MultiVeStA: Statistical Model Checking for Discrete Event Simulators

The modeling, analysis and performance evaluation of large-scale systems are difficult tasks. Due to the size and complexity of the considered systems, an approach typically followed by engineers consists in performing simulations of systems models to obtain statistical estimations of quantitative properties. Similarly, a technique used by computer scientists working on quantitative analysis is Statistical Model Checking (SMC), where rigorous mathematical languages (typically logics) are used to express systems properties of interest. Such properties can then be automatically estimated by tools performing simulations of the model at hand. These property specifications languages, often not popular among engineers, provide a formal, compact and elegant way to express systems properties without needing to hard-code them in the model definition. This paper presents MultiVeStA, a statistical analysis tool which can be easily integrated with existing discrete event simulators, enriching them with efficient distributed statistical analysis and SMC capabilities

A Tool for automated design of sigma-delta modulators using statistical optimization

A tool is presented which starting from high level specifications of SC σδ modulators (resolution, bandwidth and oversampling ratio) calculates first optimum specifications for the building blocks (op-amps, comparator, etc.), and then, optimum sizes for their schematics. At both design levels (high-level synthesis and cell dimensioning), optimization is performed via using statistical techniques and innovative heuristics, which allow global design (independent on the initial conditions) and increased computer efficiency as compared to conventional statistical optimization techniques. The tool has been conceived to be flexible at the high-level part(via the use of an architecture independent, behaviourable modeling approach) and completely open at the cell-design part. Performance of the tool is demonstrated via the automatic design of a 16bit-dynamic range, 8Khz second-order SC σδ modulator in 1.2 μm CMOS technology, for which measurements on a fabricated prototype are reported

[MODELING OF PH NEUTRALIZATION PROCESS PILOT PLANT]

System Identification is an art of constructing a mathematical model for a dynamic response system. The modeling process is based on the observed input and output data for a system. To start a modeling process, a good understanding of process behavior is required as it will determine the important parameters and characteristics to be analyzed. pH neutralization is a very nonlinear process. It is not easy to get an accurate model as compared to the actual model. Modeling using conventional methods does not seem to give a reliable model for this process. Thus, for wide a range of neutralization pH values, conventional modeling methods are not sufficient. Therefore, for this project, intelligent approaches are considered. The conventional methods that are used by the Author are mathematical modeling, empirical modeling and statistical modeling. Mathematical modeling is done to see the relation of inputs and output. Empirical modeling is the common method used for plant modeling. Statistical modeling is more a to computerized modeling where it requires a good computer configuration basic in order to achieve the desired output. Neural Network is used for the intelligent method. Neural network is an intelligent approach that has the capability to predict future plant performance by training several datasets. These conventional and intelligent methods are compared between each other in term of the model accuracy, model reliability and flexibility. Modeling using mathematical modeling is tedious and requires more effort on the block diagram configuration in order to get an accurate result. Empirical modeling is basically good enough for plant identification, unfortunately for a highly nonlinear system, the method does not seem reliable. Statistical modeling has the ability to predict an acceptable higher order model. On top of that, neural network could give a more reliable and accurate result

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented

Verification of Space Station Secondary Power System Stability Using Design of Experiment

This paper describes analytical methods used in verification of large DC power systems with applications to the International Space Station (ISS). Large DC power systems contain many switching power converters with negative resistor characteristics. The ISS power system presents numerous challenges with respect to system stability such as complex sources and undefined loads. The Space Station program has developed impedance specifications for sources and loads. The overall approach to system stability consists of specific hardware requirements coupled with extensive system analysis and testing. Testing of large complex distributed power systems is not practical due to size and complexity of the system. Computer modeling has been extensively used to develop hardware specifications as well as to identify system configurations for lab testing. The statistical method of Design of Experiments (DoE) is used as an analysis tool for verification of these large systems. DOE reduces the number of computer runs which are necessary to analyze the performance of a complex power system consisting of hundreds of DC/DC converters. DoE also provides valuable information about the effect of changes in system parameters on the performance of the system. DoE provides information about various operating scenarios and identification of the ones with potential for instability. In this paper we will describe how we have used computer modeling to analyze a large DC power system. A brief description of DoE is given. Examples using applications of DoE to analysis and verification of the ISS power system are provided

Barriers of Adoption Building Information Modeling (BIM) in Construction Projects of Iraq

Building Information Modeling (BIM) is a unified and comprehensive system for all that associated with the construction project, which includes a set of effective policies, procedures, and computer applications that increase the level of performance in construction project during its life cycle. Through this study investigate about the potential barriers which facing the adoption of BIM was performed. The quantitative approach was adopted by conducting a questionnaire directed to professionals in the field of construction projects in the public and private sectors. Three hundred copies of the forms were distributed to the private companies and governmental institutions and departments. The data were subjected to the appropriate statistical analysis and the results showed that the three highest potential barriers of using BIM in Iraq are a weakness of the government's efforts, Poor knowledge about the benefits of BIM, and resistance to change