Decision support system for surface irrigation design

The SADREG decision support system was developed to help decision makers in the process of design and selection of farm surface irrigation systems to respond to requirements of modernization of surface irrigation—furrow, basin, and border irrigation. It includes a database, simulation models, user-friendly interfaces, and multicriteria analysis models. SADREG is comprised of two components: design and selection. The first component applies database information, and through several simulation and computational tools, produces a set of design alternatives in agreement with the user options. These alternatives are characterized by several hydraulic, economic, and environmental indicators that allow appropriate selection and ranking. The selection component bases upon multicriteria analysis using composite programming and ELECTRE II ranking models, which support the decision maker to select the best alternative. The decision maker participates in all decision processes through a user-friendly interface that allows expressing design options and priorities. SADREG was tested with data collected from field experiments. In addition to describing the modeling approach, an application to a sector of the Lower Mondego Irrigation Project, Portugal, is presente

Improving System Design Through the Integration of Human Systems and Systems Engineering Models

The human is a critical aspect of many systems, but frequently there is a failure to properly account for human capabilities and involvement during system design. This inattention results in systems with higher lifecycle costs, decreased user compatibility, and the potential to produce disastrous consequences. This research presents an approach to integrating the human into system models by using two methods: static and dynamic modeling. The static method uses a user-centered design framework to create system- and human-centered models that deconstruct the system and user into their respective components. These models are integrated to create system models that include relevant information about the human and highlight potentially conflicting tasks. The dynamic method uses a human performance modeling tool to create a discrete event simulation (DES) of the system. This DES model is used to perform an analysis between system trades, by which constraints and assumptions placed on the human are verified. Data gained from the analysis are integrated back into system models in order to reflect true system performance. By applying these two integration methods early in the system’s lifecycle, system models can more effectively account for the human as a critical component of the system, thus improving system design

Technique of uncertainty and sensitivity analysis for sustainable building energy systems performance calculations

Sustainable buildings design process is typical for modeling and simulation usage. The main reason is because there is generally no experience with such buildings and there is lot of new approaches and technical solutions to be used. Computer simulation could be supporting tool in engineering design process and can bring the good way for reducing energy consumption together with optimalization algorithm. For the optimization process we have to know which most sensitive input parametr from many of them has to be investigate. Therefore at first is necessary to perform the sensitivity analysis and find out the "strongest" input parametrs which most affecting the results under observation. Also still the simulation tools are mainly using to predict energy consumption, boiler and chiller loads, indoor air quality, etc. before the building is build. The information about the building envelope, schedule and HVAC components are unclear and can bring large uncertainty in results by setting this inputs to the simulation tools. Paper presents preview of uncertainty and sensitivity analysis. This techniques are shown on case study concretely BESTEST case600 with DRYCOLD climate conditions. Also systems VAV (variable volume of air) and water fancoil system are compared. For this prototype the simulation tool IES was chosen

Developing Methods of Obtaining Quality Failure Information from Complex Systems

The complexity in most engineering systems is constantly growing due to ever-increasing technological advancements. This result in a corresponding need for methods that adequately account for the reliability of such systems based on failure information from components that make up these systems. This dissertation presents an approach to validating qualitative function failure results from model abstraction details. The impact of the level of detail available to a system designer during conceptual stages of design is considered for failure space exploration in a complex system. Specifically, the study develops an efficient approach towards detailed function and behavior modeling required for complex system analyses. In addition, a comprehensive research and documentation of existing function failure analysis methodologies is also synthesized into identified structural groupings. Using simulations, known governing equations are evaluated for components and system models to study responses to faults by accounting for detailed failure scenarios, component behaviors, fault propagation paths, and overall system performance. The components were simulated at nominal states and varying degrees of fault representing actual modes of operation. Information on product design and provisions on expected working conditions of components were used in the simulations to address normally overlooked areas during installation. The results of system model simulations were investigated using clustering analysis to develop an efficient grouping method and measure of confidence for the obtained results. The intellectual merit of this work is the use of a simulation based approach in studying how generated failure scenarios reveal component fault interactions leading to a better understanding of fault propagation within design models. The information from using varying fidelity models for system analysis help in identifying models that are sufficient enough at the conceptual design stages to highlight potential faults. This will reduce resources such as cost, manpower and time spent during system design. A broader impact of the project is to help design engineers identifying critical components, quantifying risks associated with using particular components in their prototypes early in the design process and help improving fault tolerant system designs. This research looks to eventually establishing a baseline for validating and comparing theories of complex systems analysis

Modeling strategies for multiple scenarios and fast simulations in large systems: applications to fire safety and energy engineering

The use of computational modeling has become very popular and important in many engineering and physical fields, as it is considered a fast and inexpensive technique to support and often substitute experimental analysis. In fact system design and analysis can be carried out through computational studies instead of experiments, that are typically demanding in terms of cost and technical resources; sometimes the systems characteristics and the technical problems make the experiments impossible to perform and the use of computational tools is the only feasible option. Demand of resources for realistic simulation is increasing due to the interest in studying complex and large systems. In these framework smart modeling approaches and model reduction techniques play a crucial role for making complex and large system suitable for simulations. Moreover, it should be considered that often more than one simulation is requested in order to perform an analysis. For instance, if a heuristic method is applied to the optimization of a component, the model has to be run a certain number of times. The same problem arises when a certain level of uncertainty affect the system parameters; in this case also many simulation are required for obtaining the desired information. This is the reason why the use of technique that allows to obtain compact model is an interesting topic nowadays. In this PhD thesis different reduction approaches and strategies have been used in order to analyze three energetic systems involving large domain and long time, one for each reduction approach categories. In all the topic considered, a smart model has been adopted and, when data were available, tested using experimental data. All the model are characterized by large domain and the time involved in the analysis are high in all the cases, therefore a method for compact model achievement is used in all the cases. The considered topics are: • Groundwater temperature perturbations due to geothermal heat pump installations, analyzed trough a multi-level model. • District heating networks (DHN), studied from both the fluid-dynamic and thermal point of view and applied to one of the larger network in Europe, the Turin district heating system (DHS), trough a Proper Orthogonal Decomposition - Radial Basis Function model. • Forest fire propagation simulation carried out using a Proper Orthogonal Decomposition projection model

In-silico-Systemanalyse von Biopathways

Chen M. In silico systems analysis of biopathways. Bielefeld (Germany): Bielefeld University; 2004.In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways. This dissertation addresses these problems of in silico systems analysis of biopathways. We developed a software system to integrate the access to different databases, and exploited the Petri net methodology to model and simulate metabolic networks in cells. It develops a computer modeling and simulation technique based on Petri net methodology; investigates metabolic networks at a system level; proposes a markup language for biological data interchange among diverse biological simulators and Petri net tools; establishes a web-based information retrieval system for metabolic pathway prediction; presents an algorithm for metabolic pathway alignment; recommends a nomenclature of cellular signal transduction; and attempts to standardize the representation of biological pathways. Hybrid Petri net methodology is exploited to model metabolic networks. Kinetic modeling strategy and Petri net modeling algorithm are applied to perform the processes of elements functioning and model analysis. The proposed methodology can be used for all other metabolic networks or the virtual cell metabolism. Moreover, perspectives of Petri net modeling and simulation of metabolic networks are outlined. A proposal for the Biology Petri Net Markup Language (BioPNML) is presented. The concepts and terminology of the interchange format, as well as its syntax (which is based on XML) are introduced. BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to exchange biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators. A web-based metabolic information retrieval system, PathAligner, is developed in order to predict metabolic pathways from rudimentary elements of pathways. It extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. The system also provides a navigation platform to investigate metabolic related information, and transforms the output data into XML files for further modeling and simulation of the reconstructed pathway. An alignment algorithm to compare the similarity between metabolic pathways is presented. A new definition of the metabolic pathway is proposed. The pathway defined as a linear event sequence is practical for our alignment algorithm. The algorithm is based on strip scoring the similarity of 4-hierarchical EC numbers involved in the pathways. The algorithm described has been implemented and is in current use in the context of the PathAligner system. Furthermore, new methods for the classification and nomenclature of cellular signal transductions are recommended. For each type of characterized signal transduction, a unique ST number is provided. The Signal Transduction Classification Database (STCDB), based on the proposed classification and nomenclature, has been established. By merging the ST numbers with EC numbers, alignments of biopathways are possible. Finally, a detailed model of urea cycle that includes gene regulatory networks, metabolic pathways and signal transduction is demonstrated by using our approaches. A system biological interpretation of the observed behavior of the urea cycle and its related transcriptomics information is proposed to provide new insights for metabolic engineering and medical care

An Integrated Reliability and Physics-Based Risk Modeling Approach for Assessing Human Spaceflight Systems

This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development