools for predicting diffusion of molecules in nanoporous materials. The accuracy of predictions, however, often depends upon a major assumption that the framework of the material is rigid. This saves a considerable amount of computational time and is often the only method applicable to materials for which accurate force fields to model framework flexibility are not available. In this study, we systematically characterize the effect of framework flexibility on diffusion in four model zeolites (LTA, CHA, ERI, and BIK) that exhibit different patterns of window flexibility. We show that for molecules with kinetic diameters comparable to (or larger than) the size of the window the rigid framework approximation can produce order(s) of magnitude difference in diffusivities as compared to the simulations performed with a fully flexible framework. We also show that simple recipes to include the effect of framework flexibility are not generally accurate. To account for framework flexibility effects efficiently and reliably, we introduce two new methods in which the flexible structure is approximated as a set of discrete rigid snapshots obtained from simulations of dynamics of an empty framework, using either classical or, in principle, ab initio methods. In the first method, we perform MD simulations of diffusion in a usual manner but replace the rigid structure with a new random snapshot at a certain characteristic frequency corresponding to the breathing motion of the window, while keeping positions of adsorbate molecules constant. In the second method, we directly compute cage to cage hopping rates in each rigid snapshot using TST and average over a distribution of snapshots. Excellent agreement is obtained between diffusivities predicted with these two new methods and direct MD simulations using fully flexible structures. Both methods are orders of magnitude more efficient than the simulations with the fully flexible structure. The new methods are broadly applicable for fast and accurate predictions of both infinite dilution and finite loading diffusivities of simple molecules in zeolites and other nanoporous materials, generally without the need for an accurate flexible force field.