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A probabilistic‐based design for turbine disk at high temperature can quantify risk and thus identify areas of possible overdesign (conservatism). Moreover, the need for cost‐effective designs has resulted in the development of probabilistic design to quantify the effects of these uncertainties so as to improve the reliability of the component. This paper aims to address these issues.

The flow for probabilistic design established through investigating traditional design methods of the turbine disk at high temperature is divided into the processes of crack initiation and crack growth to find important design inputs at each course, where the probabilistic design criterion has been built based on the deterministic criteria and successful experiences.

The probabilistic‐based design procedure has been demonstrated by taking the reliability design of crack initiation process for turbine disk as the example. The reliability analysis for the disk life after optimization analysis was completed by considering random parameters reflecting the uncertainties. The results showed there was a margin in design for disk life referred to as the probabilistic criterion. This measure was taken by redesigning the structure to reduce the disk's weight within the range of reliability.

The present study provides a method to design aero‐engine components based on probabilistic design for further research.

Moreover, the present study provides a way to design structures based on probabilistic design.

It is proved that probabilistic‐based design could produce a lower weight turbine disk by integrating well‐proved deterministic design methods and tools with probabilistic design techniques while maintaining low failure probability.

This paper aims to reasonably quantify the radial deformation of turbine blade from a probabilistic design perspective. A probabilistic design for turbine blade radial deformation considering non-linear dynamic influences can quantify risk and thus control blade tip clearance to further develop the high performance and high reliability of aeroengine. Moreover, the need for a cost-effective design has resulted in the development of probabilistic design method with high computational efficiency and accuracy to quantify the effects of these uncertainties.

An extremum response surface method-based support vector machine (SVM-ERSM) was proposed based on SVM of regression to improve the computational efficiency and precision of blade radial deformation dynamic probabilistic design regarding non-linear material properties and dynamically thermal and mechanical loads.

Through the example calculation and comparison of methods, the results show that the blade radial deformation reaches at the maximum at t = 180 s; the probabilistic distribution and inverse probabilistic features of output parameters and the major factors (rotor speed and gas temperature) are gained; besides, the SVM-ERSM holds high computational efficiency and precision in the non-linear dynamic probabilistic design of aeroengine typical components.

The present efforts provide a method to design turbine besides other aeroengine components considering dynamic and non-linear factors base on probabilistic design for further research.

Moreover, the present study provides a way to design dynamic (motion) structures from a probabilistic perspective.

It is proved that the dynamic probabilistic design-based SVM-ERSM could produce a more reasonable blade radial deformation while maintaining low failure probability, as well as offer a useful reference for blade-tip clearance control and a promising insight to the optimal design of aeroengine typical components.

The purpose of this paper is to report a study in which the feasibility of conducting probabilistic finite element analysis (FEA) for crane hook design has been explored.

This paper presents the results of probabilistic analysis, where in the input random variables are varied and corresponding variations in the output parameters were observed. In this study, material properties and load have been considered as random input variables and the maximum stress, maximum deflection variations were considered as output random variables.

The probability of occurrence of output variation and the sensitivity of output variables on the input variables are the important results generated from this analysis. By performing this probabilistic analysis, the random selection of factor of safety could be avoided.

The implementation study has been carried out for a single product.

The usage of the approach will indicate the importance of probabilistic analysis in product design and development process. This process will enable the organization to compete in the global market.

A case study has been reported to indicate the feasibility of performing probabilistic FEA for crane hook design. Hence, the contributions are original.

The purpose of this paper is to present the probabilistic approach to a new robustness-based design strategy for thin-walled composite structures in post-buckling.

Because inherent uncertainties in geometry, material properties, ply orientation and thickness affect the structural performance and robustness, these variations are taken into account.

The methodology is demonstrated for the sake of simplicity with an unstiffened composite plate under compressive loading, and the probabilistic and deterministic results are compared. In this context, the structural energy and uncertainties are employed to investigate the robustness and reliability of thin-walled composite structures in post-buckling.

As practical implication, the methodology can be extended to stiffened shells, widely used in aerospace design with the aim to satisfy weight, strength and robustness requirements. Moreover, a new argument is strengthened to accept the collapse close to ultimate load once robustness is ensured with a required reliability.

This innovative strategy embedded in a probabilistic framework might lead to a different design selection when compared to a deterministic approach, or an approach that only accounts for the ultimate load. Moreover, robustness measures are redefined in the context of a probabilistic design.

In a conventional finite element analysis, material properties, dimensions and applied loads are usually defined as deterministic quantities. This simplifying assumption however, is not true in practical applications. Using statistics in engineering problems enables us to consider the effects of the input variables dispersion on the output parameters in an analysis. This provides a powerful tool for better decision making for more reliable design. In this paper, a probabilistic based design is presented which evaluates the sensitivity of a mechanical model to random input variables. To illustrate the effectiveness of this method, a simple bracket is analyzed for stress‐strain behavior using commercially available finite element software. Young’s modulus, applied pressure and dimensions are considered as random variables with Gaussian distribution and their effects on maximum stress and displacement is evaluated. The finite element results are compared with reliability based theoretical results which show very good agreement. This demonstrates the capability of commercially available software to handle probabilistic approach design.

Fluid flow in a circular pipe and a slider bearing was computationally simulated by finite element methods and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for the flow rate and load bearing capacity of the slider bearing due to the several random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in fluid flow and to the identification of both the most critical measurements and parameters.

This paper aims to investigate the state of the art for the reliability evaluation of reinforced concrete beams in a fire situation. Special emphasis is placed on addressing which parameters were considered probabilistically or deterministically, the prescribed probabilistic models for the assumed stochastic variables, the treatment of the heat transfer mechanism, the quantification of the structural fire performance and the assumed target reliability levels.

Research papers were identified through a search on the Web of Science, Google Scholar and detailed searches within the journals Journal of Structural Fire Engineering, Fire Technology and Fire Safety Journal, supplemented with references known by the authors.

Considering the state-of-the-art review, gaps in the literature are identified related to (1) the probabilistic evaluation of shear capacity for standard fires and parametric fires, and bending capacity for parametric fires, (2) the absence of reference fragility curves for immediate design application/code calibration and (3) the specification of target safety levels for reliability-based design.

The lack of research papers gathering studies on the reliability of reinforced concrete beams in fire situation makes it difficult to further develop research in the area. The value of this work lies precisely in the collection of the basic information, making it possible to identify gaps to be addressed in future research and the suggestion of a research framework.

To survey the approaches to design optimization based on possibility theory and evidence theory comparatively, as well as their prominent characteristics mainly for epistemic uncertainty.

Owing to uncertainties encountered in engineering design problems and limitations of the conventional probabilistic approach in handling the impreciseness of data or knowledge, the possibility‐based design optimization (PBDO), evidence‐based design optimization (EBDO) and their integrated approaches are investigated from the viewpoints of computational development and performance improvement. After that, this paper discusses the fusion technologies and an example of integrated approach in reliability to reveal the qualitative value and efficiency.

It is recognized that more conservative results are obtained with both PBDO and EBDO, which may be appropriate for design against catastrophic failure compared with the probability‐based design. Furthermore, the EBDO design may be less conservative compared with the PBDO design.

How to perfect already‐existing integration approaches in a more generally analytical framework is still an active domain of research.

The paper is a holistic reference for design engineers and industry managers.

The paper is focused on decomposition strategies and fusion technologies, especially addressing epistemic uncertainty for large‐scale and complex systems when statistical data are scarce or incomplete.

Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk.

A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence.

The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing.

The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.

The work set out to design and develop an overheating risk tool using the UKCP09 climate projections that is compatible with building performance simulation software. The aim of the tool is to exploit the Weather Generator and give a reasonably accurate assessment of a building's performance in future climates, without adding significant time, cost or complexity to the design team's work.

Because simulating every possible future climate is impracticable, the approach adopted was to use principal component analysis to give a statistically rigorous simplification of the climate projections. The perceptions and requirements of potential users were assessed through surveys, interviews and focus groups.

It is possible to convert a single dynamic simulation output into many hundreds of simulation results at hourly resolution for equally probable climates, giving a population of outcomes for the performance of a specific building in a future climate, thus helping the user choose adaptations that might reduce the risk of overheating. The tool outputs can be delivered as a probabilistic overheating curve and feed into a risk management matrix. Professionals recognized the need to quantify overheating risk, particularly for non‐domestic buildings, and were concerned about the ease of incorporating the UKCP09 projections into this process. The new tool has the potential to meet these concerns.

The paper is the first attempt to link UKCP09 climate projections and building performance simulation software in this way and the work offers the potential for design practitioners to use the tool to quickly assess the risk of overheating in their designs and adapt them accordingly.