MATLAB Micro-Symposium

Join us to learn how your colleagues and peers are crossing boundaries and using MATLAB in their work in areas such as chemistry, biology, pharmacy and bioengineering! We will have four faculty speakers who will share their MATLAB uses in teaching, projects and research. This will be followed by a coffee break that includes:Poster session with faculty and student projects using MATLABThe opportunity to talk to the MathWorks team about the tools they provide – learn about what’s new and ask your questions here!An opportunity to network with your fellow Pacific colleagues from other departments! MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include:Math and computationAlgorithm developmentModeling, simulation, and prototypingData analysis, exploration, and visualizationScientific and engineering graphicsApplication developmen

Evaluating the robustness of objective pilling classification with the two-dimensional discrete wavelet transform

Previously, we proposed a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample fabric images. We have further evaluated this method, and our results indicate that it is robust to small horizontal and/or vertical translations and to significant variations in the brightness of the image under analysis, and is sensitive to rotation and to dilation of the image. These results suggest that as long as precautions are taken to ensure fabric test samples are imaged under consistent conditions of weave/knit pattern alignment (rotation) and apparent interyarn pitch (dilation), the method will yield repeatable results. <br /

On-the-fly laser machining: a case study for in situ balancing of rotative parts

On-the-fly laser machining is defined as a process that aims to generate pockets/patches on target components that are rotated or moved at a constant velocity. Since it is a nonintegrated process (i.e., linear/rotary stage system moving the part is independent of that of the laser), it can be deployed to/into large industrial installations to perform in situ machining, i.e., without the need of disassembly. This allows a high degree of flexibility in its applications (e.g., balancing) and can result in significant cost savings for the user (e.g., no dis(assembly) cost). This paper introduces the concept of on-the-fly laser machining encompassing models for generating user-defined ablated features as well as error budgeting to understand the sources of errors on this highly dynamic process. Additionally, the paper presents laser pulse placement strategies aimed at increasing the surface finish of the targeted component by reducing the area surface roughness that are possible for on-the-fly laser machining. The overall concept was validated by balancing a rotor system through ablation of different pocket shapes by the use of a Yb:YAG pulsed fiber laser. In this respect, first, two different laser pulse placement strategies (square and hexagonal) were introduced in this research and have been validated on Inconel 718 target material; thus, it was concluded that hexagonal pulse placement reduces surface roughness by up to 17% compared to the traditional square laser pulse placement. The concept of on-the-fly laser machining has been validated by ablating two different features (4 × 60 mm and 12 × 4 mm) on a rotative target part at constant speed (100 rpm and 86 rpm) with the scope of being balanced. The mass removal of the ablated features to enable online balancing has been achieved within < 4 mg of the predicted value. Additionally, the error modeling revealed that most of the uncertainties in the dimensions of the feature/pocket originate from the stability of the rotor speed, which led to the conclusion that for the same mass of material to be removed it is advisable to ablate features (pockets) with longer circumferential dimensions, i.e., stretched and shallower pockets rather than compact and deep

Aerodynamic and Aeroacoustic Performance of Small UAV Propellers in Static Conditions

The proliferation of small multi-rotor UAVs in commercial, recreational, and surveillance spheres has garnered significant interest in the noise produced by these vehicles. The current research aims to study the relationship between the aerodynamic performance and acoustic characteristics of small-scale UAV propellers. Three commercially available propellers for the DJI Phantom 2/3 UAV were selected for preliminary development and validation of an aeroacoustic experimental test setup and associated data reduction methods. Propeller thrust, torque, and power measurements were recorded at static conditions. Upon successful validation of the test bench, acoustic measurements were taken at the propeller disk’s upstream and in-plane locations. The power spectral density of these acoustic signals was estimated using the modified periodogram (Welch’s) method to identify frequency content and calculate sound pressure levels (SPLs) at each of the observation locations. Additionally, time-frequency analysis verified the periodogram results and identified possible sources of transient noise at static thrust. These methods found the nonrotor noise to be a major contributor to the SPL at higher frequencies and the propeller noise dominating the SPL spectra at the lower frequencies. Experimental thrust, torque, power, and sound pressure level (SPL) data were then compared for each propeller to identify relationships between aerodynamic performance and acoustic characteristics with variations in propeller geometry and blade loading

Optimisation of material properties for the modelling of large deformation manufacturing processes using a finite element model of the Gleeble compression test

The finite element modelling of manufacturing processes often requires a large amount of large plastic strain flow stress data in order to represent the material of interest over a wide range of temperatures and strain rates. Compression data generated using a Gleeble thermo-mechanical simulator is difficult to interpret due to the complex temperature and strain fields, which exist within the specimen during the test. In this study, a non-linear optimisation process is presented, which includes a finite element model of the compression process to accurately determine the constants of a five-parameter Norton–Hoff material model. The optimisation process is first verified using a reduced three-parameter model and then the full five-parameter model using a known set of constants to produce the target data, from which the errors are assessed. Following this, the optimisation is performed using experimental target data starting from a set of constants derived from the test data using an initial least-squares fit and also an arbitrary starting point within the parameter space. The results of these tests yield coefficients differing by a maximum of less than 10% and significantly improve the representation of the flow stress of the material

Image based simulation of one-dimensional compression tests on carbonate sand

High factors of safety and conservative methods are commonly used on foundation design on shelly carbonate soils. A better understanding of the behavior of this material is, thus, critical for more sustainable approaches for the design of a number of offshore structures and submarine pipelines. In particular, understanding the physical phenomena taking place at the microscale has the potential to spur the development of robust computational methods. In this study, a one-dimensional compression test was performed inside an X-ray scanner to obtain 3D images of the evolving internal structure of a shelly carbonate sand. A preliminary inspection of the images through five loading increments has shown that the grains rearrange under loading and in some cases cracks develop at the contacts. In order to replicate of the experiments in the numerical domain, the 3D image of the soil prior to loading was imported into a micro Finite Element (µFE) framework. This image-based modelling tool enables measurements of the contact force and stress map inside the grains while making use of the real microstructure of the soil. The potential of the µFE model to contribute insights into yield initiation within the grain is demonstrated here. This is of particular interest to better understand the breakage of shelly grains underpinning their highly compressive behavior

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

Modeling drying kinetics of thyme (thymus vulgaris l.): theoretical and empirical models, and neural networks

[EN] The drying kinetics of thyme was analyzed by considering different conditions: air temperature of between 40 C and 70 C, and air velocity of 1 m/s. A theoretical diffusion model and eight different empirical models were fitted to the experimental data. From the theoretical model application, the effective diffusivity per unit area of the thyme was estimated (between 3.68 10 5 and 2.12 10 4 s 1). The temperature dependence of the effective diffusivity was described by the Arrhenius relationship with activation energy of 49.42 kJ/mol. Eight different empirical models were fitted to the experimental data. Additionally, the dependence of the parameters of each model on the drying temperature was determined, obtaining equations that allow estimating the evolution of the moisture content at any temperature in the established range. Furthermore, artificial neural networks were developed and compared with the theoretical and empirical models using the percentage of the relative errors and the explained variance. The artificial neural networks were found to be more accurate predictors of moisture evolution with VAR 99.3% and ER 8.7%.The authors acknowledge the financial support from the 'Ministerio de Educacion y Ciencia' in Spain, CONSOLIDER INGENIO 2010 (CSD2007-00016).Rodríguez Cortina, J.; Clemente Polo, G.; Sanjuán Pellicer, MN.; Bon Corbín, J. (2014). Modeling drying kinetics of thyme (thymus vulgaris l.): theoretical and empirical models, and neural networks. Food Science and Technology International. 20(1):13-22. https://doi.org/10.1177/1082013212469614S132220

Defects and boundary layers in non-Euclidean plates

We investigate the behavior of non-Euclidean plates with constant negative Gaussian curvature using the F\"oppl-von K\'arm\'an reduced theory of elasticity. Motivated by recent experimental results, we focus on annuli with a periodic profile. We prove rigorous upper and lower bounds for the elastic energy that scales like the thickness squared. In particular we show that are only two types of global minimizers -- deformations that remain flat and saddle shaped deformations with isolated regions of stretching near the edge of the annulus. We also show that there exist local minimizers with a periodic profile that have additional boundary layers near their lines of inflection. These additional boundary layers are a new phenomenon in thin elastic sheets and are necessary to regularize jump discontinuities in the azimuthal curvature across lines of inflection. We rigorously derive scaling laws for the width of these boundary layers as a function of the thickness of the sheet

Object knowledge modulates colour appearance

We investigated the memory colour effect for colour diagnostic artificial objects. Since knowledge about these objects and their colours has been learned in everyday life, these stimuli allow the investigation of the influence of acquired object knowledge on colour appearance. These investigations are relevant for questions about how object and colour information in high-level vision interact as well as for research about the influence of learning and experience on perception in general. In order to identify suitable artificial objects, we developed a reaction time paradigm that measures (subjective) colour diagnosticity. In the main experiment, participants adjusted sixteen such objects to their typical colour as well as to grey. If the achromatic object appears in its typical colour, then participants should adjust it to the opponent colour in order to subjectively perceive it as grey. We found that knowledge about the typical colour influences the colour appearance of artificial objects. This effect was particularly strong along the daylight axis