Reliability Optimization Research Articles

Cost-optimization based target reliabilities for design of structures exposed to fire

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment. Cost-benefit analyses, however, are complicated and computationally challenging, and hence impractical for application to individual projects. To address this issue, design guidance proposes target reliability indices for normal design conditions, but no target reliability indices are defined for structural fire design. We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures. We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions. The optimum safety level is expressed as a function of a new dimensionless variable named “Damage-to-investment indicator” (DII). The cost optimization approach is validated for the target reliability indices for normal design condition. The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures. Two case studies are presented: (i) a one-way loaded reinforced concrete slab and (ii) a steel column under axial loading. This study thus provides a framework for deriving optimum (target) reliability index for structural fire design which can support the development of rational provisions in codes and standards.

Optimization of a high through-flow design turbine using response surface method

Enhancing the through-flow capability of the turbine facilitates the potential to decrease the external dimension of the engine, leading to a reduction in weight and an increase in the thrust-to-weight ratio. The optimization of blade profile in terms of aerodynamic design is an essential strategy to improve the overall performance of the high through-flow design turbine. This study aims to develop an automated optimization technique, utilizing the response surface method (RSM), to enable reliable prediction and rapid optimization of aerodynamic performance. A parametric modeling method is devised to generate blade profiles with continuous curvature throughout the automated optimization process. The Box–Behnken experimental design, in combination with Reynolds-averaged Navier–Stokes numerical calculation, is employed to construct a second-order polynomial RSM approximation model. The optimization process comprises two levels: improving the through-flow capability and reducing blade profile loss. The optimized profile, Opt1, achieves a substantial 13.19% improvement in through-flow capability at the cost of a 12.27% increase in blade profile loss. Thus, further optimization is performed to minimize blade profile loss based on the Opt1 scheme. Geometric constraints are applied to the most influential parameters affecting through-flow capability to mitigate their impacts. Compared to the Opt1, the final optimized profile, Opt2, achieves a substantial 31.83% decrease in blade profile loss with a negligible sacrifice of 0.23% in through-flow capability.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Reliability Optimization of Traffic Routing in Train Communication Networks Based on Faulty Link Diagnosis

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement.
 Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. 
 Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution.
 Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

Analysis of Dynamic Wear Characteristics of Joint Contact Friction Pair of Excavators Working Device

The working device of an excavator in construction machinery is prone to damage and wear under ordinary working conditions. Based on a model of an excavator under typical working conditions, the dynamic load-bearing situation of the three main joint friction subsets of the working device is simulated by using the virtual prototype technology; the location of the functional device with high stress is identified based on finite element analysis, and the correctness of the simulation results is verified by designing strain gauges. Based on this, the dynamic contact stress variation law of the contact surface of the end-face friction subsets was explored, and the end-face wear depth was calculated by combining Archard wear theory and finite element wear simulation technology; the specimens were worn on the end-face wear tester, and the surface wear was observed under the scanning electron microscope to summarize the wear mechanism and analyze the element content changes of the worn surface. The results show that the three main joints of the working device produce large dynamic fluctuations and are prone to wear, and the destructive degree is more prominent; the wear process is accompanied by higher temperatures, fatigue wear, and abrasive wear on the wear surface, and the wear depth value of the right end face is significantly larger than that of the left end face. This method has a significant reference value for reliability analysis and optimization improvement when using construction machinery’s main joint friction pairs.

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

AbstractThe combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

Remote island renewable transition potential: Affordable, reliable and sustainable generation optimisation for Mornington island

Remote islands, comprising over one-sixth of the Earth's surface area and home to approximately 9% of the global population, face formidable challenges in securing affordable, sustainable, and reliable energy. This paper presents a pioneering investigation into Mornington Island's transition from diesel reliance to renewable energy predominance over the next four decades. By demonstrating the tangible benefits of renewable energy implementation on Mornington Island, this research provides compelling simulated evidence that blending traditional and renewable energy sources can revolutionize energy provision for small island communities. Employing hybrid Wind-Solar renewable energy systems bolstered by an effective battery storage system (ESS), this innovative approach ensures a seamless shift to renewable energy, resilient against seasonal variations and extreme weather events such as cyclones. Our analysis, conducted through a tech-economic model simulating each 5% increment of renewable energy penetration, reveals that renewable energy outperforms traditional diesel generation in terms of affordability over a 40-year operational span. Specifically, a 95% renewable energy penetration yields the lowest levelized energy cost ($162.2/MWh), resulting in a remarkable $8.54 million reduction in diesel costs. A 5% diesel component secures annual energy supply, bridging the gap during periods of seasonal renewable energy variability and extreme cyclonic weather. While achieving 100% renewable energy generation is financially feasible, challenges arise in scaling battery capacity to stabilize energy supply during cyclone seasons. Moreover, our carbon accounting model indicates that although the construction of renewable energy infrastructure entails some indirect (Scope 3) carbon emissions, a 95% renewable penetration mitigates emissions by 90% compared to traditional diesel generation, amounting to a reduction of 39.17 kilotons over the 40-year period. This comprehensive study provides policymakers with invaluable insights, fostering a holistic understanding of the financial, technical, environmental, and political dimensions inherent in island energy transitions.

Leakage and Thermal Reliability Optimization of Stacked Nanosheet Field-Effect Transistors with SiC Layers.

In this work, we propose a SiC-NSFET structure that uses a PTS scheme only under the gate, with SiC layers under the source and drain, to improve the leakage current and thermal reliability. Punch-through stopper (PTS) doping is widely used to suppress the leakage current, but aggressively high PTS doping will cause additional band-to-band (BTBT) current. Therefore, the bottom oxide isolation nanosheet field-effect transistor (BOX-NSFET) can further reduce the leakage current and become an alternative to conventional structures with PTS. However, thermal reliability issues, like bias temperature instability (BTI), hot carrier injection (HCI), and time-dependent dielectric breakdown (TDDB), induced by the self-heating effect (SHE) of BOX-NSFET, become more profound due to the lower thermal conductivity of SiO2 than silicon. Moreover, the bottom oxide will reduce the stress along the channel due to the challenges associated with growing high-quality SiGe material on SiO2. Therefore, this method faces difficulties in enhancing the mobility of p-type devices. The comprehensive TCAD simulation results show that SiC-NSFET significantly suppresses the substrate leakage current compared to the conventional structure with PTS. In addition, compared to the BOX-NSFET, the stress reduction caused by the bottom oxide is avoided, and the SHE is mitigated. This work provides significant design guidelines for leakage and thermal reliability optimization of next-generation advanced nodes.

Super-optimality and relative distance coding in location memory.

The prevailing model of landmark integration in location memory is Maximum Likelihood Estimation, which assumes that each landmark implies a target location distribution that is narrower for more reliable landmarks. This model assumes weighted linear combination of landmarks and predicts that, given optimal integration, the reliability with multiple landmarks is the sum of the reliabilities with the individual landmarks. Super-optimality is reliability with multiple landmarks exceeding optimal reliability given the reliability with each landmark alone; this is shown when performance exceeds predicted optimal performance, found by aggregating reliability values with single landmarks. Past studies claiming super-optimality have provided arguably impure measures of performance with single landmarks given that multiple landmarks were presented at study in conditions with a single landmark at test, disrupting encoding specificity and thereby leading to underestimation in predicted optimal performance. This study, unlike those prior studies, only presented a single landmark at study and the same landmark at test in single landmark trials, showing super-optimality conclusively. Given that super-optimal information integration occurs, emergent information, that is, information only available with multiple landmarks, must be used. With the target and landmarks all in a line, as throughout this study, relative distance is the only emergent information available. Use of relative distance was confirmed here by finding that, when both landmarks are left of the target at study, the target is remembered further right of its true location the further left the left landmark is moved from study to test.

Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater.

Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.

A Decoupling Algorithm for Efficient Estimation of Failure Probability Function Based on Statistical Moment Functions

Abstract Failure probability function (FPF) is an important index that reflects the influence of designable distribution parameters on the safety degree of a structure, and it can be used for decoupling reliability optimization models. Thus, its efficient solution is expected. A decoupling algorithm based on statistical moment functions (SMFs) of performance function is proposed to solve the FPF efficiently in this paper. The proposed algorithm first constructs an extended density weight function (EDWF), which can cover the interested region of the distribution parameters and is independent of the distribution parameters so that the statistical moment integrals corresponding to different realizations of the distribution parameters can share the same EDWF. Then, using the same EDWF, a strategy is dexterously designed to estimate the SMFs by sharing a set of integral characteristic nodes. Finally, the FPF is approximated by the SMFs, which varies with the distribution parameters in the interested design region. In addition, the proposed algorithm introduces the Box–Cox transformation of the performance function to guide the high accuracy of FPF approximated by the SMFs. The main contribution of the proposed algorithm is constructing the EDWF to decouple the dependence of solving SMFs on the realizations of the distribution parameters over the interested region and designing the strategy of estimating the SMFs by sharing the same integral characteristic nodes. Since the proposed algorithm employs a point estimation method to evaluate the FPF, it has higher efficiency than the competitive methods. Numerical and engineering examples demonstrate the superiority of the proposed algorithm.

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method

Multi-level optimization strategies for large-scale nonlinear process systems

With growing needs to develop and improve climate-friendly processes, optimization strategies are essential at all levels of decision-making in chemical and energy processes, including process development, process synthesis and design, as well as process operations, control, scheduling, and planning. Challenges include the formulation of well-posed and well-conditioned process models, and development and application of efficient, reliable optimization algorithms. Here we describe a synthesis of optimization concepts and algorithms that enable large-scale nonlinear programming, nonintrusive decomposition strategies and the inclusion of a wide class of surrogate models. All of these are crucial to address challenging nonconvex, multi-scale problems in Computer Aided Process Engineering (CAPE). These elements are demonstrated through dynamic optimization strategies for novel energy generation, demand-based optimization for specialty chemicals, and optimization with integrated heterogeneous models for carbon capture processes.

Performance analysis of electrical signal output of multi-state flexoelectric structures with parameter uncertainties through quasi-Monte Carlo method

Flexoelectric effect is a more universal electromechanical coupling effect than piezoelectric effect. Flexoelectric beams as the main structural component of flexoelectric power signal output have broad application prospects in the next generation of micro–nano electromechanical systems. However, the electrical signal output of flexoelectric structures in macro-scale is far less than the output of the piezoelectric signal. Therefore, it is urgent to explore the influence of the parameter uncertainties on the electrical signal output of the flexoelectric structures, in order to improve the electrical signal output of flexoelectric materials with excellent design performance. Based on the quasi-static theory, the output voltage model and the output charge model of flexoelectric structures as well as the effective piezoelectric coefficient model are constructed. Then the influences of the flexoelectric parameters on the output voltage and the output charge are researched as well as the influence of the effective piezoelectric coefficient. Finally, the influences of uncertain parameters under different electrical states (e.g. the electrical open circuit and short circuit states) on the output performance of flexoelectric signal are studied by the quasi-Monte Carlo method, in order to further provide a reference for the reliability analysis and optimization design of the flexoelectric structures.

Bottom-up stochastic multiscale model for the mechanical behavior of multidirectional composite laminates with microvoids

This study for the first time develops a coherent and efficient bottom-up stochastic multiscale modeling approach, which aims at revealing the impact of microvoids on the mechanical properties and damage mechanisms of multidirectional (MD) carbon fiber-reinforced polymer (CFRP) composites. To this end, void defects within the composites are characterized using X-ray micro-computed tomography to construct a high-fidelity representative defective volume (RDV). Subsequently, the influence of voids on the microscale failure modes and macroscopic mechanical properties are analyzed by using micromechanics-based finite element (FE) method. Finally, a stochastic multiscale analysis method is developed to predict the mechanical response of MD CFRP laminates and validated by tensile and three-point bending experiments. The results indicate that the proposed framework can accurately predict the mechanical behavior and damage mechanisms of MD CFRP composites. This framework is believed to be a promising method to provide valuable guidance for reliability design and optimization of CFRP composites.

Constrained multi-objective trans-media maneuver trajectory optimization for morphing unmanned aerial-underwater vehicles

In this study, the challenge of optimizing trans-media maneuver trajectories is addressed through the introduction of a Constrained Multi-Objective Grey Wolf Optimizer (CMOGWO). The objective function has been refined to assess solutions more accurately based on their feasibility and adherence to constraints. Three distinct Archives have been employed for categorizing and optimizing dominant, non-dominant, and feasible non-dominant solutions, each benefiting from specifically tailored optimization strategies. A hybrid search method, complemented by a watch-based strategy, is proposed for effectively guiding the population towards the Pareto front, ensuring the discovery of potential optimal solutions. Investigations have been conducted into the impact of initial altitude on trajectory optimization, with comparative simulations revealing the effectiveness of the proposed CMOGWO in managing unmanned aerial-underwater vehicle trajectories. The results demonstrate the potential of the CMOGWO in enhancing the reliability and feasibility of trajectory optimization in complex environmental conditions.

Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Extreme disasters have become increasingly common in recent years and pose significant dangers to the integrated energy system’s secure and dependable energy supply. As a vital part of an integrated energy system, the energy storage system can help with emergency rescue and recovery during major disasters. In addition, it can improve energy utilization rates and regulate fluctuations in renewable energy under normal conditions. In this study, the sizing scheme of multi-energy storage equipment in the electric–thermal–hydrogen integrated energy system is optimized; economic optimization in the regular operating scenario and resilience enhancement in extreme disaster scenarios are also considered. A refined model of multi-energy storage is constructed, and a two-layer capacity configuration optimization model is proposed. This model is further enhanced by the integration of a Markov two-state fault transmission model, which simulates equipment defects and improves system resilience. The optimization process is solved using the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm to provide reliable and accurate optimization results. The results indicate that addressing severe disaster situations in a capacity configuration fully leverages the reserve energy function of energy storage and enhances system resilience while maintaining economic efficiency; furthermore, adjusting the load loss penalty coefficients offers a more targeted approach to the balancing of the system economy and resilience. Thus, new algorithmic choices and planning strategies for future research on enhancing the resilience of integrated energy systems under extreme disaster scenarios are provided.

Integrated CPU Monitoring Using 2D Temperature Sensor Arrays Directly Printed on Heat Sinks

AbstractIn today's digital world, the demand for computer security and system reliability is a crucial element. Monitoring the CPU temperature during operation provides valuable insights but is currently limited to the embedded on‐chip sensors. The implementation of an extra security layer based on temperature monitoring can detect anomalies in an early stage, identify malware, and help mitigate attacks. The approach of integrating more on‐chip temperature sensors into the silicon is avoided due to space, power limitations, and cost constraints. However, the field of printed electronics and sensor technology has recently seen significant progress. This is the first work that introduces a fully‐printed temperature sensor array integrated onto the cooling unit of a CPU. It offers a novel approach for the practical implementation of such sensors into an operational computer system. The present sensor array, consisting of 396 sensor pixels, detects local hotspots induced by the underneath CPU cores, leading to an individual thermal fingerprint. This unique method paves the way for addressing pivotal elements of computer security, as deviations from expected thermal behavior can signal malicious activities. Additionally, this innovation facilitates reliability optimization. The detailed thermal map garnered provides insights into which cores are subjected to significant stress over time.

Market-based Estimation of Average Electricity Outage Costs in the United States

Electricity outage cost (EOC) estimates ($ per kWh unserved) are essential input data for optimal reliability planning and efficient pricing of electricity services. Based on the 2019-2020 market data published by two US government agencies for the lower 48 states, this paper’s EOC estimates by census region and year are median values of $1.39 to $2.93 per kWh unserved, well below the estimate of $9 per kWh unserved adopted by Texas for optimal reliability planning. The policy implications of adopting our lower EOC estimate are (a) a reduction in an electric grid’s optimal planning reserve to improve the grid’s cost efficiency; and (b) a decline in the grid’s marginal cost-based retail price to encourage welfare-enhancing end-use consumption.