Analyzing diffuse scattering with supercomputers

Two new approaches to quantitatively analyze diffuse diffraction intensities from faulted layer stacking are reported. The parameters of a probability-based growth model are determined with two iterative global optimization methods: a genetic algorithm (GA) and particle swarm optimization (PSO). The results are compared with those from a third global optimization method, a differential evolution (DE) algorithm [Storn & Price (1997). J. Global Optim. 11, 341–359]. The algorithm efficiencies in the early and late stages of iteration are compared. The accuracy of the optimized parameters improves with increasing size of the simulated crystal volume. The wall clock time for computing quite large crystal volumes can be kept within reasonable limits by the parallel calculation of many crystals (clones) generated for each model parameter set on a super- or grid computer. The faulted layer stacking in single crystals of trigonal three-pointedstar- shaped tris(bicylco[2.1.1]hexeno)benzene molecules serves as an example for the numerical computations. Based on numerical values of seven model parameters (reference parameters), nearly noise-free reference intensities of 14 diffuse streaks were simulated from 1280 clones, each consisting of 96 000 layers (reference crystal). The parameters derived from the reference intensities with GA, PSO and DE were compared with the original reference parameters as a function of the simulated total crystal volume. The statistical distribution of structural motifs in the simulated crystals is in good agreement with that in the reference crystal. The results found with the growth model for layer stacking disorder are applicable to other disorder types and modeling techniques, Monte Carlo in particular.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Multiobjective optimization applied to structural sizing of low cost university-class microsatellite projects

In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In this paper, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. Finally, a Genetic Algorithm (GA) multiobjective optimization is applied to the design space. The result is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget, which gives a useful insight to the design team at the early phases of the design.

Multi-objective Aerodynamic Optimization of High-speed Trains in Tunnels

A genetic algorithm (GA) is employed for the multi-objective shape optimization of the nose of a high-speed train. Aerodynamic problems observed at high speeds become still more relevant when traveling along a tunnel. The objective is to minimize both the aerodynamic drag and the amplitude of the pressure gradient of the compression wave when a train enters a tunnel. The main drawback of GA is the large number of evaluations need in the optimization process. Metamodels-based optimization is considered to overcome such problem. As a result, an explicit relationship between pressure gradient and geometrical parameters is obtained.

Multidisciplinary Design Optimization Application to Conceptual Design of University-class Microsatellite Projects

Esta tesis se ha realizado en el contexto del proyecto UPMSat-2, que es un microsatélite diseñado, construido y operado por el Instituto Universitario de Microgravedad "Ignacio Da Riva" (IDR / UPM) de la Universidad Politécnica de Madrid. Aplicación de la metodología Ingeniería Concurrente (Concurrent Engineering: CE) en el marco de la aplicación de diseño multidisciplinar (Multidisciplinary Design Optimization: MDO) es uno de los principales objetivos del presente trabajo. En los últimos años, ha habido un interés continuo en la participación de los grupos de investigación de las universidades en los estudios de la tecnología espacial a través de sus propios microsatélites. La participación en este tipo de proyectos tiene algunos desafíos inherentes, tales como presupuestos y servicios limitados. Además, debido al hecho de que el objetivo principal de estos proyectos es fundamentalmente educativo, por lo general hay incertidumbres en cuanto a su misión en órbita y cargas útiles en las primeras fases del proyecto. Por otro lado, existen limitaciones predeterminadas para sus presupuestos de masa, volumen y energía, debido al hecho de que la mayoría de ellos están considerados como una carga útil auxiliar para el lanzamiento. De este modo, el costo de lanzamiento se reduce considerablemente. En este contexto, el subsistema estructural del satélite es uno de los más afectados por las restricciones que impone el lanzador. Esto puede afectar a diferentes aspectos, incluyendo las dimensiones, la resistencia y los requisitos de frecuencia. En la primera parte de esta tesis, la atención se centra en el desarrollo de una herramienta de diseño del subsistema estructural que evalúa, no sólo las propiedades de la estructura primaria como variables, sino también algunas variables de nivel de sistema del satélite, como la masa de la carga útil y la masa y las dimensiones extremas de satélite. Este enfoque permite que el equipo de diseño obtenga una mejor visión del diseño en un espacio de diseño extendido. La herramienta de diseño estructural se basa en las fórmulas y los supuestos apropiados, incluyendo los modelos estáticos y dinámicos del satélite. Un algoritmo genético (Genetic Algorithm: GA) se aplica al espacio de diseño para optimizaciones de objetivo único y también multiobjetivo. El resultado de la optimización multiobjetivo es un Pareto-optimal basado en dos objetivo, la masa total de satélites mínimo y el máximo presupuesto de masa de carga útil. Por otro lado, la aplicación de los microsatélites en misiones espaciales es de interés por su menor coste y tiempo de desarrollo. La gran necesidad de las aplicaciones de teledetección es un fuerte impulsor de su popularidad en este tipo de misiones espaciales. Las misiones de tele-observación por satélite son esenciales para la investigación de los recursos de la tierra y el medio ambiente. En estas misiones existen interrelaciones estrechas entre diferentes requisitos como la altitud orbital, tiempo de revisita, el ciclo de vida y la resolución. Además, todos estos requisitos puede afectar a toda las características de diseño. Durante los últimos años la aplicación de CE en las misiones espaciales ha demostrado una gran ventaja para llegar al diseño óptimo, teniendo en cuenta tanto el rendimiento y el costo del proyecto. Un ejemplo bien conocido de la aplicación de CE es la CDF (Facilidad Diseño Concurrente) de la ESA (Agencia Espacial Europea). Está claro que para los proyectos de microsatélites universitarios tener o desarrollar una instalación de este tipo parece estar más allá de las capacidades del proyecto. Sin embargo, la práctica de la CE a cualquier escala puede ser beneficiosa para los microsatélites universitarios también. En la segunda parte de esta tesis, la atención se centra en el desarrollo de una estructura de optimización de diseño multidisciplinar (Multidisciplinary Design Optimization: MDO) aplicable a la fase de diseño conceptual de microsatélites de teledetección. Este enfoque permite que el equipo de diseño conozca la interacción entre las diferentes variables de diseño. El esquema MDO presentado no sólo incluye variables de nivel de sistema, tales como la masa total del satélite y la potencia total, sino también los requisitos de la misión como la resolución y tiempo de revisita. El proceso de diseño de microsatélites se divide en tres disciplinas; a) diseño de órbita, b) diseño de carga útil y c) diseño de plataforma. En primer lugar, se calculan diferentes parámetros de misión para un rango práctico de órbitas helio-síncronas (sun-synchronous orbits: SS-Os). Luego, según los parámetros orbitales y los datos de un instrumento como referencia, se calcula la masa y la potencia de la carga útil. El diseño de la plataforma del satélite se estima a partir de los datos de la masa y potencia de los diferentes subsistemas utilizando relaciones empíricas de diseño. El diseño del subsistema de potencia se realiza teniendo en cuenta variables de diseño más detalladas, como el escenario de la misión y diferentes tipos de células solares y baterías. El escenario se selecciona, de modo de obtener una banda de cobertura sobre la superficie terrestre paralelo al Ecuador después de cada intervalo de revisita. Con el objetivo de evaluar las interrelaciones entre las diferentes variables en el espacio de diseño, todas las disciplinas de diseño mencionados se combinan en un código unificado. Por último, una forma básica de MDO se ajusta a la herramienta de diseño de sistema de satélite. La optimización del diseño se realiza por medio de un GA con el único objetivo de minimizar la masa total de microsatélite. Según los resultados obtenidos de la aplicación del MDO, existen diferentes puntos de diseños óptimos, pero con diferentes variables de misión. Este análisis demuestra la aplicabilidad de MDO para los estudios de ingeniería de sistema en la fase de diseño conceptual en este tipo de proyectos. La principal conclusión de esta tesis, es que el diseño clásico de los satélites que por lo general comienza con la definición de la misión y la carga útil no es necesariamente la mejor metodología para todos los proyectos de satélites. Un microsatélite universitario, es un ejemplo de este tipo de proyectos. Por eso, se han desarrollado un conjunto de herramientas de diseño para encarar los estudios de la fase inicial de diseño. Este conjunto de herramientas incluye diferentes disciplinas de diseño centrados en el subsistema estructural y teniendo en cuenta una carga útil desconocida a priori. Los resultados demuestran que la mínima masa total del satélite y la máxima masa disponible para una carga útil desconocida a priori, son objetivos conflictivos. En este contexto para encontrar un Pareto-optimal se ha aplicado una optimización multiobjetivo. Según los resultados se concluye que la selección de la masa total por satélite en el rango de 40-60 kg puede considerarse como óptima para un proyecto de microsatélites universitario con carga útil desconocida a priori. También la metodología CE se ha aplicado al proceso de diseño conceptual de microsatélites de teledetección. Los resultados de la aplicación del CE proporcionan una clara comprensión de la interacción entre los requisitos de diseño de sistemas de satélites, tales como la masa total del microsatélite y la potencia y los requisitos de la misión como la resolución y el tiempo de revisita. La aplicación de MDO se hace con la minimización de la masa total de microsatélite. Los resultados de la aplicación de MDO aclaran la relación clara entre los diferentes requisitos de diseño del sistema y de misión, así como que permiten seleccionar las líneas de base para el diseño óptimo con el objetivo seleccionado en las primeras fase de diseño. ABSTRACT This thesis is done in the context of UPMSat-2 project, which is a microsatellite under design and manufacturing at the Instituto Universitario de Microgravedad “Ignacio Da Riva” (IDR/UPM) of the Universidad Politécnica de Madrid. Application of Concurrent Engineering (CE) methodology in the framework of Multidisciplinary Design application (MDO) is one of the main objectives of the present work. In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In the first part of this thesis, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the satellite system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on the analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. A Genetic Algorithm (GA) is applied to the design space for both single and multiobejective optimizations. The result of the multiobjective optimization is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget. On the other hand, the application of the microsatellites is of interest for their less cost and response time. The high need for the remote sensing applications is a strong driver of their popularity in space missions. The satellite remote sensing missions are essential for long term research around the condition of the earth resources and environment. In remote sensing missions there are tight interrelations between different requirements such as orbital altitude, revisit time, mission cycle life and spatial resolution. Also, all of these requirements can affect the whole design characteristics. During the last years application of the CE in the space missions has demonstrated a great advantage to reach the optimum design base lines considering both the performance and the cost of the project. A well-known example of CE application is ESA (European Space Agency) CDF (Concurrent Design Facility). It is clear that for the university-class microsatellite projects having or developing such a facility seems beyond the project capabilities. Nevertheless practicing CE at any scale can be beneficiary for the university-class microsatellite projects. In the second part of this thesis, the main focus is on developing a MDO framework applicable to the conceptual design phase of the remote sensing microsatellites. This approach enables the design team to evaluate the interaction between the different system design variables. The presented MDO framework contains not only the system level variables such as the satellite total mass and total power, but also the mission requirements like the spatial resolution and the revisit time. The microsatellite sizing process is divided into the three major design disciplines; a) orbit design, b) payload sizing and c) bus sizing. First, different mission parameters for a practical range of sun-synchronous orbits (SS-Os) are calculated. Then, according to the orbital parameters and a reference remote sensing instrument, mass and power of the payload are calculated. Satellite bus sizing is done based on mass and power calculation of the different subsystems using design estimation relationships. In the satellite bus sizing, the power subsystem design is realized by considering more detailed design variables including a mission scenario and different types of solar cells and batteries. The mission scenario is selected in order to obtain a coverage belt on the earth surface parallel to the earth equatorial after each revisit time. In order to evaluate the interrelations between the different variables inside the design space all the mentioned design disciplines are combined in a unified code. The integrated satellite system sizing tool developed in this section is considered as an application of the CE to the conceptual design of the remote sensing microsatellite projects. Finally, in order to apply the MDO methodology to the design problem, a basic MDO framework is adjusted to the developed satellite system design tool. Design optimization is done by means of a GA single objective algorithm with the objective function as minimizing the microsatellite total mass. According to the results of MDO application, there exist different optimum design points all with the minimum satellite total mass but with different mission variables. This output demonstrates the successful applicability of MDO approach for system engineering trade-off studies at the conceptual design phase of the design in such projects. The main conclusion of this thesis is that the classical design approach for the satellite design which usually starts with the mission and payload definition is not necessarily the best approach for all of the satellite projects. The university-class microsatellite is an example for such projects. Due to this fact an integrated satellite sizing tool including different design disciplines focusing on the structural subsystem and considering unknown payload is developed. According to the results the satellite total mass and available mass for the unknown payload are conflictive objectives. In order to find the Pareto-optimal a multiobjective GA optimization is conducted. Based on the optimization results it is concluded that selecting the satellite total mass in the range of 40-60 kg can be considered as an optimum approach for a university-class microsatellite project with unknown payload(s). Also, the CE methodology is applied to the remote sensing microsatellites conceptual design process. The results of CE application provide a clear understanding of the interaction between satellite system design requirements such as satellite total mass and power and the satellite mission variables such as revisit time and spatial resolution. The MDO application is done with the total mass minimization of a remote sensing satellite. The results from the MDO application clarify the unclear relationship between different system and mission design variables as well as the optimum design base lines according to the selected objective during the initial design phases.

Desenvolvimento de algoritmo de controle de tração para regeneração de energia metroviária - ACTREM: melhoria da eficiência energética de sistemas de tração metroviária.

Esta tese propõe um modelo de regeneração de energia metroviária, baseado no controle de paradas e partidas do trem ao longo de sua viagem, com o aproveitamento da energia proveniente da frenagem regenerativa no sistema de tração. O objetivo é otimizar o consumo de energia, promover maior eficiência, na perspectiva de uma gestão sustentável. Aplicando o Algoritmo Genético (GA) para obter a melhor configuração de tráfego dos trens, a pesquisa desenvolve e testa o Algoritmo de Controle de Tração para Regeneração de Energia Metroviária (ACTREM), usando a Linguagem de programação C++. Para analisar o desempenho do algoritmo de controle ACTREM no aumento da eficiência energética, foram realizadas quinze simulações da aplicação do ACTREM na linha 4 - Amarela do metrô da cidade de São Paulo. Essas simulações demonstraram a eficiência do ACTREM para gerar, automaticamente, os diagramas horários otimizados para uma economia de energia nos sistemas metroviários, levando em consideração as restrições operacionais do sistema, como capacidade máxima de cada trem, tempo total de espera, tempo total de viagem e intervalo entre trens. Os resultados mostram que o algoritmo proposto pode economizar 9,5% da energia e não provocar impactos relevantes na capacidade de transporte de passageiros do sistema. Ainda sugerem possíveis continuidades de estudos.

Fail-Safe Testing of Web Applications

This dissertation introduces an approach to generate tests to test fail-safe behavior for web applications. We apply the approach to a commercial web application. We build models for both behavioral and mitigation requirements. We create mitigation tests from an existing functional black box test suite by determining failure type and points of failure in the test suite and weaving required mitigation based on weaving rules to generate a test suite that tests proper mitigation of failures. A genetic algorithm (GA) is used to determine points of failure and type of failure that needs to be tested. Mitigation test paths are woven into the behavioral test at the point of failure based on failure specific weaving rules. A simulator was developed to evaluate choice of parameters for the genetic algorithm. We showed how to tune the fitness function and performed tuning experiments for GA to determine what values to use for exploration weight and prospecting weight. We found that higher defect densities make prospecting and mining more successful, while lower mitigation defect densities need more exploration. We compare efficiency and effectiveness of the approach. First, the GA approach is compared to random selection. The results show that the GA performance was better than random selection and that the approach was robust when the search space increased. Second, we compare the GA against four coverage criteria. The results of comparison show that test requirements generated by a genetic algorithm (GA) are more efficient than three of the four coverage criteria for large search spaces. They are equally effective. For small search spaces, the genetic algorithm is less effective than three of the four coverage criteria. The fourth coverage criteria is too weak and unable to find all defects in almost all cases. We also present a large case study of a mortgage system at one of our industrial partners and show how we formalize the approach. We evaluate the use of a GA to create test requirements. The evaluation includes choice of initial population, multiplicity of runs and a discussion of the cost of evaluating fitness. Finally, we build a selective regression testing approach based on types of changes (add, delete, or modify) that could occur in the behavioral model, the fault model, the mitigation models, the weaving rules, and the state-event matrix. We provide a systematic method by showing the formalization steps for each type of change to the various models.

Asymmetric Bragg mirrors for the reduction of emission wavelength dependence on the viewing angle in organic microcavity light emitting diodes

Organic microcavity light emitting diodes typically exhibit a blue shift of the emitting wavelength with increasing viewing angle. While the wavelength shift can be reduced with the appropriate choice of organic materials and metal mirrors, for further reduction of the emission wavelength shift it is necessary to consider a mirror whose phase shift can partly compensate the effect of the change of optical path within the cavity. In this work, we used a genetic algorithm (GA) to design an asymmetric Bragg mirror in order to minimize the emission wavelength shift with viewing angle. Based on simulation results, the use of asymmetric Bragg mirrors represents a promising way to reduce the emission wavelength shift. Detailed comparison between GA optimized and conventional Bragg mirrors in terms of resonant wavelength dependence on the viewing angle, spectral narrowing, and brightness enhancement is given. (C) 2004 Elsevier B.V. All rights reserved.

Optimizing PCB assembly for family setup strategy

Purpose – The purpose of this paper is to investigate the optimization for a placement machine in printed circuit board (PCB) assembly when family setup strategy is adopted. Design/methodology/approach – A complete mathematical model is developed for the integrated problem to optimize feeder arrangement and component placement sequences so as to minimize the makespan for a set of PCB batches. Owing to the complexity of the problem, a specific genetic algorithm (GA) is proposed. Findings – The established model is able to find the minimal makespan for a set of PCB batches through determining the feeder arrangement and placement sequences. However, exact solutions to the problem are not practical due to the complexity. Experimental tests show that the proposed GA can solve the problem both effectively and efficiently. Research limitations/implications – When a placement machine is set up for production of a set of PCB batches, the feeder arrangement of the machine together with the component placement sequencing for each PCB type should be solved simultaneously so as to minimize the overall makespan. Practical implications – The paper investigates the optimization for PCB assembly with family setup strategy, which is adopted by many PCB manufacturers for reducing both setup costs and human errors. Originality/value – The paper investigates the feeder arrangement and placement sequencing problems when family setup strategy is adopted, which has not been studied in the literature.

Distributed processing, reconfigurable processes and active network

The fast spread of the Internet and the increasing demands of the service are leading to radical changes in the structure and management of underlying telecommunications systems. Active networks (ANs) offer the ability to program the network on a per-router, per-user, or even per-packet basis, thus promise greater flexibility than current networks. To make this new network paradigm of active network being widely accepted, a lot of issues need to be solved. Management of the active network is one of the challenges. This thesis investigates an adaptive management solution based on genetic algorithm (GA). The solution uses a distributed GA inspired by bacterium on the active nodes within an active network, to provide adaptive management for the network, especially the service provision problems associated with future network. The thesis also reviews the concepts, theories and technologies associated with the management solution. By exploring the implementation of these active nodes in hardware, this thesis demonstrates the possibility of implementing a GA based adaptive management in the real network that being used today. The concurrent programming language, Handel-C, is used for the description of the design system and a re-configurable computer platform based on a FPGA process element is used for the hardware implementation. The experiment results demonstrate both the availability of the hardware implementation and the efficiency of the proposed management solution.

Decision support system for vendor managed inventory supply chain:a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

The construction and optimization of an ion mobility spectrometer for the analysis of explosives and drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs.^ In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug.^ Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). ^ The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.^

The Construction and Optimization on an Ion Mobility Spectrometer for the Analysis of Explosives and Drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs. In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug. Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.