Structural models based on 3D constitutive laws: Variational structure and numerical solution
مدل های ساختاری مبتنی بر قوانین سازنده سه بعدی: ساختار تغییرات و راه حل عددی-2020
In all structural models, the section or fiber response is a relation between the strain measures and the stress resultants. This relation can only be expressed in a simple analytical form when the material response is linear elastic. For other, more complex and interesting situations, kinematic and kinetic hypotheses need to be invoked, and a constrained three-dimensional constitutive relation has to be employed at every point of the section in order to implement non-linear and dissipative constitutive laws into dimensionally reduced structural models. In this article we explain in which sense reduced constitutive models can be expressed as minimization problems, helping to formulate the global equilibrium as a single optimization problem. Casting the problem this way has implications from the mathematical and numerical points of view, naturally defining error indicators. General purpose solution algorithms for constrained material response, with and without optimization character, are discussed and provided in an open-source library.
Keywords: Structural models | Constitutive models | Variational method | Error estimation
Error estimation of the parametric non-intrusive reduced order model using machine learning
تخمین خطای مدل مرتبه کاهش یافته غیر پارامتری با استفاده از یادگیری ماشین-2019
A novel error estimation method for the parametric non-intrusive reduced order model (P-NIROM) based on machine learning is presented. This method relies on constructing a set of response functions for the errors between the high fidelity full model solutions and P-NIROM using machine learning method, particularly, Gaussian process regression method. This yields closer solutions agreement with the high fidelity full model. The novelty of this work is that it is the first time to use machine learning method to derive error estimate for the P-NIROM. The capability of the new error estimation method is demonstrated using three numerical simulation examples: flow past a cylinder, dam break and 3D fluvial channel. It is shown that the results are closer to those of the high fidelity full model when considering error terms. In addition, the interface between two phases of dam break case is captured well if the error estimator is involved in the P-NIROM. CrownCopyright⃝c 2019 Published by ElsevierB.V.All rights reserved.
Keywords: NIROM | Machine learning | Gaussian process regression | Error estimation
A novel dynamics model of ball-screw feed drives based on theoretical derivations and deep learning
دل دینامیکی جدید درایوهای خورشیدی پیچ بر اساس مشتقات نظری و یادگیری عمیق-2019
High fidelity models of feed drive are critical factors to increase positioning accuracy and decrease contour error. To predict feed drives dynamics, this paper reports a novel method for modeling dynamics of feed drive by combining advantages of theoretical derivations and deep learning. First, the paper derives a rigid-flexible-combined dynamics model (RFCDM) for feed drive from classical dynamics theory. Then parameters identification of RFCDM is accomplished by referring product manuals and conducting constant velocity experiment with different feed rates. Continuous action reinforcement learning automata (CARLA) is adopted to tune all parameters of RFCDM simultaneously. A simulation error estimation model (SEEM) is applied to approximate simulation error between models sim- ulation position and worktables actual position. The hybrid dynamics model (HDM) of feed drives which integrates RFCDM with SEEM is validated by experiments with various tra- jectories. Experimental results show that the gap between HDMs prediction position and worktables actual position is on the order of magnitude of 0.01 mm which is about 1/10 of the tracking error, indicating the HDM can predict the dynamics of feed drives with safe accuracy.
Keywords: Feed drive | Dynamics model | CARLA | Deep learning
A multihypothesis set approach for mobile robot localization using heterogeneous measurements provided by the Internet of Things
یک رویکرد چند فرض برای محلی سازی ربات های موبایل با استفاده از اندازه گیری های ناهمگن ارائه شده از اینترنت اشیاء -2017
Mobile robot localization consists in estimation of robot pose by using real-time measurements. The Internet of Things (IoT) adds a new dimension to this process by enabling communications with smart objects at anytime and anywhere. Thus data used by localization process can come both from the robot on-board sensors and from environment objects, mobile or not, able to sense the robot. The paper considers localization problem as a nonlinear bounded-error estimation of the state vector whose components are the robot coordinates. The approach based on interval analysis is able to answer the constraints of IoT by easily taking account a heterogeneous set and a variable number of measurements. Bounded-error state estimation can be an alternative to other approaches, notably particle filtering which is sensible to non-consistent measures, large measure errors, and drift of robot evolution model. The theoretical formulation of the set-membership approach and the application to the estimation of the robot localization are addressed first. In order to meet more realistic conditions the way of reducing the effect of environment model inaccuracies, evolution model drift, outliers and disruptive events such as robot kidnapping is introduced. By integrating these additional treatments to the set membership approach we propose a bounded-error estimator using multihypothesis tracking. Simulation results show the contribution of each step of the estimator. Real experiments focus on global localization and specific treatments for synchronizing measurements and processing outliers.
Keywords: Robot mobile localization | Interval analysis | Bounded-error estimator | Outlier | Model inaccuracy and drift | Multi-hypothesis tracking