Quantifying the uncertainty introduced by discretization and time‐averaging in two‐fluid model predictions

The two‐fluid model (TFM) has become a tool for the design and troubleshooting of industrial fluidized bed reactors. To use TFM for scale up with confidence, the uncertainty in its predictions must be quantified. Here, we study two sources of uncertainty: discretization and time‐averaging. First, we show that successive grid refinement may not yield grid‐independent transient quantities, including cross‐section–averaged quantities. Successive grid refinement would yield grid‐independent time‐averaged quantities on sufficiently fine grids. Then a Richardson extrapolation can be used to estimate the discretization error, and the grid convergence index gives an estimate of the uncertainty. Richardson extrapolation may not work for industrial‐scale simulations that use coarse grids. We present an alternative method for coarse grids and assess its ability to estimate the discretization error. Second, we assess two methods (autocorrelation and binning) and find that the autocorrelation method is more reliable for estimating the uncertainty introduced by time‐averaging TFM data. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5343–5360, 2017     

Interval force/position modeling and control of a microgripper composed of two collaborative piezoelectric actuators and its automation

This paper deals with the modeling and control of a microgripper devoted to micromanipulation and microassembly applications and tasks. Based on two collaborative piezoelectric actuators, the microgripper is typified by a high sensitivity to the environment, in particular a high sensitivity to the properties of the manipulated objects. This sensitivity makes the behavior of the microgripper variable and uncertain versus the environment and consequently makes the tasks lose performances. A possible way to overstep that problem is to model the microgripper behavior and its dependency with the environment as perfect as possible and then calculate a controller from this. However, such model is complex to handle and the yielded controllers are often very complex for implementation. In this paper, we propose to use interval models to describe the behavior of the piezoelectric actuators that compose the microgripper. Then a controllers synthesis consisting in combining interval techniques and classical control theory is proposed. Both the position and the force raised in the microgripper are considered. The main advantages of the proposed technique are: 1) ease and natural way to model the uncertainties, 2) the robustness of the synthesized controllers, 3) and the derivation of low order controllers that are easier for implementation relative to those of classical robust control techniques. Finally, the paper presents the application of the controlled microgripper to an automated pick-transport-andplace task of micro-objects. This automated task demonstrates the efficiency of the control technique in micromanipulation and microassembly applications.     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

Toward a higher order unsteady finite volume solver based on reproducing kernel methods

During the last decades, research efforts are headed to develop high order methods on CFD and CAA to reach most industrial applications (complex geometries) which need, in most cases, unstructured grids. Today, higher-order methods dealing with unstructured grids remain in infancy state and they are still far from the maturity of structured grids-based methods when solving unsteady cases. From this point of view, the development of higher order methods for unstructured grids become indispensable. The finite volume method seems to be a good candidate, but unfortunately it is difficult to achieve space flux derivation schemes with very high order of accuracy for unsteady cases. In this paper we propose, a high order finite volume method based on Moving Least Squares approximations for unstructured grids that is able to reach an arbitrary order of accuracy on unsteady cases. In order to ensure high orders of accuracy, two strategies were explored independently: (1) a zero-mean variables reconstruction to enforce the mean order at the time derivative and (2) a pseudo mass matrix formulation to preserve the residuals order.     

Numerical simulation of the transient hydrogen trapping process using an analytical approximation of the McNabb and Foster equation

In order to simulate hydrogen charging and discharging cycles of mechanically loaded structures, an analytical solution for the differential equation of trapping kinetics is proposed, as a generalization of the Oriani's equilibrium relationship.This solution has been implemented in the Abaqus finite element software, and validated by comparison with the one-dimensional kinetic MRE Hydrogen Isotope Inventory Processes Code (HIIPC). Last, the results of an application on a 3D structure are presented.     

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.     

Harnessing knowledge workers' participation for IT planning effectiveness: the informational and motivational mediating effects of users' microplanning behaviour

In most organizations, knowledge is produced on a continuous basis such that formal planning methods fail to capture it on time for utilization into effective IT solutions. Microplanning, as user-led informal planning behaviour, allows users as knowledge workers to continuously use their knowledge and skills to identify opportunities for using IT or replanning its existing use. This is the informational effect of microplanning. Microplanning is also an empowering process which enhances users' own motivation. This empowerment effect is referred to as the motivational effect. User microplanning behaviour therefore constitutes the main construct of the model of microplanning effectiveness described in this study. It yields effectiveness through two paths, informational and motivational. After operational definitions for the variables were derived, the two main hypotheses of the study were empirically tested on a sample of 263 knowledge workers. Results of the study fully support the informational and motivational effects of microplanning.     

3D model for powder compact densification in rotational molding

During rotational molding, a loosely packed, low‐density powder compact transforms into a fully densified polymer part. This transformation is a consequence of particles sintering. Powder compact density evolution of the polymer powder is measured experimentally. Obtained results show that the powder densification process consists of two stages, and its mechanism during these two stages is not the same. During the first stage, densification occurs by grains coalescence, and air between the grains escape by open pores between particles. These open pores close in time by particles coalescence progress, and remaining air entrapped in polymer melt becomes air bubbles. Surface tension, viscosity, grains size, and temperature are the controlling parameters during first stage. A three‐dimensional model is proposed for the densification of polymer powder during first stage. Second stage starts after bubble forming. Diffusion is the controlling phenomena during this stage. A diffusion‐based model is used for the second stage of densification. By comparing with the other models, proposed model exhibits several advantages: it is proposed in three‐dimensional and takes into account the nature of layer‐by‐layer powder densification. Model verification by experimental data obtained for densification of two different polymers shows a close agreement between model prediction and experiments. POLYM. ENG. SCI., 52:2033–2040, 2012. © 2012 Society of Plastics Engineers     

Influence of process parameters on thermal and mechanical properties of polylactic acid fabricated by fused filament fabrication

Fused filament fabrication is considered one of the most used processes in additive manufacturing rapid prototypes out of polymeric material. Poor strength of the deposited layers is still one of the main critical problems in this process, which affects the mechanical properties of the final parts. To improve the mechanical strength, investigation into various process parameters must be considered. In this article, the influence of different process parameters has been experimentally investigated by means of physicochemical and mechanical characterizations. Special attention was given to the thermal aspect. In that respect, the in situ measurement of temperature profile during deposition indicated that several parameters affect the cooling rate of material and consequently have an influence on the final parts. It was found that the influence of increasing the extruder temperature is more significant in comparison with other process parameters.     

Accurate,dense and shading-aware multi-view stereo reconstruction using metaheuritic optimization

Among the modern means of 3D geometry creation that exist in the literature, there are the Multi-View Stereo (MVS) reconstruction methods that received much attention from the research community and the multimedia industry. In fact, several methods showed that it is possible to recover geometry only from images with reconstruction accuracies paralleling that of excessively expensive laser scanners. The majority of these methods perform on images such as online community photo collection and estimate the surface position with its orientation by minimizing a matching cost function defined over a small local region. However, these datasets not only they are large but also contain more challenging scenes setups with different photometric effects; therefore fine-grained details of an object’s surface cannot be captured. This paper presents a robust multi-view stereo method based on metaheuristic optimization namely the Particle Swarm Optimization (PSO) in order to find the optimal depth, orientation, and surface roughness. To deal with the various shading and stereo mismatch problems caused by rough surfaces, shadows, and interreflections, we propose to use a robust matching/energy function which is a combination of two similarity measurements. Finally, our method computes individual depth maps that can be merged into compelling scene reconstructions. The proposed method is evaluated quantitatively using well-known Middlebury datasets and the obtained results show a high completeness score and comparable accuracy to those of the current top performing algorithms.