Model correlation and damage location for large space truss structures: Secant method development and evaluation

On-orbit testing of a large space structure will be required to complete the certification of any mathematical model for the structure dynamic response. The process of establishing a mathematical model that matches measured structure response is referred to as model correlation. Most model correlation approaches have an identification technique to determine structural characteristics from the measurements of the structure response. This problem is approached with one particular class of identification techniques - matrix adjustment methods - which use measured data to produce an optimal update of the structure property matrix, often the stiffness matrix. New methods were developed for identification to handle problems of the size and complexity expected for large space structures. Further development and refinement of these secant-method identification algorithms were undertaken. Also, evaluation of these techniques is an approach for model correlation and damage location was initiated

Optimal dynamic taxation, saving and investment

Fiscal Policy;Optimization;public economics

Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.Comment: Revised versio

Splitting methods for constrained diffusion-reaction systems

We consider Lie and Strang splitting for the time integration of constrained partial differential equations with a nonlinear reaction term. Since such systems are known to be sensitive with respect to perturbations, the splitting procedure seems promising as we can treat the nonlinearity separately. This has some computational advantages, since we only have to solve a linear constrained system and a nonlinear ODE. However, Strang splitting suffers from order reduction which limits its efficiency. This is caused by the fact that the nonlinear subsystem produces inconsistent initial values for the constrained subsystem. The incorporation of an additional correction term resolves this problem without increasing the computational cost. Numerical examples including a coupled mechanical system illustrate the proven convergence results

Difference games and policy evaluation: A conceptual framework

Economics

On the Interaction of Financial Frictions and Fixed Capital Adjustment Costs: Evidence from a Panel of German Firms

This paper analyzes the interaction of financial frictions and non- convex adjustment costs. With non-convex adjustment costs firms infrequently carry out discrete investment projects. Therefore, financial variables may influence investment in two ways. Theoretically, they can alter the frequency at which investment projects are undertaken, or they can influence the size of the stock of capital a company wishes to hold in the long run. Empirically, finance has nearly no long-run influence on the stock of capital in the sample of German companies which this paper analyzes. By contrast, the influence of finance on investment decisions is substantial. Consequently, finance primarily affects investment frequencies and accordingly, financial factors and fundamental capital productivity strongly interact in the determination of investment.Investment; imperfect capital markets; debt constraints; adjustment costs; nonlinear panel cointegration

Optimal dynamic taxation with respect to firms

Investment;Corporate Tax;investeringen