Non-iterative structural topology optimization using deep learning

This paper presents a non-iterative topology optimizer for conductive heat transfer structures with the help of deep learning. An artificial neural network is trained to deal with the black-and-white pixel images and generate near-optimal structures. Our design is a two-stage hierarchical prediction–refinement pipeline consisting of two coupled neural networks: a generative adversarial network (GAN) for predicting a low resolution near-optimal structure and a super-resolution generative adversarial network (SRGAN) for predicting the refined structure in high resolution. Training datasets with given boundary conditions and the optimized pixel image structures are obtained after simulating a big amount of topology optimization procedures. For more effective training and inference, these datasets are generated with two different resolutions. Experiments demonstrated that our learning based optimizer can provide accurate estimation of the conductive heat transfer topology using negligible computational time. This effective incorporation of deep learning into topology optimization could enable promising applications in large-scale engineering structure design

Numerical Investigation of Preload Process of Bolted Joint with Superelastic Shape Memory Alloy

A phenomenological constitutive model is developed to describe the uniaxial transformation ratcheting behaviors of the superelastic shape memory alloy (SMA) by employing a cosine–type phase transformation equation with the initial martensite evolution coefficient that can capture the feature of the predictive residual martensite accumulation evolution and the nonlinear hysteresis loop on a finite element (FE) analysis framework. The effect of the applied loading level on transformation ratcheting is considered in the proposed model. The evolutions of transformation ratcheting and transformation stresses are constructed as the function of the accumulated residual martensite volume fraction. The FE implementation of the proposed model is carried out for the numerical analysis of transformation ratcheting of the SMA bar element. The integration algorithm and the expression of consistent tangent modulus are deduced in a new form for the forward and reverse transformation. The numerical results are compared with those of existing models; experimental results show the validity of the proposed model and its FE implementation in transformation ratcheting. Finally, a FE modeling is established for a repeated preload analysis of SMA bolted joint

Degradation and Pathways of Carvone in Soil and Water

Carvone is a monoterpene compound that has been widely used as a pesticide for more than 10 years. However, little is known regarding the fate of carvone, or its degradation products, in the environment. We used GC-MS (gas chromatography–mass spectrometry) to study the fate of carvone and its degradation and photolysis products under different soil and light conditions. We identified and quantified three degradation products of carvone in soil and water samples: dihydrocarvone, dihydrocarveol, and carvone camphor. In soil, dihydrocarveol was produced at very low levels (≤0.067 mg/kg), while dihydrocarvone was produced at much higher levels (≤2.07 mg/kg). In water exposed to differing light conditions, carvone was degraded to carvone camphor. The photolysis rate of carvone camphor under a mercury lamp was faster, but its persistence was lower than under a xenon lamp. The results of this study provide fundamental data to better understand the fate and degradation of carvone and its metabolites in the environment

Degradation and Pathways of Carvone in Soil and Water

Carvone is a monoterpene compound that has been widely used as a pesticide for more than 10 years. However, little is known regarding the fate of carvone, or its degradation products, in the environment. We used GC-MS (gas chromatography–mass spectrometry) to study the fate of carvone and its degradation and photolysis products under different soil and light conditions. We identified and quantified three degradation products of carvone in soil and water samples: dihydrocarvone, dihydrocarveol, and carvone camphor. In soil, dihydrocarveol was produced at very low levels (≤0.067 mg/kg), while dihydrocarvone was produced at much higher levels (≤2.07 mg/kg). In water exposed to differing light conditions, carvone was degraded to carvone camphor. The photolysis rate of carvone camphor under a mercury lamp was faster, but its persistence was lower than under a xenon lamp. The results of this study provide fundamental data to better understand the fate and degradation of carvone and its metabolites in the environment

Finite Element Analysis for the Self-Loosening Behavior of the Bolted Joint with a Superelastic Shape Memory Alloy

A macroscopic constitutive model is proposed in this research to reproduce the uniaxial transition ratcheting behaviors of the superelastic shape memory alloy (SMA) undergoing cyclic loading, based on the cosine-type phase transition equation with the initial martensite evolution coefficient that provides the predictive residual martensite accumulation evolution and the nonlinear feature of hysteresis loop. The calculated results are compared with the experimental results to show the validity of the present computational procedure in transition ratcheting. Finite element implementation for the self-loosening behavior of the superelastic SMA bolt is then carried out based on the proposed constitutive model to analyze the curves of stress-strain responses on the bolt bar, clamping force reduction law, dissipation energy change law of the bolted joint for different external loading cases, and preload force of the bolt