Reliability Optimization Research Articles

Multi-XGB: A multi-objective reliability evaluation approach for aeroengine turbine discs

Multiple failure sites often co-existed in aeroengine turbine discs, its reliability performance contains complex characteristics of high-nonlinearity and multiple-output, leading to the insufficient reliability evaluating accuracy/efficiency of traditional direct simulation or surrogate separate methods. To address this problem, a multi-XGB model is presented by fusing the linkage sampling technique to extract a multi-input multi-output dataset, the XGBoost series ensemble platform to build the modelling architecture, and the Bayesian optimization algorithm to find the optimal model hyperparameters. Moreover, a multi-XGB driven multi-objective reliability evaluation framework is presented as well. Regarding a typical aeroengine high-pressure turbine disc with multiple failure sites (i.e., disc core, disc rim) as an example, the probabilistic distributions, correlation relationships, and reliability degree for each/all frail sites in turbine disc are acquired by applying the presented multi-XGB model. Through the method comparisons, the presented method is verified to hold high computing accuracy and efficiency in evaluating the multi-objective reliability of turbine discs. The current efforts shed light on the theoretical development for reliability evaluation, design, and optimization of high-end equipment like aeroengine.

Federated Learning via Plurality Vote.

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.

Joint Reliability Optimization and Beamforming Design for STAR-RIS-Aided Multi-User MISO URLLC Systems

Joint Reliability Optimization and Beamforming Design for STAR-RIS-Aided Multi-User MISO URLLC Systems

MODELING THE DYNAMICS OF DEFORMABLE OBJECTS BASED ON VOLUMETRIC PATCHES OF FREE FORMS

A method for solving nonlinear implicit numerical integration problems based on volumetric patches of free forms for modeling deformable objects with high-speed dynamics is proposed. This method improves the accuracy, consistency and controllability of deformation modeling and animation. The method is characterized by speed, accuracy, stability and uses optimization with region decomposition. The method is well suited for modeling deformable bodies with a large time step, in a wide range of deformation dynamics. We propose decomposed optimization, an optimization method based on free-form patches with region decomposition to minimize incremental potentials at each time step. The method uses quadratic matrix decomposition to combine non-overlapping subregions. The Hessian is evaluated once at the beginning of the time step. The advantages of the method are as follows. Geometric primitives and their mathematical models are proposed, which allow the reasonable application of these primitives to solve problems of volume-oriented modeling. Such requirements are met by volumetric patches of free forms based on analytical perturbation functions relative to the base triangles. The decomposed Hessian is constructed for each subregion and calculated using a set of vertices taken from a complete non-decomposed grid. The weights add the missing second-order Hessian data to the vertices of the subregions from the neighbors along the decomposition boundaries. Thanks to this, the descent is performed along the grid coordinates. There is no need to add gradients. During the descent, the gradient is determined. The Hessians under the region are calculated and factorized in parallel once per time step. They are used as an initializer at each iteration. Then the results are mixed together. This ensures stable and continuous high-quality modeling. An automated and reliable optimization method adapted for modeling nonlinear materials, high-speed dynamics and large deformations is proposed.

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner.

A reliability-based multidisciplinary design parallel optimization method based on double-layer approximation model for nuclear fuel assembly bottom nozzle

The design of intricate systems such as the nuclear fuel assembly necessitates elevated levels of multi-disciplinary performance and reliability. In current reliability-based multidisciplinary design optimization methods, the decoupling strategy between optimization and reliability assessment involves sequential optimization and reliability assessment, with reliability serving as a constraint for optimization. This approach often suffers failure in locating the global optimal solution. In light of this issue, a reliability-based multidisciplinary design parallel optimization method based on double-layer approximation model is hereby proposed, achieving parallel separation and global efficient optimization of deterministic disciplinary performances and reliability, while performing efficient and accurate calculation on the multi-disciplinary reliability numerical solution. This method has been implemented for the multi-disciplinary uncertain optimization of the nuclear fuel assembly bottom nozzle. In comparison to conventional optimization methods, this approach demonstrates enhanced global optimization effectiveness.

Magnetic and Thermal Behavior of a Planar Toroidal Transformer

This paper presents a study on the magnetic and thermal behaviors of a planar toroidal transformer, comprising two planar toroidal coils. In our configuration, the primary coil consists of twenty turns, while the secondary coil consists of ten turns. This design combines the advantages of both toroidal and planar transformers: it employs flat coils, akin to those utilized in planar transformers, while retaining a toroidal shape for its magnetic core. This combination enables leveraging the distinctive characteristics of both transformer types. This study delves into electromagnetic and thermal behaviors. Electromagnetic behavior is elucidated through Maxwell’s equations, offering insights into the distribution of magnetic fields, potentials, and electric current densities. Fluid flow is modeled via the Navier–Stokes equations. By coupling these equation sets, a more comprehensive and accurate portrayal of the thermal phenomena surrounding electrical equipment is attained. Such research is invaluable in the design and optimization of electrical systems, empowering engineers to forecast and manage thermal effects more efficiently. Consequently, this aids in enhancing the reliability, durability, and performance optimization of electrical equipment. The mathematical model was solved using the finite element method integrated into the COMSOL Multiphysics software v. 6.0. The COMSOL Multiphysics simulation showed correct behavior of potential, electric field, current density, and uniformly distributed temperature. In addition, this planar toroidal coil transformer model offers many advantages, such as small dimensions, high resonance frequency, and high operating reliability. This study made it possible to identify the range of its optimal functioning.

Conditional knockdown of OsMLH1 to improve plant prime editing systems without disturbing fertility in rice.

High-efficiency prime editing (PE) is desirable for precise genome manipulation. The activity of mammalian PE systems can be largely improved by inhibiting DNA mismatch repair by coexpressing a dominant-negative variant of MLH1. However, this strategy has not been widely used for PE optimization in plants, possibly because of its less conspicuous effects and inconsistent performance at different sites. We show that direct RNAi knockdown of OsMLH1 in an ePE5c system increases the efficiency of our most recently updated PE tool by 1.30- to 2.11-fold in stably transformed rice cells, resulting in as many as 85.42% homozygous mutants in the T0 generation. The high specificity of ePE5c is revealed by whole-genome sequencing. To overcome the partial sterility induced by OsMLH1 knockdown of ePE5c, a conditional excision system is introduced to remove the RNAi module by Cre-mediated site-specific recombination. Using a simple approach of enriching excision events, we generate 100% RNAi module-free plants in the T0 generation. The increase in efficiency due to OsMLH1 knockdown is maintained in the excised plants, whose fertility is not impaired. This study provides a safe and reliable plant PE optimization strategy for improving editing efficiency without disturbing plant development via transient MMR inhibition with an excisable RNAi module of MLH1.

Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy

Vibration active control is a significant problem in practical engineering, but the influence of hybrid uncertainties in practical engineering cannot be ignored. For the uncertainties with adequate data, probability theory can be used while for the uncertainties with insufficient data, probability theory is limited and interval quantification can be used. Taking the widely used proportional-integral-differential (PID) control as an example, a novel study on controller design based on probability density evolution method and interval-oriented sequential optimization strategy considering interval and random uncertainties is proposed. Firstly, under a certain sample point of interval uncertainties, probability density evolution equation is utilized to calculate the probability density function of uncertain response considering only random uncertainties. In this way the probabilistic reliability corresponding to the sample point can be obtained. Then the hybrid reliability is defined as the average probability within the interval uncertainty and the adaptive dichotomy method is utilized to choose proper sample points of interval uncertainties, in which the reliability curve and the interval uncertainty axis is regarded as the judgement of convergence. Next the hybrid reliability is introduced to the optimization as a constraint and the PID gains reliable design optimization is established to balance the energy-consuming and safety. Finally, the sequential optimization strategy is adopted to improve the efficiency of optimization. In every iteration step of the optimization, the constraint of deterministic optimal design is adjusted according to the static reliability at the moment before the passage or reaching the maximum value. Three numerical examples are presented to demonstrate the accuracy and practicability of the proposed framework.

Replica fault-tolerant scheduling with time guarantee under energy constraint in fog computing

Fog computing offers stronger local computing power and reduces data transmission load, making it an ideal solution to meet the energy-saving and efficient requirements of intelligent connected vehicle applications. As intelligent networked vehicles and vehicle–road collaboration technologies advance rapidly, optimizing scheduling under fog computing architecture has become a prominent research area. However, existing studies primarily concentrate on parallel task scheduling with low energy consumption or high real-time performance, failing to address the requirement for high reliability in intelligent networked vehicle scenarios. To achieve time and reliability optimization for vehicular applications in fog computing architecture, this paper proposes a fog computing task scheduling algorithm and explores its extension using replication techniques. Subsequently, the algorithm underwent evaluation utilizing randomly generated directed acyclic graph models as well as real-life automotive application instances. The experimental findings indicate that in comparison to existing methods, the proposed algorithm exhibits a notable improvement in reliability while ensuring time optimization, thereby demonstrating a distinct level of advancement and practicality.

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL.

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.

Optimization of reliability and sensitivity analysis for flange structures

The study delves into the reliability-driven optimization design of the flanges, drawing from reliability design theory, advanced optimization methodologies, and comprehensive sensitivity analysis. Flanges, integral to mechanical systems, demand precise design. Reliability design theory emphasizes consistent operation under diverse conditions. A novel approach to the flange-specific reliability sensitivity analysis method is central to this research. It promises to revolutionize flange design. By understanding the first two moments of core random parameters, we expedite reliability-driven optimization designs for mechanical connections. The programs enhance our approach, streamlining the pursuit of reliability-driven designs and providing rapid, accurate sensitivity analysis data for mechanical connections. This data offers critical insights into parameter influence, enabling targeted design adjustments. In summary, this research represents a significant advancement in mechanical engineering. By integrating reliability design theory with cutting-edge methodologies, it promises to enhance the quality and reliability of mechanical connections, meeting the rigorous demands of contemporary engineering applications. This holistic approach will play a pivotal role in the future of mechanical component design.

Research on optimal design method of dynamic stress parameters of helical gear based on orthogonal experimental design

Aiming at the reliability optimization design problem of two-grade helical cylindrical gear transmission system, the UG parameterized model based on expression and ADAMS dynamic simulation model under real working conditions are integrated in ISIGHT platform, and the distribution law of peak contact force on the tooth surface of helical gear pair in the transmission system at different meshing times is obtained. Considering the uncertainty of material properties, load conditions and geometric parameters, the sample set was generated according to the orthogonal experimental design method, and the Kriging approximation model was established. On this basis, the contact strength reliability of helical gear was analyzed. The mathematical model of meshing efficiency reliability optimization of helical gear pair was established and solved by Hooke-Jeeves Direct Search method. The results show that the meshing efficiency and reliability of the optimized helical gear pair are improved, and the peak value of the contact force is obviously reduced.

The Role of Oxygen Vacancy and Hydrogen on the PBTI Reliability of ALD IGZO Transistors and Process Optimization

The Role of Oxygen Vacancy and Hydrogen on the PBTI Reliability of ALD IGZO Transistors and Process Optimization

A prediction model for drilling temperature of CFRP/Ti stacks and green cooling strategy considering chip ejection process

Drilling temperature has always been a hot issue in the drilling process of CFRP/Ti stacks. Accurately predicting drilling temperature is necessary for reliable optimization of drilling parameters. However, since the drilling process involves two materials and may affect each other, the prediction of temperature during drilling is still a challenge. In this paper, a novel drilling temperature model is established to predict the temperature distribution in CFRP workpiece during the drilling process of CFRP/Ti stacks, considering the minor cutting edge and titanium alloy chip ejection process. Specifically, according to different drilling phases, in addition to considering the heat source of the cutting zone of different materials, the heat source generated by the interaction of minor cutting edge and rebound hole wall of CFRP and the heat source formed by the thermal-mechanical effect of Ti chip ejection process are also considered. The results show that the prediction errors of peak temperature in CFRP entrance and stack interface region during drilling are within 15.8 % and 10.9 %, respectively, which indicates the validity of the model. Finally, the green cooling strategy (High and low frequency compound vibration-assisted drilling) is adopted to significantly reduce the peak temperature in the drilling process by controlling the chip morphology and ejection process, which further proves that the thermo-mechanical action of Ti chips on CFRP cannot be ignored, and provides reference for temperature modeling and low-damage processing of CFRP/metal stacks.

Application of the FMECA Method for Optimizing the Reliability of the 1600T Press

Reliability optimization is a process aimed at improving the effectiveness and efficiency of maintenance activities within an organization. This optimization can concern various aspects, including failure prevention, resource management, cost reduction, and improving the operational availability of equipment. In this work, our study aims to implement a comprehensive maintenance strategy for the 1600T press at Algal[Formula: see text] company with the primary objectives of preventing breakdowns, minimizing unplanned downtime, optimizing productivity, extending the lifespan of equipment, ensuring safety, and improving the competitiveness and profitability of the company. The problem of reliability and availability of the 1600T press at the Algal[Formula: see text] company can be addressed by a systematic approach to industrial maintenance. By adopting a holistic approach to industrial maintenance, Algal[Formula: see text] company can improve the reliability and availability of its equipment, thereby reducing unplanned downtime and optimizing overall productivity. By systematically applying FMECA to the 1600T press, the optimization efforts become data-driven and focused on addressing the most critical risks. This, in turn, significantly contributes to optimizing the reliability and availability of the equipment, reducing unplanned downtime, and enhancing overall operational efficiency.

Denaturing mass photometry for rapid optimization of chemical protein-protein cross-linking reactions

Chemical cross-linking reactions (XL) are an important strategy for studying protein-protein interactions (PPIs), including low abundant sub-complexes, in structural biology. However, choosing XL reagents and conditions is laborious and mostly limited to analysis of protein assemblies that can be resolved using SDS-PAGE. To overcome these limitations, we develop here a denaturing mass photometry (dMP) method for fast, reliable and user-friendly optimization and monitoring of chemical XL reactions. The dMP is a robust 2-step protocol that ensures 95% of irreversible denaturation within only 5 min. We show that dMP provides accurate mass identification across a broad mass range (30 kDa–5 MDa) along with direct label-free relative quantification of all coexisting XL species (sub-complexes and aggregates). We compare dMP with SDS-PAGE and observe that, unlike the benchmark, dMP is time-efficient (3 min/triplicate), requires significantly less material (20–100×) and affords single molecule sensitivity. To illustrate its utility for routine structural biology applications, we show that dMP affords screening of 20 XL conditions in 1 h, accurately identifying and quantifying all coexisting species. Taken together, we anticipate that dMP will have an impact on ability to structurally characterize more PPIs and macromolecular assemblies, expected final complexes but also sub-complexes that form en route.

APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SOLUTION OF ENGINEERING PROBLEMS. ADVANTAGES AND CHALLENGES

Artificial Intelligence (AI) is becoming an integral part of modern engineering, promising to transform the ways of design, production, and system management. Its application ranges from process automation to optimization of production cycles, enhancing efficiency and reliability. However, despite the advantages, there are challenges such as AI integration into the design phase, safety requirements, and data confidentiality. Especially in the EPC industry, where each project has unique requirements, and high safety standards complicate AI implementation. Additionally, the need for qualified professionals and effective data collection mechanisms create further obstacles. Successful AI implementation requires the integration of company experience, a strategic approach, and support from senior management.