arXiv subjects

Hongtao Zhu

Publications and source records attributed to Hongtao Zhu.

3 recordsLinked to original sources

arXiv2025

Numerical investigation of erosive wear considering surface degradation using coupled CFD-DEM and a bond model

A three-dimensional simulation model is proposed here to study the erosive wear of structure caused by solid particles, which accounts for the accumulation of surface deformation and degradation during the erosion process. Although there are numerous studies on the erosion, they have been primarily based on semi-empirical equations to predict the material loss and lack of an engineering approach to account for the surface evolution during erosion process, which is proved to be of great important. Our proposed model aims to overcome these challenges. In this approach, a conventional volume of solid object is discretized into a number of discrete spherical elements. A bond network is added to connect neighbouring particles at micro-scale, forming a cohesive structure at macro-scale. It is indeed a combination of particulate flow simulation by coupled Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) and materials modelling by DEM using a bond model. An extensive study of fluid response in both laminar and turbulent flow is conducted when the traditional continuous rigid object is replaced by a corresponding discrete deformable object composed of bonded spherical particles. The replacement of discrete object allows this model to naturally incorporate the three-way interaction of fluid, structure, and particles. The interaction between fluid and structure at macro-scale is implicitly represented by the interaction between fluid and bonded particles at micro-scale, while material properties and the micro-structure of the solid object are reproduced by properties of bond that account for deformation and fracture. Both resolved and unresolved coupling schemes result in good agreement with the traditional CFD approach.

physics.flu-dyn↗

arXiv2024

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.

stat.ME↗

arXiv2021

Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarises the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhanced the F1-Score but not the mAP.

cs.CV↗