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Author(s):  
Rakesha Chandra Dash ◽  
Narayan Sharma ◽  
Dipak Kumar Maiti ◽  
Bhrigu Nath Singh

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.


Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7881
Author(s):  
Tatiana González Grandón ◽  
Fernando de Cuadra García ◽  
Ignacio Pérez-Arriaga

Renewable-powered “undergrid mini-grids” (UMGs) are instrumental for electrification in developing countries. An UMG can be installed under a—possibly unreliable— main grid to improve the local reliability or the main grid may “arrive” and connect to a previously isolated mini-grid. Minimising costs is key to reducing risks associated with UMG development. This article presents a novel market-logic strategy for the optimal operation of UMGs that can incorporate multiple types of controllable loads, customer smart curtailment based on reliability requirements, storage management, and exports to and imports from a main grid, which is subject to failure. The formulation results in a mixed-integer linear programming model (MILP) and assumes accurate predictions of the following uncertain parameters: grid spot prices, outages of the main grid, solar availability and demand profiles. An AC hybrid solar-battery-diesel UMG configuration from Nigeria is used as a case example, and numerical simulations are presented. The load-following (LF) and cycle-charging (CC) strategies are compared with our predictive strategy and HOMER Pro’s Predictive dispatch. Results prove the generality and adequacy of the market-logic dispatch model and help assess the relevance of outages of the main grid and of spot prices above the other uncertain input factors. Comparison results show that the proposed market-logic operation approach performs better in terms of cost minimisation, higher renewable fraction and lower diesel use with respect to the conventional LF and CC operating strategies.


2021 ◽  
Vol 6 (11) ◽  
pp. 158
Author(s):  
Filippo Landi ◽  
Francesca Marsili ◽  
Noemi Friedman ◽  
Pietro Croce

In civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters to be calibrated can be obtained; this approach is more informative than a deterministic local minimum point derived from a classical optimization problem. In addition, building a response surface surrogate model could allow one to overcome computational difficulties. Here, the general polynomial chaos expansion (gPCE) theory is adopted with this objective in mind. Owing to the fact that the ability of these methods to identify uncertain inputs depends on several factors linked to the model under investigation, as well as the experiment carried out, the understanding of results is not univocal, often leading to doubtful conclusions. In this paper, the performances and the limitations of three gPCE-based stochastic inverse methods are compared: the Markov Chain Monte Carlo (MCMC), the polynomial chaos expansion-based Kalman Filter (PCE-KF) and a method based on the minimum mean square error (MMSE). Each method is tested on a benchmark comprised of seven models: four analytical abstract models, a one-dimensional static model, a one-dimensional dynamic model and a finite element (FE) model. The benchmark allows the exploration of relevant aspects of problems usually encountered in civil, bridge and infrastructure engineering, highlighting how the degree of non-linearity of the model, the magnitude of the prior uncertainties, the number of random variables characterizing the model, the information content of measurements and the measurement error affect the performance of Bayesian updating. The intention of this paper is to highlight the capabilities and limitations of each method, as well as to promote their critical application to complex case studies in the wider field of smarter and more informed infrastructure systems.


Author(s):  
Tatiana Pogarskaia ◽  
Sergey Lupuleac ◽  
Julia Shinder ◽  
Philipp Westphal

Abstract Riveting and bolting are common assembly methods in aircraft production. The fasteners are installed immediately after hole drilling and fix the relative tangential displacements of the parts, that took place. A proper fastener sequence installation is very important because a wrong one can lead to a “bubble-effect”, when gap between parts after fastening becomes larger in some areas rather than being reduced. This circumstance affects the quality of the final assembly. For that reason, the efficient methods for determination of fastening sequence taking into account the specifics of the assembly process are needed. The problem is complicated by several aspects. First of all, it is a combinatorial problem with uncertain input data. Secondly, the assembly quality evaluation demands the time-consuming computations of the stress-strain state of the fastened parts caused by sequential installation of fasteners. Most commonly used strategies (heuristic methods, approximation algorithms) require a large number of computational iterations what dramatically complicates the problem. The paper presents the efficient methods of fastener sequence optimization based on greedy strategy and the specifics of the assembly process. Verification of the results by comparison to commonly used installation strategies shows its quality excellence.


Author(s):  
Hongyu Wu ◽  
Wendong Niu ◽  
Shuxin Wang ◽  
Shaoze Yan

In actual application, the energy utilization rate of underwater glider directly affects the total voyage range. When underwater glider is used for executing exploration mission for a fixed point, the position that the glider resurfaces should be accurate enough. In this paper, we employ a multi-objective optimization method to determine the control parameters values that can maximize the position accuracy that the glider resurfaces and the energy utilization rate simultaneously. Especially, the optimization of this paper considers the effect of uncertain input errors. The control parameters include the net buoyancy adjustment amount and the movable mass block translation amount. The input errors include the control parameters errors, the motion depth error and the current. Based on the dynamic model of an underwater glider, we propose the calculation model and evaluation flow that are used for analyzing the glider position accuracy and energy utilization rate, considering the effect of uncertain input errors. Besides, a combinatorial experimental design method is proposed to calculate the performance evaluation parameters under different control parameters values. Then the radial basis function neural network is employed to establish the surrogate models of performance evaluation parameters to participate in the optimization calculation, which can improve the optimization efficiency. After optimization calculation based on the non-dominated sorting genetic algorithm II, we obtain a Pareto optimal set consisting of 257 sets of non-dominated solutions. Finally, the selection rule of optimal control parameters values is given, and the optimization results are validated under 3 sets of solutions. This research may be valuable for the improvement of the glider work quality.


Author(s):  
Maxime Mulamba ◽  
Jayanta Mandi ◽  
Michelangelo Diligenti ◽  
Michele Lombardi ◽  
Victor Bucarey ◽  
...  

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost.


Open Mind ◽  
2021 ◽  
pp. 1-17
Author(s):  
Dario Paape ◽  
Shravan Vasishth ◽  
Ralf Engbert

Abstract Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis (The coach smiled at the player tossed a frisbee by the opposing team). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than under constant grammatical supervision (Tabor et al., 2004), or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input (Levy, 2008b). We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. In our data, local coherence affected on-line processing immediately at the point of the manipulation. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). We discuss implications for self-organized and noisy-channel models of local coherence.


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