Changes in modal parameters of a steel truss bridge due to artificial damage

This study investigated how damage changes the modal parameters of a real bridge by means of a field experiment which was conducted on a real steel truss bridge consecutively subjected to four artificial damage scenarios. In the experiment, both the forced and free vibrations of the bridge were recorded, the former for identifying higher modes available exclusively and the latter for lower modes with higher resolution. Results show that modal parameters are little affected by damage causing low stress redistribution. Modal frequencies decrease as damage causing high stress redistribution is applied; such a change can be observed if the damage is at the non-nodal point of the corresponding mode shape. Mode shapes are distorted due to asymmetric damage; they show an amplification in the damaged side as damage is applied at the non-nodal point. Torsion modes become more dominant as damage is applied either asymmetrically or on an element against large design loads. © 2013 Taylor & Francis Group, London.

Identification of the Dynamic Parameters of a Real Bridge under Different Damage Conditions in a Field Experiment

This paper presents the results of a real bridge field experiment in which damage was applied artificially to a steel truss bridge. The aim of this paper is to identify the dynamic parameters of this bridge using conventional techniques and investigate the effect of various damage conditions on those parameters. In the field experiment, acceleration measurements were recorded at a number of locations on the bridge deck. To excite the bridge, a two-axle van was driven across the bridge at constant speed. Dynamic parameters, such as the bridge mode shape, natural frequency and damping constant, are identified from the acceleration signals using existing techniques such as the fast Fourier transform, logarithmic decrement and frequency domain decomposition. The variation of these parameters under the influence of artificially applied damage conditions is investigated in order to evaluate their sensitivity to the bridge damage.

Theoretical investigation of the use of a moving vehicle to identify bridge dynamic parameters

Pavements and bridges are subject to a continuous degradation due to traffic aggressiveness, ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure in order to provide adequate maintenance and guarantee the required levels of transport service and safety. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamics of bridges. A simplified quarter carbridge interaction model is used in theoretical simulations and the natural frequency of the bridge is extracted from the spectra of the vehicle accelerations. The accuracy is better at lower speeds and for smooth road profiles. The structural damping of the bridge was also monitored for smooth and rough road profiles. The magnitude of peaks in the power spectral density of the vehicle accelerations decreased with increasing bridge damping and this decrease was easier to detect the smoother the road profile.

Tidally Distorted Exoplanets: Density Corrections for Short-period Hot-Jupiters Based Solely on Observable Parameters

The close proximity of short-period hot-Jupiters to their parent star means they are subject to extreme tidal forces. This has a profound effect on their structure and, as a result, density measurements that assume that the planet is spherical can be incorrect. We have simulated the tidally distorted surface for 34 known short-period hot-Jupiters, assuming surfaces of constant gravitational equipotential for the planet, and the resulting densities have been calculated based only on observed parameters of the exoplanet systems. Comparing these results to the density values, assuming the planets are spherical, shows that there is an appreciable change in the measured density for planets with very short periods (typically less than two days). For one of the shortest-period systems, WASP-19b, we determine a decrease in bulk density of 12% from the spherical case and, for the majority of systems in this study, this value is in the range of 1%-5%. On the other hand, we also find cases where the distortion is negligible (relative to the measurement errors on the planetary parameters) even in the cases of some very short period systems, depending on the mass ratio and planetary radius. For high-density gas planets requiring apparently anomalously large core masses, density corrections due to tidal deformation could become important for the shortest-period systems.

Investigation of Machining Parameters for Multiple Response Optimization in Microelectrodischarge Milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R a ), maximum peak-to-valley roughness height (R y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R a , R y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R a , R y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min−1, respectively for 4.79 μm s−1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.

Algorithms for Hidden Markov Models With Imprecisely Specified Parameters

Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic graphical models, they require the specification of precise probability values, which can be too restrictive for some domains, especially when data are scarce or costly to acquire. We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions. Our models have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. Efficient inference algorithms are developed to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that the use of imprecise probabilities leads to more reliable inferences without compromising efficiency.

Absolute magnitudes and slope parameters of Pan-STARRS PS1 asteroids -preliminary results

We present the study of absolute magnitude (H) and slope parameter (G) of 170,000 asteroids observed by the Pan-STARRS1 telescope during the period of 15 months within its 3-year all-sky survey mission. The exquisite photometry with photometric errors below 0.04 mag and well-defined filter and photometric system allowed to derive H and G with statistical and systematic errors. Our new approach lies in the Monte Carlo technique simulating rotation periods, amplitudes, and colors, and deriving most-likely H, G and their systematic errors. Comparison of H_M by Muinonen's phase function (Muinonen et al., 2010) with the Minor Planet Center database revealed a negative offset of 0.22±0.29 meaning that Pan-STARRS1 asteroids are fainter. We showed that the absolute magnitude derived by Muinonen's function is systematically larger on average by 0.14±0.29 and by 0.30±0.16 when assuming fixed slope parameter (G=0.15, G_{12}=0.53) than Bowell's absolute magnitude (Bowell et al., 1989). We also derived slope parameters of asteroids of known spectral types and showed a good agreement with the previous studies within the derived uncertainties. However, our systematic errors on G and G_{12} are significantly larger than in previous work, which is caused by poor temporal and phase coverage of vast majority of the detected asteroids. This disadvantage will vanish when full survey data will be available and ongoing extended and enhanced mission will provide new data.

Molecular pedigree reconstruction and estimation of evolutionary parameters in a wild Atlantic salmon river system with incomplete sampling: a power analysis

Background: Pedigree reconstruction using genetic analysis provides a useful means to estimate fundamental population biology parameters relating to population demography, trait heritability and individual fitness when combined with other sources of data. However, there remain limitations to pedigree reconstruction in wild populations, particularly in systems where parent-offspring relationships cannot be directly observed, there is incomplete sampling of individuals, or molecular parentage inference relies on low quality DNA from archived material. While much can still be inferred from incomplete or sparse pedigrees, it is crucial to evaluate the quality and power of available genetic information a priori to testing specific biological hypotheses. Here, we used microsatellite markers to reconstruct a multi-generation pedigree of wild Atlantic salmon (Salmo salar L.) using archived scale samples collected with a total trapping system within a river over a 10 year period. Using a simulation-based approach, we determined the optimal microsatellite marker number for accurate parentage assignment, and evaluated the power of the resulting partial pedigree to investigate important evolutionary and quantitative genetic characteristics of salmon in the system.

Results: We show that at least 20 microsatellites (ave. 12 alleles/locus) are required to maximise parentage assignment and to improve the power to estimate reproductive success and heritability in this study system. We also show that 1.5 fold differences can be detected between groups simulated to have differing reproductive success, and that it is possible to detect moderate heritability values for continuous traits (h(2) similar to 0.40) with more than 80% power when using 28 moderately to highly polymorphic markers.

Conclusion: The methodologies and work flow described provide a robust approach for evaluating archived samples for pedigree-based research, even where only a proportion of the total population is sampled. The results demonstrate the feasibility of pedigree-based studies to address challenging ecological and evolutionary questions in free-living populations, where genealogies can be traced only using molecular tools, and that significant increases in pedigree assignment power can be achieved by using higher numbers of markers.

Microstructural evolution and mechanical properties of short-term thermally exposed 9/12Cr heat-resistant steels

The microstructural evolution during short-term (up to 3000 hours) thermal exposure of three 9/12Cr heat-resistant steels was studied, as well as the mechanical properties after exposure. The tempered martensitic lath structure, as well as the precipitation of carbide and MX type carbonitrides in the steel matrix, was stable after 3000 hours of exposure at 873 K (600 °C). A microstructure observation showed that during the short-term thermal exposure process, the change of mechanical properties was caused mainly by the formation and growth of Laves-phase precipitates in the steels. On thermal exposure, with an increase of cobalt and tungsten contents, cobalt could promote the segregation of tungsten along the martensite lath to form Laves phase, and a large size and high density of Laves-phase precipitates along the grain boundaries could lead to the brittle intergranular fracture of the steels.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.