System Dynamics is methodology for modeling and analyzing complex systems. Such systems can be characterized by interconnectedness and feedback. Applying risk assessment to the results of System Dynamics models is a challenge. Though in some cases the resulting time series data generated by a simulation may appear approximately random at a specific scale, there is often a high-degree of auto-correlation within the data series due to the deterministic nature of generation and feedback loops inherent in the system. This paper presents proposed Dynamic Risk Assessment Method (DRAM) that allows for the estimation of risk for system dynamics data series that appear to be approximately random. DRAM is based on standard risk assessment methods and is simple both to calculate and apply. In this article, the proposed method is applied to determine the risk connected with hypothetical costs of illness stemming from water supply system contamination with Cryptosporidium.
In this paper, quanizted multisine inputs for a maneuver with simultaneous elevator, aileron and rudder deflections are presented. The inputs were designed for 9 quantization levels. A nonlinear aircraft model was exited with the designed inputs and its stability and control derivatives were identified. Time domain output error method with maximum likelihood principle and a linear aircraft model were used to perform parameter estimation. Visual match and relative standard deviations of the estimates were used to validate the results for each quantization level for clean signals and signals with measurement noise present in the data. The noise was included into both output and input signals. It was shown that it is possible to obtain accurate results when simultaneous flight controls deflections are quantized and noise is present in the data.
The problem of reconstructing an unknown disturbance under measuring a part of phase coordinates of a system of linear differential equations is considered. Solving algorithm is designed. The algorithm is based on the combination of ideas from the theory of dynamical inversion and the theory of guaranteed control. The algorithm consists of two blocks: the block of dynamical reconstruction of unmeasured coordinates and the block of dynamical reconstruction of an input.