In modelling laser-induced plasma plume formation, the proper description of laser absorption in the plasma plays an important role. In the present model, absorption is described by means of three ...different mechanisms: inverse bremsstrahlung (IB), photoionization (PI) and absorption by small condensed clusters. Numerical solutions of the model are given for KrF laser beam irradiation (wavelength
λ
=
248
nm) impinging on a nickel target at various fluences. The influence of particular absorption mechanisms on the absorbed laser beam energy in the plasma plume during the pulse is shown for different fluences. Using all three absorption mechanisms, the calculated plasma properties show good agreement with the experimental results of other authors.
•We propose a novel annular laser beam based system for metal droplet generation.•We propose and examine a process of drop on demand generation.•We define the relations between the process parameters ...and droplet characteristics.•High melting point drops with preset diameter and temperature can be generated.•We demonstrate process capability by deposition of Ni droplets on Ti substrate.
In the paper a novel system for drop-on-demand (DoD) generation from a metal wire is presented, whose main component is a newly developed laser droplet generation head, consisting of annular laser beam shaping optics and a wire feeding system. In the pendant droplet formation phase of the DoD generation, a laser pulse is used to melt the wire-end, which is fed into the focus of an annular laser beam. The formed pendant droplet is then detached by means of a detachment pulse, which induces Rayleigh–Plateau instability of the molten column of wire above the neck of the pendant droplet. The main process parameters, including the laser pulse and wire feeding parameters as well as the additional parameters which influence particular phases of the DoD generation process, have been identified. The empirical correlations between the influencing process parameters and the droplet characteristics, including droplet diameter and temperature, were determined, based on the analysis of high speed IR records of the process, images being acquired by an optical microscope and temperature data being acquired by pyrometers. As an example, DoD generation from a commercially pure 99.6% Ni wire (Nickel 200) of 0.6mm diameter is considered. It is shown that droplets with diameters ranging from 0.85 to 1.25mm can be generated, with a resolution of 50μm and a standard deviation of 15μm. The temperature of the detached droplet remains above the melting point of the Ni wire, and increases with the droplet diameter within the range from 1650 to 1750°C. Some examples of Ni droplets deposited on a Ti sheet surface are presented, with the aim of demonstrating the capability of the proposed system, and motivating further applications in which drops on demand having a high temperature and a precisely defined diameter need to be generated, while limiting the thermal loading of the surroundings.
Pendant droplet detachment regimes in the novel annular laser beam droplet generation from a metal wire are analyzed. In drop-on-demand generation, droplet detachment can be achieved via ...Rayleigh–Plateau instability based molten wire column break-up. Detachment dynamics are influenced by the distance between the annular laser beam focus and the pendant droplet neck. In the continuous generation of a droplet sequence, droplet detachment is governed by the laser pulse frequency, resulting in a spontaneous, resonant, or Rayleigh–Plateau instability based detachment regime. In addition to drop-on-demand generation, continuous droplet generation with spontaneous and mass-spring resonant detachment are suitable for metal droplet based engineering applications where accurate droplet diameter and deposition position are required.
•Traffic dynamics on a ring road-based transportation network is considered.•Traffic dynamics is being analyzed based on real traffic flow data.•For the analysis, nonlinear methods of time series ...analysis are used.•Time series are tested for chaos by means of the novel 0–1 test for chaos.•Various traffic dynamics are distinguished on the basis of Lyapunov spectra.
This paper considers the dynamics of traffic on a ring road-based transportation network around a major city, via traffic flow time series analysis and characterization. In particular, three traffic flow time series are examined. Two of the time series are acquired from measurement stations located on highways, while one is from a station on the ring road around Ljubljana city. For the analysis and characterization of time series the novel test called 0–1 test for chaos is applied. Based on the outputs of the test it is concluded that the observed traffic dynamics is inherently chaotic. Additionally, a more detailed characterization of traffic dynamics is carried out on Lyapunov spectrum basis, which reveals that traffic dynamics on the highway is quantitatively quite different from the traffic dynamics on the ring road.
Health monitoring systems for plastic based structures require the capability of real time tracking of changes in response to the time-dependent behavior of polymer based structures. The paper ...proposes artificial neural networks as a tool of solving inverse problem appearing within time-dependent material characterization, since the conventional methods are computationally demanding and cannot operate in the real time mode. Abilities of a Multilayer Perceptron (MLP) and a Radial Basis Function Neural Network (RBFN) to solve ill-posed inverse problems on an example of determination of a time-dependent relaxation modulus curve segment from constant strain rate tensile test data are investigated. The required modeling data composed of strain rate, tensile and related relaxation modulus were generated using existing closed-form solution. Several neural networks topologies were tested with respect to the structure of input data, and their performance was compared to an exponential fitting technique. Selected optimal topologies of MLP and RBFN were tested for generalization and robustness on noisy data; performance of all the modeling methods with respect to the number of data points in the input vector was analyzed as well. It was shown that MLP and RBFN are capable of solving inverse problems related to the determination of a time dependent relaxation modulus curve segment. Particular topologies demonstrate good generalization and robustness capabilities, where the topology of RBFN with data provided in parallel proved to be superior compared to other methods.
•Comparisons of static and adaptive models for natural gas consumption forecasting.•Comparisons of linear and nonlinear forecasting models (ARX, neural networks, SVM).•Relevant inputs to forecasting ...models are determined by stepwise regression.•Methods are applied to local distribution company and individual house data sets.•The best results are obtained by using linear adaptive forecasting models.
In this paper the performance of static and adaptive models for short-term natural gas load forecasting has been investigated. The study is based on two sets of data, i.e. natural gas consumption data for an individual model house, and natural gas consumption data for a local distribution company. Various forecasting models including linear models, neural network models, and support vector regression models, were constructed for the one day ahead forecasting of natural gas demand. The models were examined in their static versions, and in adaptive versions. A cross-validation approach was applied in order to estimate the generalization performance of the examined forecasting models. Compared to the static model performance, the results confirmed the significantly improved forecasting performance of adaptive models in the case of the local distribution company, whereas, as was expected, the forecasts made in the case of the individual house were not improved by the adaptive models, due to the stationary regime of the latter’s heating. The results also revealed that nonlinear models do not outperform linear models in terms of generalization performance. In summary, if the relevant inputs are properly selected, adaptive linear models are recommended for applications in daily natural gas consumption forecasting.
In this paper, the possibilities of developing machine learning based data-driven models for the short-term prediction of indoor temperature within prediction horizons ranging from 1 hour up to 12 ...hours are systematically investigated. The study was based on a TRNSYS emulation of a residential building heated by a heat pump, combined with measured weather data for a typical winter season in Ljubljana, Slovenia. Autoregressive models with exogenous inputs (ARX), neural network models (NN), and extreme learning machine models (ELM) are considered. The results confirm the finding that nonlinear models, particularly the NN model trained by regularization, consistently outperform linear models in both fitting and generalization performance, so they are the recommended choice as predictive models. The availability of future weather data considerably improved the predictive performance of all the tested models. Besides data about the future outdoor temperature, also data about future expected solar radiation significantly improve predictions of temperature in buildings. The linear models required embedding dimensions of 24 hours for accurate predictions, whereas the nonlinear models were not very sensitive to the use of past data. Nonlinear models required about three months of training data to reach good predictive performance, whereas the linear models converged to accurate predictions within six weeks. The RMSE prediction errors, averaged over all the data sets and all the prediction horizons, are within the range between 0.155 °C for the linear ARX model (in the case of no future available weather data), and 0.065 °C for the neural network model (in the case of available future weather data).
•Characterization of stress corrosion cracking (SCC) by acoustic emission (AE).•Interpretation of SCC related AE with additional methods.•Manual and automatic methods for the SCC related AE signal ...detection are presented.•AE time and power spectra features for the automatic detection of SCC are proposed.•Estimation of the size of ductile fracture area based on energy of detected AE bursts.
In the paper the results of the acoustic emission (AE) based detection and characterization of stress-corrosion cracking (SCC) in stainless steel are presented. As supportive methods for AE interpretation, electrochemical noise, specimen elongation measurements, and digital imaging of the specimen surface were used. Based on the defined qualitative and quantitative time and power spectra characteristics of the AE bursts, a manual and an automatic procedure for the detection of crack related AE bursts were introduced. The results of the analysis of the crack related AE bursts indicate that the AE method is capable of detecting large scale cracks, where, apart from intergranular crack propagation, also some small ductile fractures occur. The sizes of the corresponding ductile fracture areas can be estimated based on a relative comparison of the energies of the detected AE bursts. It has also been shown that AE burst time and power spectra features can be successfully used for the automatic detection of SCC.
The paper presents recurrence plot based stability analysis of the horizontal band sawing process of structural steel profiles. The analysis is performed in the parameter space defined by the cutting ...speed, the distance between the blade supports, and the feed rate. The corresponding stability diagrams have been constructed using the recurrence plot characteristic, the determinism of the sound pressure emitted by the process, which quantifies the process predictability. The topology of the experimentally obtained stability diagrams revealed non-linear non-monotonic dynamic behaviour, which made two different chatter avoidance strategies possible by cutting speed variation.
The paper provides a description of an annular laser beam based direct wire deposition head, which can be used to perform controllable, simultaneous, and symmetrical heating of both the workpiece ...surface and the axially fed wire. In order to characterize the proportion of energy delivered to the workpiece and fed wire, respectively, a new process parameter, referred to as the workpiece illumination proportion, has been introduced, which is defined as the percentage of the annular laser beam power incident on the workpiece surface. The influence of this newly defined illumination parameter on the process outcome was characterized, based on single-layer deposition experiments using a 0.6 mm diameter nickel wire which was fed onto a stainless steel workpiece. The results show that the geometry of the deposited layer is influenced, as well as by the conventional process parameters, also by the newly introduced workpiece illumination parameter, which also affects the process stability and its robustness.
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