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Reduction of gear pair transmission error with tooth profile modification
The gear noise problem that widely occurs in power transmission systems is typically characterised by one or more high amplitude acoustic signals. The noise originates from the vibration of the gear pair system caused by transmission error excitation that arises from tooth profile errors, misalignment and tooth deflections. This paper aims to further research the effect of tooth profile modifications on the transmission error of gear pairs. A spur gear pair was modelled using finite elements, and the gear mesh was simulated and analysed under static conditions. The results obtained were used to study the effect of intentional tooth profile modifications on the transmission error of the gear pair. A detailed parametric study, involving development of an optimisation algorithm to design the tooth modifications, was performed to quantify the changes in the transmission error as a function of tooth profile modification parameters as compared to an unmodified gear pair baseline
Reliability based robust design optimization based on sensitivity and elasticity factors analysis
In this paper, a Reliability Based Robust Design Optimization (RBRDO) based on sensitivity and elasticity factors analysis is presented. In the first step, a reliability assessment is performed using the First-and Second Order Reliability Method (FORM)/ (SORM), and Monte Carlo Simulation. Furthermore, FORM method is used for reliability elasticity factors assessment, which can be carried out to determine the most influential parameters, these factors can be help to reduce the size of design variables vector in RBRDO process. The main objective of the RBRDO is to improve both reliability and design of a cylindrical gear pair under uncertainties. This approach is achieved by integration of two objectives which minimize the variance and mean values of performance function. To solve this problem a decoupled approach of Sequential Optimization and Reliability Assessment (SORA) method is implemented. The results obtained shown that a desired reliability with a robust design is progressively achieved
Time-Staging Enhancement of Hybrid System Falsification
Optimization-based falsification employs stochastic optimization algorithms
to search for error input of hybrid systems. In this paper we introduce a
simple idea to enhance falsification, namely time staging, that allows the
time-causal structure of time-dependent signals to be exploited by the
optimizers. Time staging consists of running a falsification solver multiple
times, from one interval to another, incrementally constructing an input signal
candidate. Our experiments show that time staging can dramatically increase
performance in some realistic examples. We also present theoretical results
that suggest the kinds of models and specifications for which time staging is
likely to be effective
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Evolutionary optimization within an intelligent hybrid system for design integration
An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail
Conformance Testing as Falsification for Cyber-Physical Systems
In Model-Based Design of Cyber-Physical Systems (CPS), it is often desirable
to develop several models of varying fidelity. Models of different fidelity
levels can enable mathematical analysis of the model, control synthesis, faster
simulation etc. Furthermore, when (automatically or manually) transitioning
from a model to its implementation on an actual computational platform, then
again two different versions of the same system are being developed. In all
previous cases, it is necessary to define a rigorous notion of conformance
between different models and between models and their implementations. This
paper argues that conformance should be a measure of distance between systems.
Albeit a range of theoretical distance notions exists, a way to compute such
distances for industrial size systems and models has not been proposed yet.
This paper addresses exactly this problem. A universal notion of conformance as
closeness between systems is rigorously defined, and evidence is presented that
this implies a number of other application-dependent conformance notions. An
algorithm for detecting that two systems are not conformant is then proposed,
which uses existing proven tools. A method is also proposed to measure the
degree of conformance between two systems. The results are demonstrated on a
range of models
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
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