Optimization of the installation sequence for the temporary fasteners in the aircraft industry

Riveting and bolting are common assembly methods in aircraft production. The fasteners are installed immediately after hole drilling and fix the relative tangential displacements of the parts, that took place. A proper fastener sequence installation is very important because a wrong one can lead to a “bubble-effect”, when gap between parts after fastening becomes larger in some areas rather than being reduced. This circumstance affects the quality of the final assembly. For that reason, the efficient methods for determination of fastening sequence taking into account the specifics of the assembly process are needed. The problem is complicated by several aspects. First of all, it is a combinatorial problem with uncertain input data. Secondly, the assembly quality evaluation demands the time-consuming computations of the stress-strain state of the fastened parts caused by sequential installation of fasteners. Most commonly used strategies (heuristic methods, approximation algorithms) require a large number of computational iterations what dramatically complicates the problem. The paper presents the efficient methods of fastener sequence optimization based on greedy strategy and the specifics of the assembly process. Verification of the results by comparison to commonly used installation strategies shows its quality excellence.

The Impact of the Uncertain Input Data of Multi-Purpose Reservoir Volumes under Hydrological Extremes

The topic of uncertainties in water management tasks is a very extensive and highly discussed one. It is generally based on the theory that uncertainties comprise epistemic uncertainty and aleatoric uncertainty. This work deals with the comprehensive determination of the functional water volumes of a reservoir during extreme hydrological events under conditions of aleatoric uncertainty described as input data uncertainties. In this case, the input data uncertainties were constructed using the Monte Carlo method and applied to the data employed in the water management solution of the reservoir: (i) average monthly water inflows, (ii) hydrographs, (iii) bathygraphic curves and (iv) water losses by evaporation and dam seepage. To determine the storage volume of the reservoir, a simulation-optimization model of the reservoir was developed, which uses the balance equation of the reservoir to determine its optimal storage volume. For the second hydrological extreme, a simulation model for the transformation of flood discharges was developed, which works on the principle of the first order of the reservoir differential equation. By linking the two models, it is possible to comprehensively determine the functional volumes of the reservoir in terms of input data uncertainties. The practical application of the models was applied to a case study of the Vír reservoir in the Czech Republic, which fulfils the purpose of water storage and flood protection. The obtained results were analyzed in detail to verify whether the reservoir is sufficiently resistant to current hydrological extremes and also to suggest a redistribution of functional volumes of the reservoir under conditions of measurement uncertainty.

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

This paper is devoted to determination of elastic properties of composite constituents by using an inverse identification procedure. The aim of the developed identification procedure is to compute the elastic constants of individual material phases on the basis of known properties of composite materials. The inverse problem of identification has been solved by combining an evolutionary algorithm with a micromechanical model. The paper also focuses on selection of a suitable micromechanical model for optimization which should ensure a compromise between accuracy and complexity. Two different cases have been studied: composite reinforced with short cylindrical fibers and composite reinforced with cubic particles. Moreover, Monte Carlo simulations have been carried out to expose a difference in outcome of identification which may occur when uncertain input data is considered. Obtained results show that identification is successful only when properties of composite materials with at least two different volume fractions of the reinforcement are known.

Optimal Booster Station Design and Operation under Uncertain Load

Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the system's resilience can be engineered.

CALCULATION OF THE LABOR CONSUMPTION RATE FOR SHUTTERING WORKS WHILST CONSIDERING UNCERTAINTIES

Calculating construction costs and times is one of the most important and demanding tasks in construction management and economics. To arrive at a realistic calculation base, valid data and information is constantly being sought for labor consumption rates, output rates, productivity, material consumption, volumes in stock, number of transport cycles, and cost and time parameters that must be estimated or calculated ex ante. Ultimately, final cost and time parameters must be determined on the basis of such considerations and calculations. Accurate figures must be stated or submitted at the end of any analysis. These depend on the complexity of the building and on the conditions prevailing at the actual work stages and rely on more or less uncertain input data. One possible solution to this issue is to consider ranges that can deliver final conclusions on determined values. To systematically consider ranges in input parameters, this paper concentrates on applying probabilistic calculation methods based on Monte Carlo simulations. Key outcomes of probabilistic calculations include histograms that are used to directly capture the chance/risk ratio relative to a specific (selected) parameter. This paper presents a practical example of calculating the labor consumption rate for shuttering works to highlight the significance of the chosen chance/risk ratio and to show how it can be integrated into the systematic decision-making process adopted by the parties involved in the project.

Design and Implementation of a Fuzzy Inference Model for Mapping the Sustainability of Energy Crops

Energy crops are positioned as the most promising renewable energy sources. Over recent years, the use of biomass has been growing significantly, especially in countries that have made a strong commitment to renewable sources in their energy policies. One of the aspects of the use of biomass for energy is that it is still controversial with regard to full environmental sustainability. Unfortunately, the existing environmental evaluation tools in many cases are unable to manage uncertain input data. Fuzzy-set-based methods, instead, have proved to be able to deal with uncertainty in environmental topics. The idea of this chapter is to reproduce a solution by decoding it from the domain of knowledge with the calculus of fuzzy “if-then” rules. A methodology based on Fuzzy Inference Systems (FIS) is proposed to assess the environmental sustainability of biomass.

A Mathematical Approach to Estimate the Error During Calculating the Smoke Layer Height in Industrial Facilities

Engineering based calculation procedures in fire safety science often consist of unknown or uncertain input data which are to be estimated by the engineer using appropriate and plausible assumptions. Thereby, errors in this data are induced in the calculation and thus, impact the number as well as the reliability of the results. In this paper a procedure is presented to directly quantify and consider unknown input properties in the process of calculation using distribution functions and Monte-Carlo Simulations. A sensitivity analysis reveals the properties which have a major impact on the calculation reliability. Furthermore, the results are compared to the numerical models of CFAST and FDS.

Design and Implementation of a Fuzzy Inference Model for Mapping the Sustainability of Energy Crops

Energy crops are positioned as the most promising renewable energy sources. Over recent years, the use of biomass has been growing significantly, especially in countries that have made a strong commitment to renewable sources in their energy policies. One of the aspects of the use of biomass for energy is that it is still controversial with regard to full environmental sustainability. Unfortunately, the existing environmental evaluation tools in many cases are unable to manage uncertain input data. Fuzzy-set-based methods, instead, have proved to be able to deal with uncertainty in environmental topics. The idea of this chapter is to reproduce a solution by decoding it from the domain of knowledge with the calculus of fuzzy “if-then” rules. A methodology based on Fuzzy Inference Systems (FIS) is proposed to assess the environmental sustainability of biomass.