Towards Data-driven Simulation Modeling for Mobile Agent-based Systems

Simulation modeling provides insight into how dynamic systems work. Current simulation modeling approaches are primarily knowledge-driven, which involves a process of converting expert knowledge into models and simulating them to understand more about the system. Knowledge-driven models are useful for exploring the dynamics of systems, but are handcrafted which means that they are expensive to develop and reflect the bias and limited knowledge of their creators. To address limitations of knowledge-driven simulation modeling, this dissertation develops a framework towards data-driven simulation modeling that discovers simulation models in an automated way based on data or behavior patterns extracted from systems under study. By using data, simulation models can be discovered automatically and with less bias than through knowledge-driven methods. Additionally, multiple models can be discovered that replicate the desired behavior. Each of these models can be thought of as a hypothesis about how the real system generates the observed behavior. This framework was developed based on the application of mobile agent-based systems. The developed framework is composed of three components: 1) model space specification; 2) search method; and 3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior. The five framework measurement metrics: flexibility, comprehensibility, controllability, compossability, and robustness, are developed to evaluate the overall framework. Furthermore, to incorporate knowledge into the data-driven simulation modeling framework, a method was developed that uses System Entity Structures (SESs) to specify incomplete knowledge to be used by the model search process. This is significant because knowledge-driven modeling requires a complete understanding of a system before it can be modeled, whereas the framework can find a model with incomplete knowledge. The developed framework has been applied to mobile agent-based systems and the results demonstrate that it is possible to discover a variety of interesting models using the framework

Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves

Quantification and Implementation of Structural Damping in MBS Flexible Bodies

In multi body simulation (MBS) using flexible bodies, damping is an important model input to be able to predict the response of a structure with sufficient precision. The purpose of this thesis has been to estimate the structural damping of an experimental setup and implement the estimated damping into MBS-models of the setup. In this thesis the modal properties in terms of natural frequencies, mode shapes and modal damping of a chassis frame with a number of attached components are identified through physical testing. The experimental setup is modeled in the MBS software MSC Adams using both flexible and rigid bodies coupled together, a total of three different models are created. Two with different modeling of the bolted joints in the setup and one where components are modeled as separate flexible bodies and coupled with joints. The modal properties of the models are compared to the identified modal properties of the setup. The simplification in modeling of the bolted joints results in small differences in modal properties of the model compared to more detailed modeling of the bolted joints. Modeling of components as separate flexible bodies results in lesser consistency compared to identified modal parameters, making it more difficult to mimic the damping of the experimental setup. For each of the models a damping function is suggested and implemented,based on the identified modal damping of the physical system. Simulation is then performed, comparing the different models ability to predict the pseudo damage relative to the physical system. The suggested damping functions shows improved ability to predict pseudo damage for Components attached to the chassis frame. The prediction of pseudo damage on the frame side members and cross members is less sensitive to the choice of damping in the MBS-model. Parts of the modeling procedure as well as the implementation of identified damping shows room for improvement and further studies are recommended.In heavy vehicle development, simula- tion models are widely used to evaluate designs and concepts in an early stage in the product development process, eliminating the need of physical proto- types. In this thesis the focus has been to improve simulation models by measuring the damping of a physical system, and im- plementing the damping as a model input. Damping can be explained as the removal of mechanical energy from a system in motion and is the reason that the motion will eventually stop unless energy is supplied to the system at an equal or larger rate than it is dissipated. Many dierent sources of damping exists, in this work the most important one is friction between parts bolted together and within materials such as rubber. Friction transforms kinetic energy into heat mostly. When creating a model of a dynamic me- chanical system, such as a heavy vehicle chassis in this case, knowledge of the damping is of great importance for successful simulations and prediction of for example the life span of the vehicle. In this thesis a simulation model of a part of a heavy vehicle was created, based on an existing experimental setup. The damping was measured in the experimental setup and from the measurement results damping was implemented into the simulation model. The simulation results were nally compared to the physical measurements. The results show that knowledge of and correct modeling of the damping in the system is crucial in order to obtain reliable results from simulation models. Implementing damping based on measurements can greatly improve the simulations, however the implementation itself proves not to be straight forward. The study also shows that the detail level in the modeling together with the modeling of the bolted joints also aects the ability to successfully predict the behaviour of the system. In conclusion the study contributes to increasing the knowledge of damping in the physical system as well as in the model and how the model can be improved. However, further improvements can be made and the thesis suggests future studies in the field. As the development of computers continues at a fast pace, the amount of computational power we have access to is growing. More computa- tional power means that we can create better, more detailed and more complex simulation models. For the models to be useful they must be veried and compared to real measurements. The use of this thesis is to verify the currently used models, showing strengths and weaknesses. The thesis also suggests how to improve the current models with respect to damping. Our models will only serve useful if we are aware of their limitations. The work in the thesis was conducted in cooperation with heavy vehicle manufacturer Scania, who provided the necessary parts, equipment and knowledge. Figure 1 shows the experimental setup, which was subject to random vibration while the acceleration was measured at dierent locations. From the acceleration data information about energy dissipation and the deformation of the dierent parts was obtained. The information from the physical experiment was then used to make the simulation model shown in Figure 2 mimic the motion of the physi- cal system, using the same random vibration sig- nal as input. Improving the simulation models opens up the possibility in the future to completely remove the need for physical prototypes and physical testing. As the simulation models are easily modied more concepts can be tested and optimized virtually, resulting in better products. From the results it is shown that the damping of the experimental system is successfully identi- ed and implemented into the simulation model. The implementation leaves room for improve- ment. Finally it is concluded that the prediction of the lifespan of the involved components is im- proved through better knowledge and modeling of the damping in the system

NX 10 for Engineering Design -- Learning Edition

NX is one of the world’s most advanced and tightly integrated CAD/CAM/CAE product development solution. Spanning the entire range of product development, NX delivers immense value to enterprises of all sizes. It simplifies complex product designs, thus speeding up the process of introducing products to the market. The NX software integrates knowledge-based principles, industrial design, geometric modeling, advanced analysis, graphic simulation, and concurrent engineering. The software has powerful hybrid modeling capabilities by integrating constraint-based feature modeling and explicit geometric modeling. In addition to modeling standard geometry parts, it allows the user to design complex free-form shapes such as airfoils and manifolds. It also merges solid and surface modeling techniques into one powerful tool set. This self-guiding tutorial provides a step-by-step approach for users to learn NX 10. It is intended for those with no previous experience with NX. However, users of previous versions of NX may also find this tutorial useful for them to learn the new user interfaces and functions. The user will be guided from starting an NX 10 session to creating models and designs that have various applications. Each chapter has components explained with the help of various dialog boxes and screen images. These components are later used in the assembly modeling, machining and finite element analysis. The files of components are also available online to download and use. We first released the tutorial for Unigraphics 18 and later updated for NX 2 followed by the updates for NX 3, NX 5, NX 7 and NX 9. This write-up further updates to NX 10. Our previous efforts to prepare the NX self-guiding tutorial were funded by the National Science Foundation’s Advanced Technological Education Program and by the Partners of the Advancement of Collaborative Engineering Education (PACE) program

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

High performance computing based simulation for healthcare decision support

Due to the complexity and crucial role of an Emergency Department (ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulation. The objective of this research is to grasp the non-linear association between macro-level features and micro-level behavior with the goal of better understanding the bottleneck of ED performance and provide ability to quantify such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. Instead of describe all the potential causes of this complex issue. Rather, in this thesis, a layerbased application framework will be presented to discover knowledge of a complex system through simulating micro-level behaviors of its components to facilitate a systematic understanding of the aggregate behavior

Ontology engineering for simulation component reuse

Commercial-off-the-shelf (COTS) simulation packages (CSPs) are widely used in industry, although they have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organizational use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architectures provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontologies to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community