Dynamic test/analysis correlation using reduced analytical models

Test/analysis correlation is an important aspect of the verification of analysis models which are used to predict on-orbit response characteristics of large space structures. This paper presents results of a study using reduced analysis models for performing dynamic test/analysis correlation. The reduced test-analysis model (TAM) has the same number and orientation of DOF as the test measurements. Two reduction methods, static (Guyan) reduction and the Improved Reduced System (IRS) reduction, are applied to the test/analysis correlation of a laboratory truss structure. Simulated test results and modal test data are used to examine the performance of each method. It is shown that selection of DOF to be retained in the TAM is critical when large structural masses are involved. In addition, the use of modal test results may provide difficulties in TAM accuracy even if a large number of DOF are retained in the TAM

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined

Development of Test-Analysis Models (TAM) for correlation of dynamic test and analysis results

The primary objective of structural analysis of aerospace applications is to obtain a verified finite element model (FEM). The verified FEM can be used for loads analysis, evaluate structural modifications, or design control systems. Verification of the FEM is generally obtained as the result of correlating test and FEM models. A test analysis model (TAM) is very useful in the correlation process. A TAM is essentially a FEM reduced to the size of the test model, which attempts to preserve the dynamic characteristics of the original FEM in the analysis range of interest. Numerous methods for generating TAMs have been developed in the literature. The major emphasis of this paper is a description of the procedures necessary for creation of the TAM and the correlation of the reduced models with the FEM or the test results. Herein, three methods are discussed, namely Guyan, Improved Reduced System (IRS), and Hybrid. Also included are the procedures for performing these analyses using MSC/NASTRAN. Finally, application of the TAM process is demonstrated with an experimental test configuration of a ten bay cantilevered truss structure

Effect of pre-treatment on fouling propensity of feed as depicted by the modified fouling index (MFI) and cross-flow sampler-modified fouling index (CFS-MFI)

The effectiveness of different pretreatment on the fouling propensity of the feed was studied using synthetic wastewater. The fouling potential of the feed was characterized by the standard modified fouling index (MFI) and cross-flow sampler modified fouling index (CFS-MFI). In CFS-MFI, a cross-flow sampler was used to simulate the condition of a cross-flow filtration. The results indicated that the pretreatment such as flocculation with an optimum dose of 68 mg/l FeCl3 substantially reduced the fouling propensity of the feed. The standard MFI of flocculated wastewater was reduced by around 99% compared to that of the untreated wastewater. Similarly, the adsorption with powdered activated carbon (PAC) of 1 g/l reduced the standard MFI value to more than 99% compared to that of the untreated wastewater. The CFS-MFI values were lower than the standard MFI values for both treated and untreated wastewater, suggesting that the standard MFI was overestimated. The overestimation of the standard MFI compared to that of the CFS-MFI value was more than 99%. The effect of molecular weight distribution (MWD) of the foulants in the wastewater on the fouling propensity of the feed was investigated. The MWD was correlated with the MFI and CFS-MFI indices. It yielded useful insights in understanding the effect of MW on MFI and CFS-MFI and fouling propensity of the feed. © 2009 Elsevier B.V. All rights reserved

Sentimental causal rule discovery from twitter

Social media, especially Twitter is now one of the most popular platforms where people can freely express their opinion. However, it is difficult to extract important summary information from many millions of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques for extracting sentimental causal rules from textual data sources such as Twitter which combine sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced perceptions that are stated in natural language. Causal rules on the other hand indicate associations between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an effective summarization mechanism that combine causal relations among different aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule discovery is an effective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making

Estimating fast and slow reacting components in surface water and groundwater using a two-reactant model

Maintaining residual chlorine levels in a water distribution network is a challenging task, especially in the context of developing countries where water is usually supplied intermittently. To model chlorine decay in water distribution networks, it is very important to understand chlorine kinetics in bulk water. Recent studies have suggested that chlorine decay rate depends on initial chlorine levels and the type of organic and inorganic matter present in water, indicating that a first-order decay model is unable to accurately predict chlorine decay in bulk water. In this study, we employed the two-reactant (2R) model to estimate the fast and slow reacting components in surface water and groundwater. We carried out a bench-scale test for surface water and groundwater at initial chlorine levels of 1, 2, and 5 mg L−1. We used decay data sets to estimate optimal parameter values for both surface water and groundwater. After calibration, the 2R model was validated with two decay data sets with varying initial chlorine concentrations (ICCs). This study arrived at three important findings. (a) We found that the ratio of slow to fast reacting components in groundwater was 30 times greater than that of the surface water. This observation supports the existing literature which indicates the presence of high levels of slow reacting fractions (manganese and aromatic hydrocarbons) in groundwater. (b) Both for surface water and groundwater, we obtained good model prediction, explaining 97 % of the variance in data for all cases. The mean square error obtained for the decay data sets was close to the instrument error, indicating the feasibility of the 2R model for chlorine prediction in both types of water. (c) In the case of deep groundwater, for high ICC levels (> 2 mg L−1), the first-order model can accurately predict chlorine decay in bulk water