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2. Multi-Surrogate Collaboration Approach for Creep-Fatigue Reliability Assessment of Turbine Rotor

Author

Lu-Kai Song and Guang-Chen Bai

Subjects
Creep-fatigue life, reliability assessment, turbine rotor, surrogate model, neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The creep-fatigue resistance of turbine rotor seriously affects the reliability performance and service lifetime of aircraft engine. Creep-fatigue reliability assessment is an effective measure to quantify the uncertain creep-fatigue damage and evaluate the creep-fatigue reliable life for turbine rotor. To improve the modeling accuracy and simulation efficiency of creep-fatigue reliability assessment, a multi-surrogate collaboration approach (MSCA) is proposed by absorbing the strengths of the proposed dynamic neural network surrogate (DNNS) into distributed collaborative strategy. The creep-fatigue reliability assessment of a typical turbine rotor is regarded as one case to estimate the presented MSCA with respect to the fluctuations of multi-physical variables and the variabilities of multi-model parameters. The assessment results reveal that the creep-fatigue reliable life of turbine rotor under reliability degree of 0.998 7 is 629 cycles, and the fatigue strength coefficient and holding creep time play a leading role on creep-fatigue reliable life since their effect probabilities of 27 % and 19 %, respectively. Comparison of various methods (direct Monte Carlo simulation, response surface, neural network surrogate, DNNS) shows that the presented MSCA holds high efficiency and accuracy in creep-fatigue reliability assessment of turbine rotor.

Published
2020
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3. A Novel Reliability Analysis Approach With Collaborative Active Learning Strategy-Based Augmented RBF Metamodel

Author

Yanxu Wei, Guangchen Bai, and Lu-Kai Song

Subjects
Active learning function, radial basis function, reliability analysis, metamodel, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Metamodels in lieu of time-demanding performance functions can accelerate the reliability analysis effectively. In this paper, we propose an efficient collaborative active learning strategy-based augmented radial basis function metamodel (CAL-ARBF), for reliability analysis with implicit and nonlinear performance functions. For generating the suitable samples, a CAL function is first designed to constrain the new samples being generated in sensitivity region, near limit state surface and keep certain distances mutually. Then by adjusting the adjustment coefficient of CAL function, the CAL-ARBF is mathematically modeled and the corresponding reliability analysis theory is developed. The effectiveness of the proposed approach is validated by four numerical samples, including global nonlinear problem, local nonlinear problem, nonlinear oscillator and truss structure. Through comparison of several state-of-the-art methods, the proposed CAL-ARBF is demonstrated to possess the computational advantages in efficiency and accuracy for reliability analysis.

Published
2020
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26. Inspecting End-to-End Encrypted Communication Differentially for the Efficient Identification of Harmful Media.

Author

Zheng, Tengfei, Zhou, Tongqing, Lu, Kai, and Cai, Zhiping

Abstract

Due to the immense benefits of guaranteeing user privacy, popular messaging platforms have shown enthusiasm for deploying End-to-End Encryption (E2EE). However, E2EE could be misused for bypassing media moderation, opening a shortcut for the viral spreading of harmful media. Private hash-matching techniques are proposed to identify harmful content in E2EE. Unfortunately, the pioneering solution incurs prohibitively high latency due to redundant user-cloud interactions for a private inspection. In this paper, we design Entbergen for efficient inspection of E2EE media by differentially handling harmless and harmful ingredients. For this, a novel Private-2D BloOm filter with Fuzzy Query (PBO-FQ) is designed for local, agile, and private media hash matching. It is proposed as the first structure that adapts inverted index and differential privacy (DP) towards seamless integration of sketch and mask encoding. With PBO-FQ, Entbergen can instantly filter out harmless media and only pays attention to the small-scale counterparts by scrutinizing them privately based on homomorphic encryption. Security analysis shows that Entbergen can effectively fulfil the desired privacy requirements. Extensive evaluations demonstrate that Entbergen is sufficiently efficient (w.r.t. computation and communication overhead) for working on mobile devices and can easily scale to real-world inspection with a large database. [ABSTRACT FROM AUTHOR]

Published
2023
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33. RangeKV: An Efficient Key-Value Store Based on Hybrid DRAM-NVM-SSD Storage Structure

Author

Zhilong Cheng, Zhan Lin, Lu Kai, and Jiguang Wan

Subjects
non-volatile memory, General Computer Science, hybrid storage, Computer science, business.industry, Write amplification, log-structured merge-tree, Hash function, General Engineering, 020206 networking & telecommunications, 02 engineering and technology, hash index, Non-volatile memory, Tree (data structure), 020204 information systems, Embedded system, Memory architecture, 0202 electrical engineering, electronic engineering, information engineering, Key-value store, General Materials Science, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, Throughput (business), lcsh:TK1-9971, Dram
Abstract

Persistent key-value (KV) stores are an integral part of storage infrastructure in data centers. Emerging non-volatile memory (NVM) technologies are potential alternatives for future memory architecture design. In this study, we use NVM to optimize the KV store and propose RangeKV, an LSM-tree based persistent KV store built on a heterogeneous storage architecture. RangeKV uses RangeTab in NVM to manage L0 data and increases L0 capacity to reduce the number of LSM tree levels and system compactions. RangeKV pre-constructs the hash index of RangeTab data to reduce NVM access times and adopts a double-buffer structure to reduce LSM-tree write amplification due to compactions. We implement RangeKV based on RocksDB and conduct a comparative test and performance evaluation with RocksDB and NoveLSM. The test results show that the overall random write throughput is improved by $4.5\times $ to $5.7\times $ compared to RocksDB. In addition, RangeKV has a significant performance advantage over NoveLSM.

Published
2020

40. A Video Stitching System of Underwater Image

Author

Wang, Zhenyu, primary, Wu, Haoming, additional, Yu, Tianming, additional, Fu, Mengqin, additional, Liu, Bernie, additional, Cheng, Jieren, additional, Ye, Quanfeng, additional, Zhang, Ni, additional, Huang, Shuo, additional, Lu, Xingyue, additional, Li, Xiulai, additional, Lu, Kai, additional, and Xie, Gengquan, additional

Published
2021
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46. The Influence Of Controlled Parameters On Ultrasonic Peening Forming By Numerical Simulation

Author

Feng Qian, Xiao-long Lu, Lu-kai Shi, and Zhi-jun Sun

Subjects
Materials science, Computer simulation, business.industry, Peening, 02 engineering and technology, Structural engineering, Deformation (meteorology), 021001 nanoscience & nanotechnology, Curvature, Finite element method, Stress (mechanics), 020303 mechanical engineering & transports, 0203 mechanical engineering, Residual stress, Ultrasonic sensor, 0210 nano-technology, business
Abstract

In this paper, a three-step finite element model for accurate simulation of Ultrasonic Peening Forming (UPF) process is proposed with the computer assisted analyses. First, an explicit FEA model was built with ABAQUS to simulate the impact process, in which the distributions of resulting residual stress inside the simplified sub-model could be obtained. Then, by using a C language program, the resulting stress data for centre-impact zone of the sub-model was extended to the full-size complete target plate. Finally, the arc height and the curvature radii of the new target plate in transverse and longitudinal directions are calculated by an implicit FEA model in ABAQUS. Numerical results of the peening process reveal the influence of the different controlled parameters on the residual stress profiles as well as the resultant curvature deformation of target plate. The experiment related to the thickness of the plate is carried out. This numerical simulation impact model could be used for predicting the residual stress distribution and panel deformation, providing another alternative for investigating and optimizing the UPF process.

Published
2021
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47. ParaX : Bandwidth-Efficient Instance Assignment for DL on Multi-NUMA Many-Core CPUs.

Author

Zhang, Yiming, Yin, Lujia, Li, Dongsheng, Peng, Yuxing, and Lu, Kai

Subjects
NATURAL language processing, IMAGE recognition (Computer vision), DEEP learning, CENTRAL processing units
Abstract

Commercial clouds now heavily use CPUs in DL (deep learning) because there are large numbers of CPUs which would otherwise sit idle during off-peak periods. Following the trend, CPU vendors have not only released high-performance many-core CPUs but also developed efficient math kernel libraries. However, current DL platforms cannot scale well to a large number of CPU cores, making many-core CPUs inefficient in DL computation. We analyze the memory access patterns of various layers and identify the root cause of the low scalability, i.e., the per-layer barriers that are implicitly imposed by current platforms which assign one single instance (i.e., one batch of input data) to a CPU. The barriers cause severe memory bandwidth contention and CPU starvation in the access-intensive layers (like activation and BN). This paper presents a novel approach called ParaX, which boosts the performance of DL on multi-NUMA (non-uniform memory access) many-core CPUs by effectively alleviating bandwidth contention and CPU starvation. Our key idea is to assign one instance to each CPU core instead of to the entire CPU, so as to remove the per-layer barriers on the executions of the many cores. ParaX designs an ultralight scheduling policy which sufficiently overlaps the access-intensive layers with the compute-intensive ones to avoid contention, and proposes a NUMA-aware gradient server mechanism for training which leverages shared memory to substantially reduce the overhead of per-iteration parameter synchronization. We have implemented ParaX on MXNet. Extensive evaluation on a two-NUMA Intel 8280 CPU shows that ParaX significantly improves the training/inference throughput for all tested models (for image recognition and natural language processing) by $1.73\times \sim 2.93{\times}$ 1. 73 × ∼ 2. 93 × . [ABSTRACT FROM AUTHOR]

Published
2022
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