Experimental investigation of hole boring and light sail regimes of RPA by varying laser and target parameters

Temporal evolution of plasma jets from micrometre-scale thick foils following the interaction of intense (3 × 10 W cm ) laser pulses is studied systematically by time resolved optical interferometry. The fluid velocity in the plasma jets is determined by comparing the data with 2D hydrodynamic simulation, which agrees with the expected hole-boring (HB) velocity due to the laser radiation pressure. The homogeneity of the plasma density across the jets has been found to be improved substantially when irradiating the laser at circular polarization compared to linear polarization. While overdense plasma jets were formed efficiently for micrometre thick targets, decreasing the target areal density and/or increasing the irradiance on the target have provided indication of transition from the 'HB' to the 'light sail (LS)' regime of RPA, characterized by the appearance of narrow-band spectral features at several MeV/nucleon in proton and carbon spectra.

Measuring specific parameters in pretensioned concrete members using a single testing technique

Pretensioned concrete members are designed and manufactured by using at least two materials: concrete and prestressing reinforcement. Also, two main stages must be considered: prestress transfer and member loading. Hence, the behavior of these members depends strongly on the reinforcement-to-concrete bond performance and prestress losses. In this paper, a testing technique to measure the specific parameters related with the involved phenomena is presented. The testing technique is based on the analysis of series of specimens varying in embedment length to simulate several cross sections at only one end of a pretensioned concrete member. Each specimen is characterized by means of the sequential release of the prestress transfer (detensioning) and the pull-out (loading) operation. The test provides data on prestressing force, transmission length (initial and long-term), anchorage length (without and with slip), reinforcement slips, bond stresses, longitudinal concrete strains, concrete modulus of elasticity, and prestress losses (instantaneous and time-dependent).

Effect of the crown design and interface lute parameters on the stress-state of a machined crown-tooth system: A finite element analysis

The effect of preparation design and the physical properties of the interface lute on the restored machined ceramic crown-tooth complex are poorly understood. The aim of this work was to determine, by means of three-dimensional finite element analysis (3D FEA) the effect of the tooth preparation design and the elastic modulus of the cement on the stress state of the cemented machined ceramic crown-tooth complex. The three-dimensional structure of human premolar teeth, restored with adhesively cemented machined ceramic crowns, was digitized with a micro-CT scanner. An accurate, high resolution, digital replica model of a restored tooth was created. Two preparation designs, with different occlusal morphologies, were modeled with cements of 3 different elastic moduli. Interactive medical image processing software (mimics and professional CAD modeling software) was used to create sophisticated digital models that included the supporting structures; periodontal ligament and alveolar bone. The generated models were imported into an FEA software program (hypermesh version 10.0, Altair Engineering Inc.) with all degrees of freedom constrained at the outer surface of the supporting cortical bone of the crown-tooth complex. Five different elastic moduli values were given to the adhesive cement interface 1.8 GPa, 4 GPa, 8 GPa, 18.3 GPa and 40 GPa; the four lower values are representative of currently used cementing lutes and 40 GPa is set as an extreme high value. The stress distribution under simulated applied loads was determined. The preparation design demonstrated an effect on the stress state of the restored tooth system. The cement elastic modulus affected the stress state in the cement and dentin structures but not in the crown, the pulp, the periodontal ligament or the cancellous and cortical bone. The results of this study suggest that both the choice of the preparation design and the cement elastic modulus can affect the stress state within the restored crown-tooth complex.

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.

Estimation of the hydraulic conductivity of soils improved with vertical drains

In this study, some limitations associated with modeling the hydraulic conductivity of soil improved with vertical drains are discussed. In addition, some limitations of conventional methodologies for deducing the hydraulic conductivity from oedometer or Rowe cell tests are investigated. An alternative approach for estimating the hydraulic conductivity in soils improved by vertical drains is discussed. This methodology will allow for simpler finite element modeling of consolidation due to vertical drains. The effectiveness of this technique has been demonstrated using a field study.

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.