Dealing with uncertainty in constrained optimisation using decision theory

We consider constrained optimisation problems with a real-valued, bounded objective function on an arbitrary space. The constraints are expressed as a relation between the optimisation variable and the problem parameters. There is uncertainty about these problem parameters, which turns the optimisation problem into an ill-specified problem

Fatigue strengthening of tubular X joint using un-bonded CFRP laminates

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The evolution of particulates across the sooting limit in turbulent premixed opposed jet flames

Soot formation in combustors is a complex process comprising highly intermittent interactions between physical and chemical processes across a wide range of time-scales. The influence of turbulence on the molecular pathways initiating particulate formation remains unquantified. Controlling soot emissions to the atmosphere will require overcoming large gaps in the understanding of soot formation/oxidation especially in turbulent combustion. The complexities of soot formation in turbulent flames suggests that the use of a flexible compact burner configuration with well–defined boundary conditions and precise control of flow characteristics is of significant advantage. The novel back–to–burnt opposed jet configuration features fractal grid generated turbulence and provides accurate control of flow parameters. The study includes the analyses of the overall flame structure of turbulent premixed ethylene/air flames, the relative concentrations of PAHs associated with soot inception and particle size distributions. The experiments covered a series of sooting flame conditions with variations in the equivalence ratio (1.7 ≤ \phi_{UN} ≤ 2.2), the total rate of strain (255 ≤ a_{T} [s−1] ≤ 610) and burnt gas temperature (1400 ≤ T_{LN} [K] ≤ 1700). The conditions traverse the soot inception limit, e.g. the transition from lightly to heavily sooting flames, with non- intrusive ELS and PAH–PLIF combined probe sampling to quantify gaseous and PAH species using GC–TCD and GC–MS, respectively. The probe sampling features comprehensive sampling steps used to provide accurate concentrations of major gaseous, PAH species and particles with minimum losses. It is shown that the rate of strain exerts a substantial influence on both PAH concentrations and soot formation. Hence, it is likely that soot formation in turbulent flames becomes dominated by contributions from low strain regions. It is also found that the stoichiometry of the mixture controls the concentrations of PAHs associated with soot inception. The results obtained clearly show that benzo(a)pyrene is prevalent in flame structures and that relatively large amounts are condensed onto soot particles. A transition between bimodal and unimodal shapes of the particle size distributions shows strong competitions between oxidation, aggregation and surface growth processes in the turbulent flames.Open Acces

Damage detection in structural systems by improved sensitivity of modal strain energy and Tikhonov regularization method

In this article, new methods for detecting damage in structural systems are presented. These methods are categorized as damage localization and damage quantification, respectively. Hence, direct changes of modal strain energy are applied to identify locations of damage. Moreover, some restraints such as incomplete measured modes and simple assumptions in structural modeling may cause failure in the results of damage localization. Therefore, a correlation-based method is utilized to obviate these limitations and precisely detect damage sites. Subsequently, an improved sensitivity of modal strain energy is generated to determine damage severities. To achieve appropriate results in damage quantification, Tikhonov regularization approach is utilized instead of classical methods such as applying penalty function and current inverse problem techniques. Applicability and effectiveness of proposed methods are numerically verified using two practical examples consisting of a planner truss and a portal frame, respectively. Eventually, numerical results indicate that the proposed damage localization approach provides an influential algorithm for precisely identifying damage sites. Furthermore, obtained damage severities show that utilizing the sensitivity of modal strain energy and also solving the damage equation by Tikhonov regularization makes it possible to accurately determine damage extents in the case of incomplete modal data

Damage localization in shear buildings by direct updating of physical properties

The objective of this article is to present a new method for identifying the damage location in a multi-story shear building by direct model updating method. In this regard, structural perturbation matrices should be determined that are directly defined as the discrepancy between mass and stiffness matrices of undamaged and damaged structures. As a result of expanding the dynamic orthogonality conditions, mass and stiffness perturbation matrices are formulated by the initial information of undamaged structures as well as the structure’s modal parameters before and after the occurrence of damages. These matrices cannot easily detect the damage site. Therefore, a more explicit determination of damage location is performed dividing the amount of change in these matrices’ diagonals by the physical properties of undamaged structure. This modification facilitates the damage localization process and yields precise and preferable results in comparison with applying classical methods such as natural frequencies, mode shapes and structural properties changes. Subsequently, the applicability and effectiveness of the proposed damage detection method are verified numerically and experimentally. For numerical verification of the proposed methods, a six-story shear building is utilized as a discrete system. Then, the experimental verification of proposed methods is conducted detecting the location of damages in a simple laboratory frame. It can be deduced that the proposed damage localization method can reliably detect and also localize the structural damage

Optimisation under uncertainty applied to a bridge collision problem

We consider the problem of modelling the load on a bridge pillar when hit by a vehicle. This load depends on a number of uncertain variables, such as the mass of the vehicle and its speed on impact. The objective of our study is to analyse their effect on the load. More specifically, we are interested in finding the minimum distance of the pillar to the side of the road passing under the bridge such that a given constraint on the load is satisfied in 99% of impact cases, i.e., such that the probability of satisfying the constraint is 0.99. In addition, we look for solutions to the following optimisation problem: find the distance that minimises a given cost function while still satisfying a given constraint on the load. This optimisation problem under uncertain constraints is not a well-posed problem, so we turn it into a decision problem under uncertainty. For both problems, we consider two typical cases. In the first, so-called precise-probability case, all uncertain variables involved are modelled using probability distributions, and in the second, so-called imprecise-probability case, the uncertainty for at least some of the variables (in casu the mass) is modelled by an interval of possible values, which is a special imprecise-probabilistic model. In the first case, we compute the joint distribution using simple Monte Carlo simulation, and in the second case, we combine Monte Carlo simulation with newly developed techniques in the field of imprecise probabilities. For the optimisation problem with uncertain constraints, this leads to two distinct approaches with different optimality criteria, namely maximality and maximinity, which we discuss and compare

System architecture for the Canadian interim mobile satellite system

The system architecture for the Canadian Interim Mobile Satellite Service (IMSS) which is planned for commencement of commercial service in late 1989 is reviewed. The results of an associated field trial program which was carried out to determine the limits of coverage and the preliminary performance characteristics of the system are discussed

Improving feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

Feature extraction by time series modeling based on statistical pattern recognition is a powerful approach to Structural Health Monitoring (SHM). Determination of an adequate order and identi cation of an appropriate model play prominent roles in extracting sensitive features to damage from time series representations. Early damage detection under statistical decision-making via high-dimensional features is another signi cant issue. The main objectives of this study were to improve a residual-based feature extraction method by time series modeling and to propose a multivariate data visualization approach to early damage detection. A simple graphical tool based on Box-Jenkins methodology was adopted to identify the most compatible time series model with vibration time-domain measurements. Furthermore, k-means and Gaussian Mixture Model (GMM) clustering techniques were utilized to examine the performance of the residuals of the identi ed model in damage detection. A numerical concrete beam and an experimental benchmark model were applied to verifying the improved and proposed methods along with comparative analyses. Results showed that the approaches were successful and superior to a state-of-the-art order determination technique in obtaining a sufficient order, generating uncorrelated residuals, extracting sensitive features to damage, and accurately detecting early damage by high-dimensional data