An ef®cient and accurate method to evaluate the theoretical diffraction peak pro®les from spherical crystallites with lognormal size distribution (SLN pro®le) is presented. Precise results can be obtained typically by an eight-term numerical integral for any values of the parameters, by applying an appropriate substitution of the variable to the integral formula. The calculated SLN pro®les have been veri®ed by comparison with those calculated by inverse Fourier transform from the exact analytical solution of the Fourier-transformed SLN pro®le. It has been found that the shape of the SLN pro®le strongly depends on the variance of size distribution. When the logarithmic standard deviation 3 of the size distribution is close to 0.76, the SLN pro®le becomes close to a Lorentzian pro®le, and`super-Lorentzian' pro®les are predicted for larger values of 3, as has been concluded by Popa & Balzar [J. Appl. Cryst. (2002), 35, 338±346]. The intrinsic diffraction peak pro®les of an SiC powder sample obtained by deconvolution of the instrumental function have certainly showǹ super-Lorentzian' line pro®les, and they are well reproduced by the SLN pro®le for the value 3 = 0.93.

Abstract. Process of counterstreaming plasma generation for laser irradiation of the innner-surface of the first plane of a double-plane target is investigated. The image taken by streaked self-emission optical pyrometer and radiation hydrodynamic simulation show the plasma from the second plane is ablated by radiation almost at the laser timing. After ∼5 ns, increase in brightness and the generation of a plasma on the second plane are observed. According to the contemporary measurement of streaked interferometry, this is caused by the ablation of the second plane by the first plane plasma.

Method of defining the stationary acoustic environment conditions based on equivalent maximum load and cumulative fatigue damage of a vibro-acoustic structure under transient random acoustic load and its application

Since the early days of spacecraft development, accurate and simple vibro-acoustic prediction of equipment on the spacecraft panels subjected to acoustic excitation has been conducted in order to mitigate the over-conservative environmental test conditions. The conventional prediction methods are based on numerical solution of equation of motion, such as FEM/BEM and SEA, the so-called deductive approach. However, in a spacecraft with complex structures, there are many structural and non-structural objects, such as wiring harnesses, connecting cables and electronic boards in the equipment, which are usually difficult to be modelled into these methods. These un-modeled objects are usually treated by uncertainty of models, which always results in overly conservative prediction. In order to mitigate this uncertainty caused by model limitation of deductive approach, this study proposes a more accurate and simple inductive approach for vibro-acoustic prediction using Gradient Boosting Decision Trees (GBDT), which is one of the machine learning algorithms based on measured data. In addition, in order to take into account the vibration modes of the structural panels and waveform trends of vibration response spectrum in the creation of the learning model, explanatory variables based on the design drawing information were added, and the concept of bidirectional recurrent neural networks (BRNN), which is used for predicting time histories waveforms, was incorporated. This approach was applied to the vibro-acoustic prediction using the measured data of the equipment on the spacecraft panels in the acoustic tests of 7 spacecrafts developed by JAXA, and the results showed that this approach can make a reasonable prediction with the uncertainty margin mitigated by about 2 to 4 dB compared with the conventional approach.

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