Smart operational load monitoring using decision trees and artificial neural networks: a comparative study

Operational Load Monitoring is an industrial process that allows to predict the remaining in-service life of a mechanical structure under variable loads. Data from sensors embedded or mounted on the structure is acquired and allows to estimate the number and amplitude of load cycles that the structure has withstood so far in its working environment. This process is especially important in the aerospace industry where mechanical structures of an aircraft are monitored in order to maximize their operating lifetime. Smart Operational Load Monitoring means implementation of artificial intelligence techniques to the process in order to make predictions based on measurements from reduced number of sensors. In this paper a composite lightweight structure of typical geometry used in aircraft structures is taken as an example for Smart Operational Load Monitoring. The predictions are made from measurements from six strain gauges mounted to the structure, using carefully prepared artificial intelligence-based models. Efficiency of the models is compared, in terms of their prediction accuracies and computational complexities.National Agency for Academic Exchange of PolandSilesian University of Technology. Faculty of Mechanical Engineerin

Constraints on Cosmological Parameters from Future Galaxy Cluster Surveys

We study the expected redshift evolution of galaxy cluster abundance between 0 < z < 3 in different cosmologies, including the effects of the cosmic equation of state parameter w=p/rho. Using the halo mass function obtained in recent large scale numerical simulations, we model the expected cluster yields in a 12 deg^2 Sunyaev-Zeldovich Effect (SZE) survey and a deep 10^4 deg^2 X-ray survey over a wide range of cosmological parameters. We quantify the statistical differences among cosmologies using both the total number and redshift distribution of clusters. Provided that the local cluster abundance is known to a few percent accuracy, we find only mild degeneracies between w and either Omega_m or h. As a result, both surveys will provide improved constraints on Omega_m and w. The Omega_m-w degeneracy from both surveys is complementary to those found either in studies of CMB anisotropies or of high-redshift Supernovae (SNe). As a result, combining these surveys together with either CMB or SNe studies can reduce the statistical uncertainty on both w and Omega_m to levels below what could be obtained by combining only the latter two data sets. Our results indicate a formal statistical uncertainty of about 3% (68% confidence) on both Omega_m and w when the SZE survey is combined with either the CMB or SN data; the large number of clusters in the X-ray survey further suppresses the degeneracy between w and both Omega_m and h. Systematics and internal evolution of cluster structure at the present pose uncertainties above these levels. We briefly discuss and quantify the relevant systematic errors. By focusing on clusters with measured temperatures in the X-ray survey, we reduce our sensitivity to systematics such as non-standard evolution of internal cluster structure.Comment: ApJ, revised version. Expanded discussion of systematics; Press-Schechter mass function replaced by fit from simulation

The Angular Power Spectrum of EDSGC Galaxies

We determine the angular power spectrum, C_l, of the Edinburgh/Durham Southern Galaxy Catalog (EDSGC) and use this statistic to constrain cosmological parameters. Our methods for determining C_l, and the parameters that affect it are based on those developed for the analysis of cosmic microwave background maps. We expect them to be useful for future surveys. Assuming flat cold dark matter models with a cosmological constant (constrained by COBE/DMR and local cluster abundances), and a scale--independent bias, b, we find good fits to the EDSGC angular power spectrum with 1.11 < b < 2.35 and 0.2 < Omega_m < 0.55 at 95% confidence. These results are not significantly affected by the ``integral constraint'' or extinction by interstellar dust, but may be by our assumption of Gaussianity.Comment: 11 pages, 9 figures, version to appear in Ap

Operational load monitoring of a composite panel using artificial neural networks

Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.The project and publication of this article were financed by the Polish National Agency for Academic Exchange (project number: PPI/APM/2018/1/00004) in the framework of Academic International Partnerships program

New operational load monitoring approach using digital image correlation and image classification networks

The following paper presents a novel approach that can be applied to Operational Load Monitoring and Structural Health Monitoring processes. The approach is based on artificial intelligence (AI) and digital image correlation (DIC) techniques. DIC is an optical method that allows measuring full-field structural displacements and strains. In the presented approach only a relatively small fragment of the material's surface is monitored by DIC. The obtained partial image of strains or displacements is then processed by a carefully trained AI model, an image classification network, able to predict the state of whole structure (e.g. materials stresses, potential loss of material continuity). The assumption is that all possible load cases and states of the monitored structure can be identified and simulated, so the data obtained from simulations can then be used to train the image classification network. A numerical example is presented as proof of the presented concept. A modern lightweight aerostructure in the form of a hat-stiffened composite panel was used as monitored structure in the presented example and its Operational Load Monitoring was performed based on a relatively small fragment of normal strains map. The reference maps to train the network were simulated numerically. The prediction model estimates the Tsai-Wu failure criterion value for the whole composite material. The obtained accuracy of predictions proved the effectiveness and efficiency of the proposed approach.- (undefined

Comparison of numerical and experimental strains distributions in composite panel for aerospace applications

In structural applications of aerospace industry, weight efficiency, understood as minimal weight and maximal stiffness, is of great importance. This criterion can be achieved by composite lightweight structures. Typical structures for aforementioned applications are sandwich panels (e.g., with honeycomb core) and stiffened panels (e.g., with blade ribs, T-bar ribs, or hat ribs). In this paper, a hat-stiffened panel, made of carbon/epoxy woven composite, is considered. Results of experiments, consisting of loading the panel and measuring exciting forces and strains (using strain gages), are presented. The results are compared to strains distribution obtained from finite element model of the panel.The research was partially funded from financial resources from the statutory subsidy of the Faculty of Mechanical Engineering, Silesian University of Technology, in 2021. W.M. acknowledges the National Agency for Academic Exchange of Poland (under the Academic International Partnerships program, grant agreement PPI/APM/2018/1/00004) for supporting training in the University of Minho, which enabled execution of the study

Next Generation Cosmology: Constraints from the Euclid Galaxy Cluster Survey

We study the characteristics of the galaxy cluster samples expected from the European Space Agency's Euclid satellite and forecast constraints on cosmological parameters describing a variety of cosmological models. The method used in this paper, based on the Fisher Matrix approach, is the same one used to provide the constraints presented in the Euclid Red Book (Laureijs et al.2011). We describe the analytical approach to compute the selection function of the photometric and spectroscopic cluster surveys. Based on the photometric selection function, we forecast the constraints on a number of cosmological parameter sets corresponding to different extensions of the standard LambdaCDM model. The dynamical evolution of dark energy will be constrained to Delta w_0=0.03 and Delta w_a=0.2 with free curvature Omega_k, resulting in a (w_0,w_a) Figure of Merit (FoM) of 291. Including the Planck CMB covariance matrix improves the constraints to Delta w_0=0.02, Delta w_a=0.07 and a FoM=802. The amplitude of primordial non-Gaussianity, parametrised by f_NL, will be constrained to \Delta f_NL ~ 6.6 for the local shape scenario, from Euclid clusters alone. Using only Euclid clusters, the growth factor parameter \gamma, which signals deviations from GR, will be constrained to Delta \gamma=0.02, and the neutrino density parameter to Delta Omega_\nu=0.0013 (or Delta \sum m_\nu=0.01). We emphasise that knowledge of the observable--mass scaling relation will be crucial to constrain cosmological parameters from a cluster catalogue. The Euclid mission will have a clear advantage in this respect, thanks to its imaging and spectroscopic capabilities that will enable internal mass calibration from weak lensing and the dynamics of cluster galaxies. This information will be further complemented by wide-area multi-wavelength external cluster surveys that will already be available when Euclid flies. [Abridged]Comment: submitted to MNRA

Mould redesign and analysis for the production of a micro-accelerometer

In this paper we present an alternative fabrication method based on polymeric materials and technologies for three-axis thermal accelerometers. The device is composed by four microinjected parts forming an external structure responsible for the coupling and sealing of a polymeric membrane. The membrane contains and protects the heater and thermoresistors and is fabricated by microtechnologies. The fabrication process was successful although some issues were noticed in the mould during the microinjection process. Regarding the ejection side, a redesign was done to first assure the locking of the micro-parts on the movable side of the mould and second to improve the extraction of the parts avoiding its deformation. Overheating of the mould and polymer freezing on the injection nozzle were the main issues found on the injection side of the mould. Three different injection nozzle designs and two different fabrication materials were analyzed and simulated. The results show that an intermediate injection nozzle design using Ampcoloy 940® as construction material can improve the maintenance of the established temperature values for both mould and injection nozzle

Avaliação de doenças em arroz irrigado por aspersão em Parnaíba.

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