Papers by Keyword: Uncertainty

Abstract: The Churches of the Broumov region are well known for their unique baroque architecture, distinct shapes, sizes, and constitutes an integral part of the Czech cultural heritage. The St. Barbara’s Church that has been studied in this article, is in the Otovice village of Broumov. It was built in the year 1726 by Bavarian architects Christoph Dientzenhofer and Kilian Ignaz and is significant because of its religious, artistic and historic values. The main objective of this study is to evaluate the structural safety and stability of St. Barbara’s Church based on a probabilistic approach. A deterministic assessment of the structure is carried out and the results are assessed concerning the present site condition. Depending upon the observed damages, a condition for failure is defined for the structure. The uncertainties in the material parameters are considered and reliability analysis is performed to determine the reliability index, probability of failure and influence of different material parameters in the structural stability.

Abstract: In this paper, the Reliability Analysis with utilizing a Monte Carlo simulation (MCS) process was conducted on the equation of the collapse potential predicted by ANN to study its reliability when utilized in a situation of soil that has uncertainty in its properties. The prediction equation utilized in this study was developed previously by the authors. The probabilities of failure were then plotted against a range of uncertainties expressed in terms of coefficient of variation. As a result of reliability analysis, it was found that the collapse potential equation showed a high degree of reliability in case of uncertainty in gypseous sandy soil properties within the specified coefficient of variation (COV) for each property. When the COV ranges (0-100) for each soil properties under study, it was found also that the collapse potential equation is very well in predicting the collapse potential of gypseous sandy soils for all values of the COV lies between (0-100) % for initial water content and degree of saturation, and for values of the COV not exceed 11%, 19% for the initial dry unit weight and specific gravity respectively, as well as for the values of the COV not exceed 80%, 97% for the initial voids ratio and gypsum content respectively.

Abstract: Environmentally assisted cracking (EAC) of nickel-based alloys is one of the most significant potential safety hazards in the primary circuit of nuclear power plants. To understand the influence of randomness on micro-mechanical state at tip of EAC, Latin hypercube sampling method is applied to analyze the uncertainty of stress-strain in the oxide film at the EAC tip considering the uncertainties of load and material properties of base metal and oxide film. Meanwhile, to improve the efficiency of numerical analysis, MATLAB is employed in the secondary development for ABAQUS. With the help of finite element numerical simulation and Latin hypercube sampling method, the uncertainty of mechanical properties at tip of EAC in one-inch compact tension specimen is simulated and analyzed in this study. The results show that the randomness of material properties and load markedly affect the uncertainty of micro-mechanical state. Among the variables, The randomness of load has the greatest influence on uncertainty of strain, and Poisson`s ratio of oxide film is the smallest effect.

Abstract: The task of tolerance analysis usually addresses the question of the mechanical mountability of an assembly. We extend this viewpoint when talking about directional control valves in a crossdomain tolerance analysis; an analysis whose task is to determine the possible variation in the key product characteristics induced by a specific tolerance concept. As the available information about the noise factors to be toleranced is almost always imperfect generalised methods for their representation and the propagation of their impact on the key product characteristics are required. In this study the capabilities and potentials of belief and plausibility measures as well as fuzzy random variables are compared to traditional worst-case and statistical tolerance analysis.

Abstract: Ontologies represent inter-related semantic information. The automated integration of new knowledge helps to detect and reduce data-induced conflicts and model-based uncertainty in ontologies. However, automatic extension of an existing ontology from heterogeneous distributed sources can often lead to incomplete and contradictory entities. In order to resolve these conflicts and to complete entities, inductive inference mechanisms should be applied in addition to the deductive mechanisms already in use. This paper first describes various inductive inference mechanisms and compares these with each other according to pre-defined requirements and other criteria. Finally, the mechanisms’ suitability for an information model for the exchange and visualization of uncertainty in load-carrying systems and possible combinations of the individual mechanisms are discussed, also with respect to the necessity of further modifications of these mechanisms.

Abstract: Fine machining processes are of great importance in automotive series production, e.g. the machining of valve guide and seat in the cylinder head of a combustion engine. In industrial manufacturing processes, disturbances are inevitable and provide a measure of uncertainty in each production step. Increasingly, the influence of such uncertainties is being evaluated using simulation models. In this paper, a modeling approach for simulation of multi-stage fine machining processes with step tools is presented and investigations regarding influence of uncertainty caused by disturbances are performed.

Abstract: The reaming process normally takes place at the end of manufacturing processes when a lot of value has already been added. Therefore, reaming plays an important role for the quality of the finished product. To achieve this high quality, the occurring process errors caused by the machine tool and the reamer or incorrect workpiece handling have to be minimised. Measured data of the reaming process allow the prediction of occurring process errors without the need to evaluate the bore with a coordinate measuring machine. However, manufacturing is already completed at this stage and the correction of errors is either no longer possible or very costly. This paper presents an approach to detect axis offsets within the entry phase of the reamer by analysing the process forces. The calculated offset is then compensated by adjusting the nominal value of the motion control.

Abstract: Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.

Abstract: High-rise water supply systems provide water flow and suitable pressure in all levels of tall buildings. To design such state-of-the-art systems, the consideration of energy efficiency and the anticipation of component failures are mandatory. In this paper, we use Mixed-Integer Nonlinear Programming to compute an optimal placement of pipes and pumps, as well as an optimal control strategy.Moreover, we consider the resilience of the system to pump failures. A resilient system is able to fulfill a predefined minimum functionality even though components fail or are restricted in their normal usage. We present models to measure and optimize the resilience. To demonstrate our approach, we design and analyze an optimal resilient decentralized water supply system inspired by a real-life hotel building.

Abstract: Mathematical models of a suspension strut such as an aircraft landing gear are utilized by engineers in order to predict its dynamic response under different boundary conditions. The prediction of the dynamic response, for example the external loads, the stress and the strength as well as the maximum compression in the spring-damper component aids engineers in early decision making to ensure its structural reliability under various operational conditions. However, the prediction of the dynamic response is influenced by model uncertainty. As far as the model uncertainty is concerned, the prediction of the dynamic behavior via different mathematical models depends upon various factors such as the model's complexity in terms of the degrees of freedom, material and geometrical assumptions, their boundary conditions and the governing functional relations between the model input and output parameters. The latter can be linear or nonlinear, axiomatic or empiric, time variant or time-invariant. Hence, the uncertainty that arises in the prediction of the dynamic response of the resulting different mathematical models needs to be quantified with suitable validation metrics, especially when the system is under structural risk and failure assessment. In this contribution, the authors utilize the Bayesian interval hypothesis-based method to quantify the uncertainty in the mathematical models of the suspension strut.