The object of the present study is to investigate the influence of damping uncertainty and statistical correlation on the dynamic response of structures with random damping parameters in the neighbourhood of a resonant frequency. A Non-Linear Statistical model (NLSM) is successfully demonstrated to predict the probabilistic response of an industrial building structure with correlated random damping. A practical computational technique to generate first and second-order sensitivity derivatives is presented and the validity of the predicted statistical moments is checked by traditional Monte Carlo simulation. Simulation results show the effectiveness of the NLSM to estimate uncertainty propagation in structural dynamics. In addition, it is demonstrated that the uncertainty in damping indeed influences the system response with the effects being more pronounced for lightly damped structures, higher variability and higher statistical correlation of damping parameters.
Measurement of the perfusion coefficient and thermal parameters of skin tissue using dynamic thermography is presented in this paper. A novel approach based on cold provocation and thermal modelling of skin tissue is presented. The measurement was performed on a person’s forearm using a special cooling device equipped with the Peltier module. The proposed method first cools the skin, and then measures the changes of its temperature matching the measurement results with a heat transfer model to estimate the skin perfusion and other thermal parameters. In order to assess correctness of the proposed approach, the uncertainty analysis was performed.
The paper focuses on the problem of robust fault detection using analytical methods and soft computing. Taking into account the model-based approach to Fault Detection and Isolation (FDI), possible applications of analytical models, and first of all observers with unknown inputs, are considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty of soft computing models (neural networks and neuro-fuzzy networks). It is shown that based on soft computing models uncertainty defined as a confidence range for the model output, adaptive thresholds can be described. The paper contains a numerical example that illustrates the effectiveness of the proposed approach for increasing the reliability of fault detection. A comprehensive simulation study regarding the DAMADICS benchmark problem is performed in the final part.
Under steady-state conditions when fluid temperature is constant, temperature measurement can be accomplished with high degree of accuracy owing to the absence of damping and time lag. However, when fluid temperature varies rapidly, for example, during start-up, appreciable differences occur between the actual and measured fluid temperature. These differences occur because it takes time for heat to transfer through the heavy thermometer pocket to the thermocouple. In this paper, a method for determinig transient fluid temperature based on the first-order thermometer model is presented. Fluid temperature is determined using a thermometer, which is suddenly immersed into boiling water. Next, the time constant is defined as a function of fluid velocity for four sheated thermocouples with different diameters. To demonstrate the applicability of the presented method to actual data where air velocity varies, the temperature of air is estimated based on measurements carried out by three thermocouples with different outer diameters. Lastly, the time constant is presented as a function of fluid velocity and outer diameter of thermocouple.
In the existent world of continuous production systems, strong attention has been waged to anonymous risk that probably generates significant apprehension. The forecast for net present value is extremely important for any production plant. The objective of this paper is to implement Monte Carlo simulation technique for perceiving the impact of risk and uncertainty in prediction and forecasting company’s profitability. The production unit under study is interested to make the initial investment by installing an additional spray dryer plant. The expressive values acquied from the Monte Carlo technique established a range of certain results. The expected net present value of the cash flow is $14,605, hence the frequency chart outcomes confirmed that there is the highest level of certainty that the company will achieve its target. To forecast the net present value for the next period, the results confirmed that there are 50.73% chances of achieving the outcomes. Considering the minimum and maximum values at 80% certainty level, it was observed that 80% chances exist that expected outcomes will be between $5,830 and $22,587. The model’s sensitivity results validated that cash inflows had a greater sensitivity level of 21.1% and the cash inflows for the next year as 19.7%. Cumulative frequency distribution confirmed that the probability to achieve a maximum value of $23,520 is 90 % and for the value of $6,244 it is about 10 %. These validations suggested that controlling the expenditures, the company’s outflows can also be controlled definitely.
This paper proposes a practical tuning of closed loops with model based predictive control. The data assumed to be known from the process is the result of the bump test commonly applied in industry and known in engineering as step response data. A simplified context is assumed such that no prior know-how is required from the plant operator. The relevance of this assumption is very realistic in the context of first time users, both for industrial operators and as educational competence of first hand student training. A first order plus dead time is approximated and the controller parameters immediately follow by heuristic rules. Analysis has been performed in simulation on representative dynamics with guidelines for the various types of processes. Three single-input-single-output experimental setups have been used with no expert users available in different locations – both educational and industrial – these setups are representative for practical cases: a variable time delay dominant system, a non-minimum phase system and an open loop unstable system. Furthermore, in a multivariable control context, a train of separation columns has been tested for control in simulation, followed by experimental tests on a laboratory system with similar dynamics, i.e. a sextuple coupled water tank system. The results indicate the proposed methodology is suitable for hands-on tuning of predictive control loops with some limitations on performance and multivariable process control.