The Impact of Exchange Rate Uncertainty on Domestic Investment: Panel Evidence from Emerging Markets and Developing Economies

This study attempts to suggest empirical evidence about the impact of exchange rate uncertainty on the domestic investment for 25 emerging markets and developing economies (EMDEs) for the time line covering the years between 2004 and 2014. Exchange rate uncertainty is modeled by selecting one of the volatility models of GARCH(1, 1), EGARCH(1, 1), or GJR-GARCH(1, 1) for individual countries. The study aims to offer a broad point of view about the impact of exchange rate uncertainty on domestic investment through a feasible generalized least square panel data model by deeming the economic growth, real interest rate, and 2008/2009 global financial crisis (GFC). The empirical results show that the impact of exchange rate uncertainty on domestic investment for EMDEs is found to be positive and significant, which may indicate the existence of risk neutral or insensitive domestic investors to exchange rate uncertainty in these countries. On the other hand, the study also proves that the effect of economic growth is positive and significant on domestic investment, whereas the impact of GFC on domestic investment is negative and significant. However, the impact of real exchange rate on domestic investment is found to be negative but insignificant

Model updating strategy for structures with localised nonlinearities using frequency response measurements

This paper proposes a model updating strategy for localised nonlinear structures. It utilises an initial finite-element (FE) model of the structure and primary harmonic response data taken from low and high amplitude excitations. The underlying linear part of the FE model is first updated using low-amplitude test data with established techniques. Then, using this linear FE model, the nonlinear elements are localised, characterised, and quantified with primary harmonic response data measured under stepped-sine or swept-sine excitations. Finally, the resulting model is validated by comparing the analytical predictions with both the measured responses used in the updating and with additional test data. The proposed strategy is applied to a clamped beam with a nonlinear mechanism and good agreements between the analytical predictions and measured responses are achieved. Discussions on issues of damping estimation and dealing with data from amplitude-varying force input in the updating process are also provided

Model updating of nonlinear structures from measured FRFs

There are always certain discrepancies between modal and response data of a structure obtained from its mathematical model and experimentally measured ones. Therefore it is a general practice to update the theoretical model by using experimental measurements in order to have a more accurate model. Most of the model updating methods used in structural dynamics are for linear systems. However, in real life applications most of the structures have nonlinearities, which restrict us applying model updating techniques available for linear structures, unless they work in linear range. Well-established frequency response function (FRF) based model updating methods would easily be extended to a nonlinear system if the FRFs of the underlying linear system (linear FRF5) could be experimentally measured. When frictional type of nonlinearity co-exists with other types of nonlinearities, it is not possible to obtain linear FRF5 experimentally by using low level forcing. In this study a method (named as Pseudo Receptance Difference (PRD) method) is presented to obtain linear FRFs of a nonlinear structure having multiple nonlinearities including friction type of nonlinearity. PRD method, calculates linear FRFs of a nonlinear structure by using FRF5 measured at various forcing levels, and simultaneously identifies all nonlinearities in the system. Then, any model updating method can be used to update the linear part of the mathematical model. In this present work, PRD method is used to predict the linear FRFs from measured nonlinear FRF5, and the inverse eigensensitivity method is employed to update the linear finite element (FE) model of the nonlinear structure. The proposed method is validated with different case studies using nonlinear lumped single-degree of freedom system, as well as a continuous system. Finally, a real nonlinear T-beam test structure is used to show the application and the accuracy of the proposed method. The accuracy of the updated nonlinear model of the test structure is demonstrated by comparing the calculated and measured nonlinear FRFs of the test structure at several different forcing levels

Model Updating of a Nonlinear System: Gun Barrel of a Battle Tank

Nonlinearities in a structural system make the use of model updating methods developed for linear systems difficult to apply nonlinear systems. If the FRFs of the underlying linear systems in a nonlinear system could be experimentally extracted, then the linear model updating methods could easily be applied to nonlinear systems as well. When there are complex nonlinearities in a structure together with frictional type of nonlinearity, linear FRFs cannot be accurately obtained by using low level forcing. In this present work, the model updating method-Pseudo Receptance Difference (PRD) method-recently developed by the authors for nonlinear systems, is applied to the gun barrel of a battle tank. The linear FRFs of the nonlinear gun barrel of the battle tank are obtained from measured nonlinear FRFs, and simultaneously the nonlinearities in the system are identified. Then the inverse eigensensitivity method is employed to update the linear finite element (FE) model of the gun barrel. Finally, in order to demonstrate the accuracy of the updated nonlinear model, the calculated and measured FRFs of the gun barrel at several different forcing levels are compared

Experimental validation of pseudo receptance difference (PRD) method for nonlinear model updating

In real life applications most of the structures have nonlinearities, which restrict us applying model updating techniques available for linear structures. Well-established FRF based model updating methods would easily be extended to a nonlinear system if the FRFs of the underlying linear system (linear FRFs) could be experimentally measured. When frictional type of nonlinearity co-exists with other types of nonlinearities, it is not possible to obtain linear FRFs experimentally by using low level forcing. Pseudo Receptance Difference (PRD) method, recently developed by the authors, calculates linear FRFs of a nonlinear structure by using FRFs measured at various forcing levels, and simultaneously identifies multiple nonlinearities in the system. Then any model updating method can be used to update the linear part of the mathematical model. In this present work, the PRD method is used to predict the linear FRFs from nonlinear FRFs, and the inverse eigensensitivity method is employed to update the linear finite element (FE) model of a nonlinear structure. A real nonlinear T-beam test structure is used to validate the accuracy of the proposed method. First, the linear FRFs are calculated from nonlinear FRFs measured at different forcing levels, and simultaneously, the nonlinearities in the structure are identified. Then the FE model of the linear part of the structure is updated. Finally, the accuracy of the updated nonlinear model of the test structure is demonstrated by comparing the calculated and measured FRFs of the test structure at several different forcing levels

Modal Analyses and Experimental Verifications of Joined-Wing Configurations

In recent days, wing kits are extensively used to convert conventional munitions into guided munitions. The wing kit integration enables munitions to gain standoff attack capability, extends their range and together with a laser guidance system, adds moving target tracking capability. Typical wing kits are usually composed of two main wings, however, some of them have a joined-wing configuration where the aft wings can support the main wings in order to increase maneuverability, range performance and structural performance. In this study, since the geometry and sweep angle of front wings are kept fixed due to aerodynamic effects in operating conditions, joined-wing configurations are investigated by changing two key parameters, namely; aft wing sweep angle and location of the joint. Response surface methodology is used in order to determine how these parameters affect the vibration characteristics of the joined-wing configurations. In numerical analyses, the natural frequencies and the corresponding mode shapes for the configurations are obtained by using ANSYS software. Then the results of selected configurations are verified via modal testing so as to achieve accurate finite element models of the joined-wing configurations

Multilevel organisational learning in a project-based organisation: Computational analysis based on a 3^rd-order adaptive network model

This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: Recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control

Multilevel organisational learning in a project-based organisation: Computational analysis based on a 3^rd-order adaptive network model

This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: Recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control.Safety and Security Scienc