ردیف | عنوان | نوع |
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1 |
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 |
مقاله انگلیسی |
2 |
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 |
مقاله انگلیسی |
3 |
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 |
مقاله انگلیسی |