Implementation of Low Computational Cost Nonlinear Formulation for Multibody Railroad Vehicle Systems

2007 ◽  
Author(s):  
Graham G. Sanborn ◽  
Jason R. Heineman ◽  
Ahmed A. Shabana
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Companion Paper ◽  
Computer Interface ◽  
Model Parameters ◽  
Nonlinear Formulation ◽  
Vehicle Systems ◽  
Railroad Applications ◽  
Railroad Vehicle ◽  
Low Computational Cost

In a companion paper [1], a low computational cost nonlinear formulation was presented to for the analysis of the forces of long trains. The formulation allows systematically changing the number of degrees of freedom of each rail car and includes the force inputs that are typically found in railroad applications. In this paper, the implementation of this nonlinear formulation is described. A computer interface is also developed in order to allow the user to operate a virtual train model in real time and also change the model parameters and car connectivity conditions. Numerical results are presented in order to demonstrate the use of the formulation proposed and its computer implementation.

scholarly journals Component-Mode-Based Reduced Order Modeling Techniques for Mistuned Bladed Disks—Part II: Application

2000 ◽  
Vol 123 (1) ◽  
pp. 100-108 ◽  
Author(s):  
R. Bladh ◽  
M. P. Castanier ◽  
C. Pierre
Keyword(s):  
Finite Element ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Theoretical Models ◽  
Companion Paper ◽  
Reduced Order Modeling ◽  
Bladed Disks ◽  
Reduced Order ◽  
Component Interface ◽  
Forced Responses

In this paper, the component-mode-based methods formulated in the companion paper (Part I: Theoretical Models) are applied to the dynamic analysis of two example finite element models of bladed disks. Free and forced responses for both tuned and mistuned rotors are considered. Comprehensive comparisons are made among the techniques using full system finite element solutions as a benchmark. The accurate capture of eigenfrequency veering regions is of critical importance for obtaining high-fidelity predictions of the rotor’s sensitivity to mistuning. Therefore, particular attention is devoted to this subject. It is shown that the Craig–Bampton component mode synthesis (CMS) technique is robust and yields highly reliable results. However, this is achieved at considerable computational cost due to the retained component interface degrees of freedom. It is demonstrated that this problem is alleviated by a secondary modal analysis reduction technique (SMART). In addition, a non-CMS mistuning projection method is considered. Although this method is elegant and accurate, it is seen that it lacks the versatility and efficiency of the CMS-based SMART. Overall, this work shows that significant improvements on the accuracy and efficiency of current reduced order modeling methods are possible.

scholarly journals Designing Compact Convolutional Filters for Lightweight Human Pose Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shili Niu ◽  
Weihua Ou ◽  
Shihua Feng ◽  
Jianping Gou ◽  
Fei Long ◽  
...  
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Computational Cost ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Model Parameters ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Human Pose ◽  
Low Computational Cost

Existing methods for human pose estimation usually use a large intermediate tensor, leading to a high computational load, which is detrimental to resource-limited devices. To solve this problem, we propose a low computational cost pose estimation network, MobilePoseNet, which includes encoder, decoder, and parallel nonmaximum suppression operation. Specifically, we design a lightweight upsampling block instead of transposing the convolution as the decoder and use the lightweight network as our downsampling part. Then, we choose the high-resolution features as the input for upsampling to reduce the number of model parameters. Finally, we propose a parallel OKS-NMS, which significantly outperforms the conventional NMS in terms of accuracy and speed. Experimental results on the benchmark datasets show that MobilePoseNet obtains almost comparable results to state-of-the-art methods with a low compilation load. Compared to SimpleBaseline, the parameter of MobilePoseNet is only 4%, while the estimation accuracy reaches 98%.

A Low Computational Cost Nonlinear Formulation for Multibody Railroad Vehicle Systems

2007 ◽  
Author(s):  
Graham G. Sanborn ◽  
Jason R. Heineman ◽  
Ahmed A. Shabana
Keyword(s):  
Coordinate System ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Computational Cost ◽  
Space Curve ◽  
The Body ◽  
Degree Of Freedom ◽  
Arbitrary Body ◽  
Resistance Forces ◽  
Low Computational Cost

In this investigation, a multibody system formulation for the nonlinear dynamics of railroad vehicles is developed. This formulation, which permits developing simplified models for the forces acting on rail cars, allows the analysis of long trains at a low computational cost. In the dynamic models developed using the formulation proposed in this investigation, each rail car can be represented as a single rigid body. The configurations of the bodies in a train model are defined with respect to trajectory coordinate systems which follow a space curve whose geometry is defined at a preprocessing stage. In the formulation presented in this study, the number of degrees of freedom of an arbitrary body can be varied from one to six degrees of freedom. The principal degree of freedom of an arbitrary body is the arc length along the space curve. This degree of freedom defines the location of the origin and the orientation of the body trajectory coordinate system. The other five degrees of freedom define the location and orientation of the body with respect to the body trajectory coordinate system. The nonlinear equations of motion of the bodies in a train model are developed using the three-dimensional Newton-Euler equations. These equations are then expressed in terms of the trajectory coordinates and their derivatives. To this end, a velocity transformation is obtained by expressing the Cartesian and angular velocities of the bodies in terms of the time derivatives of the trajectory coordinates. Various force element models particular to rail cars are developed in this study. These forces include tractive effort, and air brake and dynamic brake forces, as well as a model of available wheel-rail adhesion. Additionally, various types of couplers are formulated as force elements, allowing the modeling of connections between cars. Resistance forces are also modeled in order to be able to simulate rolling, curve, and air resistance forces that may act on the cars during the train operations.

Component-Mode-Based Reduced Order Modeling Techniques for Mistuned Bladed Disks: Part II — Application

2000 ◽  
Author(s):  
Ronnie Bladh ◽  
Matthew P. Castanier ◽  
Christophe Pierre
Keyword(s):  
Finite Element ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Theoretical Models ◽  
Companion Paper ◽  
Reduced Order Modeling ◽  
Bladed Disks ◽  
Reduced Order ◽  
Component Interface ◽  
Forced Responses

In this paper, the component-mode-based methods formulated in the companion paper (Part I: Theoretical Models) are applied to the dynamic analysis of two example finite element models of bladed disks. Free and forced responses for both tuned and mistuned rotors are considered. Comprehensive comparisons are made among the techniques using full system finite element solutions as a benchmark. The accurate capture of eigenfrequency veering regions is of critical importance for obtaining high-fidelity predictions of the rotor’s sensitivity to mistuning. Therefore, particular attention is devoted to this subject. It is shown that the Craig-Bampton component mode synthesis (CMS) technique is robust and yields highly reliable results. However, this is achieved at considerable computational cost due to the retained component interface degrees of freedom (DOF). It is demonstrated that this problem is alleviated by a secondary modal analysis reduction technique (SMART). In addition, a non-CMS mistuning projection method is considered. Although this method is elegant and accurate, it is seen that it lacks the versatility and efficiency of the CMS-based SMART. Overall, this work shows that significant improvements on the accuracy and efficiency of current reduced order modeling methods are possible.

WAVELET ADAPTIVE MULTIRESOLUTION REPRESENTATION: APPLICATIONS TO VISCOUS MULTISCALE FLOW SIMULATIONS

2006 ◽  
Vol 04 (02) ◽  
pp. 333-343 ◽  
Author(s):  
YEVGENII A. RASTIGEJEV ◽  
SAMUEL PAOLUCCI
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Reynolds Numbers ◽  
Great Ability ◽  
Driven Cavity ◽  
Multiresolution Representation ◽  
Order Of Magnitude ◽  
Comparable Accuracy ◽  
Solution Accuracy ◽  
Low Computational Cost

We present a new wavelet-based adaptive multiresolution representation (WAMR) algorithm for the numerical solution of multiscale evolution problems. Key features of the algorithm are fast procedures for grid rearrangement, computation of derivatives, as well as the ability to minimize the degrees-of-freedom for a prescribed solution accuracy. To demonstrate the efficiency and accuracy of the algorithm, we use it to solve the two-dimensional benchmark problem of incompressible fluid-flow in a lid-driven cavity at large Reynolds numbers. The numerical experiments demonstrate the great ability of the algorithm to adapt to different scales at different locations and at different times so as to produce accurate solutions at low computational cost. Specifically, we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees-of-freedom.

Method for Lumped Parameter Simulation of Digital Displacement Pumps/Motors Based on CFD

2013 ◽  
Vol 397-400 ◽  
pp. 615-620 ◽  
Author(s):  
Daniel Beck Roemer ◽  
Per Johansen ◽  
Henrik C. Pedersen ◽  
Torben O. Andersen
Keyword(s):  
Computational Cost ◽  
Simulation Models ◽  
Lumped Parameter Model ◽  
Model Parameters ◽  
Lumped Parameter ◽  
Displacement Pump ◽  
Variable Displacement ◽  
And Performance ◽  
And Control ◽  
Low Computational Cost

Digital displacement fluid power pumps/motors offers improved efficiency and performance compared to traditional variable displacement pump/motors. These improvements are made possible by using efficient electronically controlled seat valves and careful design of the flow geometry. To optimize the design and control of digital displacement machines, there is a need for simulation models, preferably models with low computational cost. Therefore, a low computational cost generic lumped parameter model of digital displacement machine is presented, including a method for determining the needed model parameters based on steady CFD results, in order to take detailed geometry information into account. The response of the lumped parameter model is compared to a computational expensive transient CFD model for an example geometry.

Driving Torsion Scans with Wavefront Propagation

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang
Keyword(s):  
Potential Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Initial Guess ◽  
Field Development ◽  
Force Field Development ◽  
Wavefront Propagation ◽  
Open Source Software Package

<div>The parameterization of torsional / dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields.</div><div>Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms.</div><div>To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values.</div><div>However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses.</div><div>In this paper we propose a systematic and versatile workflow called \textit{TorsionDrive} to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development.</div><div>The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described.</div><div>The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.</div>

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.

Sign in / Sign up

Export Citation Format

Share Document