Direct observation of ultrafast thermal and non-thermal lattice deformation of polycrystalline Aluminum film

The dynamics of thermal and non-thermal lattice deformation of nanometer thick polycrystalline aluminum film has been studied by means of femtosecond (fs) time-resolved electron diffraction. We utilized two different pump wavelengths: 800 nm, the fundamental of Ti: sapphire laser and 1250 nm generated by a home-made optical parametric amplifier(OPA). Our data show that, although coherent phonons were generated under both conditions, the diffraction intensity decayed with the characteristic time of 0.9+/-0.3 ps and 1.7+/-0.3 ps under 800 nm and 1250 nm excitation, respectively. Because the 800 nm laser excitation corresponds to the strong interband transition of aluminum due to the 1.55 eV parallel band structure, our experimental data indicate the presence of non-thermal lattice deformation under 800 nm excitation, which occurs on a time-scale that is shorter than the thermal processes dominated by electron-phonon coupling under 1250 nm excitation

Mapping transient electric fields with picosecond electron bunches

Transient electric fields, which are an important but hardly explored parameter of laser plasmas, can now be diagnosed experimentally with combined ultrafast temporal resolution and field sensitivity, using femtosecond to picosecond electron or proton pulses as probes. However, poor spatial resolution poses great challenges to simultaneously recording both the global and local field features. Here, we present a direct 3D measurement of a transient electric field by time-resolved electron schlieren radiography with simultaneous 80-Êm spatial and 3.7-ps temporal resolutions, analyzed using an Abel inversion algorithm. The electric field here is built up at the front of an aluminum foil irradiated with a femtosecond laser pulse at 1.9 × 1012 W/cm2, where electrons are emitted at a speed of 4 × 106 m/s, resulting in a unique gpeak.valleyh transient electric field map with the field strength up to 105 V/m. Furthermore, time-resolved schlieren radiography with charged particle pulses should enable the mapping of various fast-evolving field structures including those found in plasma-based particle accelerators

Study of transfer learning from 2D supercritical airfoils to 3D transonic swept wings

Machine learning has been widely utilized in fluid mechanics studies and aerodynamic optimizations. However, most applications, especially flow field modeling and inverse design, involve two-dimensional flows and geometries. The dimensionality of three-dimensional problems is so high that it is too difficult and expensive to prepare sufficient samples. Therefore, transfer learning has become a promising approach to reuse well-trained two-dimensional models and greatly reduce the need for samples for three-dimensional problems. This paper proposes to reuse the baseline models trained on supercritical airfoils to predict finite-span swept supercritical wings, where the simple swept theory is embedded to improve the prediction accuracy. Two baseline models for transfer learning are investigated: one is commonly referred to as the forward problem of predicting the pressure coefficient distribution based on the geometry, and the other is the inverse problem that predicts the geometry based on the pressure coefficient distribution. Two transfer learning strategies are compared for both baseline models. The transferred models are then tested on the prediction of complete wings. The results show that transfer learning requires only approximately 500 wing samples to achieve good prediction accuracy on different wing planforms and different free stream conditions. Compared to the two baseline models, the transferred models reduce the prediction error by 60% and 80%, respectively

Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design

Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features.Comment: 35 pages, 30 figure