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
LPV-MPC fault-tolerant energy management strategy for renewable microgrids
استراتژی مدیریت انرژی تحمل گسل LPV-MPC برای ریز شبکه قابل تجدید-2020
This paper presents a solution for the Fault-Tolerant Energy Management problem of renewable energy microgrids. This solution is a Energy Management System (EMS) derived from a Model Predictive Controller (MPC) synthesized upon a Linear Parameter Varying (LPV) prediction model. This model describes the energy-generation process in both healthy and faulty operation conditions. The MPC is tuned to adequately coordinate the operation of the microgrid, aiming to optimally use its energetic resources, enlarge the renewable generation share and guarantee maximal efficiency and profit, despite the presence of faults (or even failures) in its subsystems. The quantification of the level of faults in the energy system is provided by an extended-state LPV fault estimation observer that works in parallel to the MPC. The proposed EMS, that acts at an hourly rate, finds timevarying control policies, that are passed as energy-generation set-points for the lower-layer subsystems, with respect to operational constraints, internal demands and taking into account the future (estimation) behaviour of the renewables. To validate the proposed fault-tolerant control scheme, a realistic, high-fidelity case study from the Brazilian sugarcane industry is considered. The achieved simulation results assess the effectiveness and qualities of the proposed energy management strategy; an overall good behaviour is exhibited in both faulty and healthy energy-generation conditions.
Keywords: Fault-tolerant control | Fault estimation | Linear parameter varying | Model predictive control | Microgrids
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