System-of-Systems Modeling and Simulation of a ... - Semantic Scholar

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A, VOL. 0, NO. 0, APRIL 2007

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System-of-Systems Modeling and Simulation of a Ship Environment with Wireless and Intelligent Maintenance Technologies Vishal Mahulkar, Shawn Mc Kay, Douglas E Adams, Alok Chaturvedi

Abstract— Modeling and simulation environments are needed to support decision making in Navy Warfighters, which are emergent systems that pose a challenge to operations management. Ships consist of complex interconnected systems such as the infrastructure, crew, and workflow. A system-of-systems approach using agent-based modeling is applied here to develop workflow simulations involving a ship’s crew conducting routine maintenance, watch duty, and reporting functions. Simple models are used to describe basic behavioural traits and intelligence in crew members; machinery including sensors for intelligent maintenance; equipment consuming power; mobile and stationary communication network access points; models for data transfer over the network; crew mobility models; power distribution and trimming models for the electrical system; and a fire model to simulate emergency scenarios. The simulation results demonstrate an increase in machine availability due to the implementation of intelligent maintenance systems. The effects of wireless network usage on crew resource utilization and overall ship capability in normal operational scenarios are also demonstrated. A simple rescheduling algorithm is used to improve crew utilization and estimate manning requirements. The effects of emergency scenarios such as fires in different locations are also studied. Sensitivity analysis is presented to verify the developed model and a note on validation is given.

The Navy currently has an initiative to “anticipate and manage” the impact of new technologies on ship capabilities and the crew especially relating to manning levels. The high costs of new technology, additional training of crew members, and other infrastructural changes are not always justified. Because manning requirements represent the largest expense onboard ships, it is essential that the impacts of new technologies on the crew and workflow be evaluated early in the technology planning process. In order to examine performance issues that affect a variety of stake holders including the ship commander and crew, a systematic approach to prediction in ships equipped with new technologies is essential. In the following sections, we establish a case for modeling a Navy Warfighter environment as a system-ofsystems. An agent-based methodology is then presented for modeling such complex interconnected systems. The model description is followed by a discussion of some interesting, and counterintuitive results obtained from this model. Finally, attempts at verification of the developed model and a note on validation is presented.

Index Terms— System-of-systems, agent-based modeling, ship, Navy Warfighter.

II. P ROBLEM S TATEMENT AND C LASSIFICATION

I. I NTRODUCTION

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N order to achieve aggressive performance requirements, many organizations are working towards the integration of existing systems into network-centric, knowledge based “system-of-systems.” For example, the defense sector aims to increase readiness and reduce vulnerability by developing strategies for integrating legacy systems with emerging technologies such as wireless networks and intelligent maintenance systems. As DeLaurentis et al. point out in [1], there is a tradeoff between the design of a product to optimize its own performance and the design of a network to optimize its overall performance. System-of-systems approaches analyze this tradeoff by examining subtle interactions, which take place between the various systems. Manuscript received xx xx xxxx; revised xx xx xxxx. This work was supported by the NSWC Crane. V. Mahulkar is with Purdue University, S Mc Kay is with Purdue University, D. Adams is with Purdue University, A. Chaturvedi is with Purdue University and Simulex Inc.

A consistent problem in military acquisition is the application of technologies that have passed their prime and are almost obsolete. The repercussions of this phenomena is the high cost in supporting technology that have become obsolete in the commercial sector and the missed opportunity to use the technology while it was in its prime. The risk of new technology insertions can be paramount. A classic example is when the USS Yorktown became ‘dead in the water’ due to a new technology insertion [2]–[4]. A simple change in one system can cascade throughout the ship’s system-of-systems in a way that can create severe vulnerabilities to structure and human life. The design and engineering of complex systems based on rapidly changing technologies continues to be a challenge. Future naval system architectures must be designed with a system-of-systems focus to take into account the complex interaction of the heterogeneous systems in the early stages of the design. Current design tools lack adequate flexibility and fidelity to accurately model large architectures and instead focus on optimizing the design of individual platforms and sub-systems. Currently, over 800 such models have been documented at the Navy Modeling and Simulation Office

c 2007 IEEE 0000–0000/00$00.00


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TABLE I M AIER CRITERIA FOR N AVY SHIP SYSTEM - OF - SYSTEMS Traits Operational Independence

Crew, Commander, equipment

Managerial Independence

Crew, Commander

Evolutionary Development

Transition from wired to wireless technologies

Emergent Behaviour Geographical Distribution

Fig. 1.

Examples

Interaction between crew and wireless technology results in changes in processes Location of ships, crew, supplies

Navy-Warfighter as a system-of-systems

(NMSO). A design tool is needed to rapidly explore the design space, examine the interactions between capabilities and technologies, evaluate the effects of technology obsolescence, and optimize the ship system-of-systems. The system-of-systems model presented in this paper systematically explores the impacts of inserting wireless technology in the Navy Warfighter. This methodology can quickly identify critical design criteria and potential vulnerabilities early in the design enabling an accelerated acquisition process and avoidance of severe vulnerabilities. A. System-of-systems The exact definition of system-of-systems is still being debated. Researchers have developed several definitions [5]– [11] and the work by Keating, et al. [12] lists a summary of several of these perspectives. For example, Keating and his co authors view system-of-systems as “metasystems comprised of multiple autonomous embedded complex systems that can be diverse in technology context, operation, geography, and conceptual framework”. The Department of Defense describes [13] a system-of-systems as “a set or arrangement of independent and useful systems integrated into a larger

system that delivers unique capabilities.” Despite these varied perspectives, there is a general consensus that such problems satisfy certain common characteristics, called the Maiers’ Criteria [14]. The Maiers’ criteria as applied to the problem under consideration is explained in Table I. A Navy Warfighter is comprised of complex interconnected infrastructure such as the electrical power systems, electric propulsion, high energy weapon systems, transmission systems, communication systems, etc. These systems interact with crew directly or over the ship board network during maintenance jobs, repair work, reporting activities, and watch duties, as a part of standard business processes. These interconnected infrastructure, business processes, and crew as illustrated in Fig. 1 can function independently in some cases but must interact horizontally, e.g., crew-to-crew, infrastructureto-infrastructure and vertically, e.g., crew-to-infrastructure, to efficiently execute mission objectives and maintain tactical and informational advantage in war scenarios [15]. These interactions are illustrated using the following two examples. •

Electrical power generation systems, electrical distribution systems and high energy weapons systems require cooling. The cooling is provided through a distributed


•

chilled water system. Electrical power is required to chill and circulate the cooling water. High energy weapons systems generally put heavy transient loads on the power and cooling system. The automated system must be able to distinguish between normal and emergency scenarios and dynamically reconfigure these interdependent systems to meet the requirements [16]. These strong interconnections can cause faults to cascade leading to catastrophic failure. Another logistics and maintenance scenario is illustrated in Fig. 2. An undetected externally detonated explosion causes a hull fire. Following the explosion, crew members in the vicinity, who were performing routine ship’s operations, are directed to extinguish the fire. The anxiety of the crew causes heavier than normal breathing resulting in rapid depletion of oxygen fire-fighting gear. In the meantime, the crew members extinguishing the fire experience a manual water valve failure. This failure needs to be communicated to crew members near the power distribution facilities, so that greater water pressure can be provided by shutting down less critical subsystems and distributing more power to the water pumping facility. At the same time, the ship must be moved away from the site of external attack and weapons stations must be prepared to combat external threat. The question regarding this

Fig. 2.

Flowchart illustrating maintenance and logistics failure scenario

example is: In the event of an external threat, does the information technology infrastructure enable the ship to efficiently and effectively extinguish the fire and at the same time execute other ship operational procedures? We thus aim to develop tools that will reduce the time and cost of technology insertion by anticipating and managing the impacts specifically to mission readiness of the total ship system-of-systems.

shown to address the emergent and evolutionary nature of system-of-systems [1]. 1) Network Theory: Network theory is a powerful tool in applications where the nature of connectivity amongst the participating systems becomes important. An example of this is the spread of disease in social groups. A host of mathematical techniques are available for identifying and classifying patterns in networks and deducing important statistical properties of network topologies. An excellent review of developments in the field of network theory is presented in [17]. 2) Agent-based Simulations: Traditional scientific methods of analysis and deduction are not sufficient to explore the problem domain of system-of-systems. Agent-based models are a relatively new and important approach to representing and exploring the phenomena associated with heterogeneous interacting agents. The main goal of agent-based models is to observe the system-wide emergent behaviour by modeling simple behaviours in entities at lower levels and an environment in which they can interact. The various motivations for using agent-based models in situations involving social processes are described in [18]. According to Bonabeau, emergent phenomena are important not just because they are difficult to predict but because they can sometimes produce counterintuitive results [19]. The important benefits of agentbased modeling are that it captures emergent phenomenon, it is flexible, and it provides a natural description of the system [20]. On the other hand, there are a few limitations to this methodology as well. 1) Any model built using agent-based methodology is application specific, no general purpose model can be built. 2) Agent-based models in the social sciences often involve human agents, with potentially irrational behavior, subjective choices, and complex psychology which are difficult to quantify, calibrate, and sometimes justify. 3) The last major drawback is the computational complexity of such a simulation due to the need to describe individual behaviour at constituent level and the varied interactions. III. M ODELING A PPROACH An agent-based model is based on two main sources: literature and experience. The resulting model is realistic to some degree, and although it is complex, it still remains a considerable simplification. This simplicity leads to some obvious limitations. For example, it might be possible to Crew Mobility Model

B. Solution Methodologies Because of its heterogeneous nature, a system-of-systems has strong ties to several established disciplines such as complexity, systems engineering, and architecting. Each systemof-systems problem is unique due to the presence of various system types (physics based versus socio-psychological), the level of control (central versus distributed), and the level of connectivity between the components (dense versus sparse). Agent-based modeling and network theory are two particularly promising methods being applied to problems of this type. They have been successfully applied in application containing human-machine interactions. These approaches have also been

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Network Model

Power System Model

Synthetic Environment

Maintenance Model

Fig. 3.

Power Generation Model

Modeling approach

build an extremely complex model of human cognition with


Fig. 4.

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Simulation Environment: refer Table II

hundreds of parameter, but it will be difficult to tune these parameters to obtain a sensible response. This is especially true when there are heterogenous interacting agents in the environment. Midgley et al. argue that the emphasis should be on minimalism [21]. This is also reiterated by the sensitivity analysis presented later in section V-A on verification of the model, which shows that very few factors actually make a significant impact on outputs. With this in mind, we next describe simple models used as building blocks for the ship environment. An agent-based system-of-systems model has been developed in MATLAB to represent a zonal section of a legacy-class ship. The modeling approach that was used is depicted in Fig. 3. The simulation environment incorporates simple models of the crew behavior, network, power distribution and trimming strategy, workflow and emergency fire response. It is also possible to incorporate traditional time-based simulation models (e.g., generator models, engine models), which interact with the environment dynamically. Emergent behaviour results from interaction of all these models. The simulation environment utilized in this paper consists of 10 crew agents, 6 communication access points, 2 workstations, 3 servers, 5 major pieces of machinery, 5 watch locations, 3 pieces of firefighting equipment, and recreational

TABLE II L EGEND F IG . 4

unit

description

Green squares Yellow squares Small red squares Small cyan squares White squares Red circle Grey circle

Access-points Servers Crew Equipment (general/fire fighting) Watch locations Fire extent Smoke extent

and other equipment, which consume power. It is possible to define user defined scenarios using an XML definition file. The front end of the simulation environment is shown in Fig. 4. A. Crew Behaviour and Mobility Model The conceptual representation used for developing the behavioural model is rule-based and event-driven and is a simplification of the representation developed by authors in [22]. The behavioural model includes a finite list of attributes and limited intelligence, and the agent behaviour results from a set of rules of engagement based on the interaction of the crew


Fig. 5.

Crew behaviour and workflow implementation

(a) Section of ship layout without doors

Fig. 6.

(b) Section of ship layout with doors

• • •

Maximum speed of movement, Current stress level, Current training level and, A simple memory model with long-term and short-term memory with corresponding memory decay rates.

Further, the crew members have: • • •

•

(c) Section of ship layout with extracted doors and rooms

Extraction of geographical information

members and the environment. An example of such a rule is: “if the agent notices that its fatigue level is high and provided there is no emergency, it will take a recreational break”. The list of attributes included in the model are: •

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Information about their location, Knowledge of the geography of the environment, Knowledge of location of all other equipment and systems in the environment, Knowledge about their current tasks and the list of tasks to be accomplished, and

Awareness about their fatigue level — this awareness is accomplished using the concept of utilization, which is the amount of time a crew member is busy starting from time 0. Fig. 5 shows a partial representation of various states (boxes) a crew agent could occupy and the conditions for entering and exiting those states (arrows). For example, if the crew member has nothing to do, it is in an idle state (upper left hand box, Fig. 5). As soon as it receives a new order, it moves to the move state. This flow is used as the basis for implementation. Movement model: The environment allows a user to load any geography as long as the layout is available as an image in TIFF format. Based on this layout the location of doors and rooms can be automatically extracted, Figs. 6(a)—6(c). These locations are used as waypoints for crew members who navigate the geography. Dijkstra’s algorithm [23], [24] is used for navigation by finding the shortest path between two rooms. The speed of movement is limited by a crew members •


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of competition. This process continues until no more clients are present. In effect, only one client is connected at a time to one channel or access point [26].

maximum speed attribute. B. Network Model Currently, most Navy ships use wired local area network (LAN) as one of the means for communication and reporting functions. There is a proposal to add a wireless local area network (WLAN) on the established LAN with the aim to improve crew efficiency and reduce manning costs. The objective is to identify the advantages/disadvantages of introducing this new technology through the modeling and simulation process before committing to the investment. The bottleneck in a LAN plus WLAN combination is usually the WLAN because the LAN operates at a much higher bandwidth compared to the

Network NetworkNode

InUse

[noPowerSupply] 1

1

Fig. 8.

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Access Point/ Terminal Machine

Crew Crew

Fig. 7.

4

NetworkNode 7

NetworkNode

5

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NetworkNode

Machine

Access Point/ Terminal


3

NetworkNode 6

NetworkNode

1111111 0000000 0000000 1111111 0000000 1111111 0000000 1111111 Server 0000000 1111111 0000000 1111111 0000000 1111111

[power available]

transfer interrupted;

Crew

Machine

[noPowerSupply]

Unavailable

2

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1

Idle

2

[done OR transfer 2 interrupted]

NetworkNode


[Occupied]

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WLAN (10 to a 100 times larger). Hence, only the WLAN will be examined in the network model presented here. An 802.11b backbone is assumed. 802.11b protocol has a maximum raw data rate of 11 Mbit/s operating in the frequency range of 2.4 GHz. It is usually used in a point-to-multipoint configuration, wherein an access point communicates via an omni-directional antenna with one or more clients that are located in a coverage area around the access point. The typical indoor range is 30 m at 11 Mbit/s and 90 m at 1 Mbit/s [25]. In the current application, it is assumed that all access occurs within 30 m of an access point and the signal strength goes to zero beyond that range. In the 802.11 protocol, the fundamental mechanism to access the medium is called distributed coordination function (DCF) [26]. The operating frequency range is divided into segments called channels. If there are multiple access points in an environment, the access points with overlapping ranges cannot have the same channels. This constraint reduces the number of usable channels dramatically in multi access point environments. The DCF is a random access scheme, in which the data to be transferred is broken down into packets of fixed size. Before a connection is established, all clients compete for a connection. The client that gets connected is chosen randomly and all others are told to wait for the next round

Network model implementation

A simpler version of this model is described below and is used to calculate transmission delays. The general network structure is shown in Fig. 7. The implementation for an individual node is shown in Fig. 8. The delays on the network are divided into the following categories: • Access delays are associated with accessing a particular application and can be initialized at the start of data transmission. • Transmission delays are associated with actual data transmission over the network. These delays are dependent on the network load and server load. The network load is further divided into the following categories: • A static load is included to account for applications like distance support, which involves ship to shore communication on a regular basis. For implementation, this load is taken to be a fixed percentage of the total load with variations about a mean value. This mean value is fixed randomly or can be read from other sources. • A dynamic load that is due to the network access by the crew to carry out maintenance or other types of activities, sensor data transmission for intelligent maintenance, etc. is also included. This load is calculated during run time and depends on the following factors: – maximum bandwidth available at each network component; – number of active server connections and – number of active access point connections Priority on the scale of 1 to 5 can be assigned to particular transmissions depending on their importance. For example, tactical information like radar or sonar data can be assigned the highest priority while web browsing can be assigned the lowest priority. The priority determines the amount of bandwidth allocated for a particular transmission. Higher priority transmissions are allocated higher bandwidths compared to


lower priority transmissions while same priority transmissions are allocated equal bandwidths. For example, consider access points APl , l = 1, · · · , n connected to a server S as the set of agents Cl are accessing APl where Ωs is the total bandwidth available at the server, Ωl is the total bandwidth available at APl , ∆i is the size of data with agent i, and pi is the priority of data with agent i. The bandwidth available to each agent i is given by: P

pk Ωs

P l min (Ωl , P n agents∈C ) j=1 agents∈Cj pk P ω i = pi agents∈Cl pk

i ∈ Cl

(1)

On average, this simpler model exhibits similar performance to the protocol described in [26]. C. Workflow Model and Scheduling A running simulation can start off in the idle state with no activity or with a predefined state with a set of activities defined for the crew. The information about each activity is obtained from a condensed Navy billet database represented in XML format [27]. More activities can be added to the list of workflows for each crew member during run time either manually or automatically due to failures or other scheduled activities. The activities represented in these models have the following important characteristics:

Fig. 9.

• •

Interface for editing workflow

The details of each maintenance sequence are listed. The triggers for crew activities like maintenance and troubleshooting are generated by either of the following: – Operational failure – Sensor alert

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– Planned maintenance diagnostics – By external user • The kind of activities that can be performed by the crew members have been divided into the following different categories: – Planned maintenance/troubleshooting – Watch duty – General email access/web browsing – Fire fighting – General recreational activity • The time required for each of the activities. • The maintenance aid devices such as laptops, PDA, diagnostic devices, etc. • Reporting applications and device aids such as PDA, workstation, etc. • A scenario can be run over a user-specified time duration and with a user-specified time granularity. The interface used for editing workflows is shown in Fig. 9. 1) Maintenance Technologies: The primary objective of equipment maintenance programs is to preserve system functions in a cost-effective manner [28]. Maintenance activities fall into two broad categories, namely corrective maintenance and preventive maintenance [29]. Corrective maintenance (CM) is performed to restore the functional capabilities of failed or malfunctioning equipment or systems. This is a reactive approach to maintenance because the action is triggered by the unscheduled event of an equipment failure. The costs of such a maintenance policy are usually high due to the following reasons [28]: • the high cost of restoring equipment to an operable condition under crisis situations • the secondary damage and safety/health hazards inflicted by the failure • the penalty associated with lost production Preventive maintenance (PM), on the other hand, is the approach developed to avoid this kind of waste by providing systematic inspection, detection, and prevention of incipient failures. Researchers have classified PM into a number of subcategories based on when the maintenance is carried out. The three main types of PM are [28]: 1) time-directed (TD) in which overhaul/replacement is performed at regular intervals 2) condition-based maintenance (CBM) or predictive maintenance, in which a preventive action is taken only when an onset of a failure is detected 3) reliability-centered maintenance, in which the criticality of failure modes are ranked and guidelines for selecting the most cost-effective strategy are provided CBM is gaining acceptance as a preferred approach to preventive maintenance [28]. A number of model and data based methods are available for Fault Detection and Diagnostics (FDD). A vast body of literature is available on these methods, for example, in survey papers such as [30], [31] and books such as [32]. These intelligent soft-computing algorithms have been applied to boost the validity of maintenance. It is assumed that such “Intelligent Technologies” are available at the crew members’ disposal.


D. Power Generation and Trimming Model Notwithstanding the facility to simulate different time granularity, there is a limitation to the minimum time step that can be simulated. This limitation is not a physical limitation but a practical one because of the nature of the problem at hand. Simulation of actual ship board power distribution models such the one in [33] show that the transients occur on the order of a few milli-seconds to a few seconds. The main idea in a systemof-systems approach is to take a high level view and, hence, a simplified model of power redistribution and trimming is used. The important characteristics of the model are: • The amount of power generated and available for this zonal environment can be controlled using the interface provided to the user or can be varied as a function of time. • Each of the rooms is assumed to consume a predefined amount of power. If the required amount of power is not available, all equipment in that room is taken off line. • Priorities are also assigned to each room depending on their importance. • If the power level falls below the amount required to power all rooms, power trimming is performed starting with rooms having lowest priority as is done in reality on a ship. • The network equipment is assumed to be powered over the Ethernet, i.e., the wireless access points are powered over the Ethernet cable by the server to which they are connected. A power outage in that zone of the ship does not necessarily affect the network equipment unless the server is affected.

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dynamics with reasonable accuracy using software like the Fire Dynamics Simulator (FDS) developed by the National Institute of Standards and Technology (NIST) [34]. An example of fire simulation in a building is examined in [35]. Fire simulations are extremely resource intensive in terms of computational time and thus impractical for this application. A simplified fire model is thus used and has the following characteristics: • The fire and smoke are assumed to spread in a circular fashion with a fixed rate. • All activity in the section of the ship stops and all agents which are not involved in firefighting activities are moved to a pre-specified room to simulate evacuation. • It is assumed that there is an unlimited supply of firefighting equipment. • The rate at which fire recedes is dependent on the training level of the crew members and the number of crew members fighting the fire. • The number of crew members dispatched increases until the rate of growth of the fire becomes negative. • Once a fire is extinguished, the ship returns to its normal activities. Fig. 10 shows the interface used to start and stop fires event. F. Reconciling Time Granularity In simulation environments such as the one described here, where models with widely differing aggregation and granularity are integrated, it becomes important to consider the time scales at which these models operate. If a model requiring very fine granularity is operated on a coarse time scale, no useful information can be extracted from the results. Typically, such systems dictate a single time resolution based on the requirements of the model with highest resolution (finest/lowest granularity). Such a strategy does not scale well to a large number of diverse agents operating at different resolutions resulting in considerable waste of resources and large system overheads. However, in the case of small systems with largely uniform agents, they are easy to implement and they work well [36]. All the models in the developed simulation environment thus operate at the same time granularity dictated by the model requiring the highest resolution, which turns out to be the network model. The environment also allows for a variable, user defined simulation resolution. However, once the resolution is chosen, all the systems and agents operate at that resolution. G. Outside World

Fig. 10.

Interface for starting stoping fires

E. Fire Model Fire is used as a representative emergency scenario on a Navy ship. It has been reported that small fires occur regularly on ships. It is possible to simulate fire and smoke

It is helpful to allow the simulation environment to interact with other applications and models to allow for scalability. This also assists in making the system boundaries porous, since in reality a section of the ship does not function in isolation and needs to exchange information and mass with other parts of the ship. The MATLAB simulation environment also has the capability to communicate to other applications written in languages like JAVA, C, etc. over the internet. The communication between the MATLAB environment and another JAVA


based model was implemented using a two-way client server paradigm. The two models can be initialized and started asynchronously, yet their execution is coordinated and synchronized through the use of message passing over the internet. A server is started on the machine that runs the MATLAB environment, while another server is started on the machine that runs the other model. The external model can trigger the MATLAB environment by passing all the required input conditions via the server and vice versa. The input conditions can be used to dictate the execution cycle, including the number of units in time to execute, before passing the results (or the outcomes) of the simulation back to other model over the Internet. The MATLAB environment can then go into a sleep state, waiting to be triggered again by the outside world or continue executing as per the requirement. The independence of the two models with different temporal and spatial granularities highlights the need to translate information passed to and from the MATLAB environment into a more understandable format. A communication architecture was implemented to allow for scalability in terms of the number of simultaneous simulations that could be connected, synchronized, and are able to communicate with one another while minimizing the impact on individual simulation performance (amount of time needed to complete a simulation cycle once triggered). IV. S IMULATION R ESULTS All the simulations were performed on a small section of the ship, assuming a closed boundary so that interactions between different components within the model can be studied in detail. Some of the important results obtained are presented here. A. Intelligent Maintenance Studies conducted by commercial airlines, NASA, USAF Air Combat Command, and ONR report high maintenance burdens and costs due to inadequate diagnostic tools and scheduled maintenance practices. Engine operation and maintenance personnel report a need for better diagnostic/prognostic tools to identify and isolate the root-cause of failures, which lead to an increased diagnostic time, inventory build up, and component test time, which will in turn affect the mission readiness of a ship [37]. A set of simulations were performed to demonstrate the effectiveness of intelligent maintenance technologies at addressing some of these issues. Scenario Description: The crew members are required to complete two sets of tasks over a period of two days onboard a ship. Each set consists of some number of preventive maintenance tasks, repair tasks and watch duty tasks. Two scenarios were compared: • Scenario (a): with intelligent maintenance deactivated, a degradation in performance is introduced in a machine at simulation time T=50. • Scenario (b): with intelligent maintenance activated, a degradation in performance is introduced in a machine at simulation time T=50. A simple linear degradation model is assumed for the machinery. More complicated models can be incorporated if required. It is assumed that intelligent maintenance technologies can

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categorize failures when the performance falls below 85% with 90% accuracy. Results and Discussion: Fig. 11 shows the comparison of the two scenarios with respect to the workflow completion of individual agents (Fig. 11(a)) and the overall work completion (Fig. 11(b)). The solid line is for the scenario with intelligent maintenance activated and the dashed line is for the scenario with intelligent maintenance deactivated. Fig. 11(a) shows the comparison of individual workflows of crew members affected by the degradation and fault. Some crew members (e.g., numbers 8 and 9) are affected directly because they are assigned the task of diagnosing and repairing the faulty machine, whereas some crew members (e.g., crew numbers 3 and 7) are affected indirectly due to changes in network activity caused by the directly affected crew members. Comparing the total workflow as seen in Fig. 11(b), the benefits of intelligent maintenance becomes more evident. The effect of rescheduling due to a fault is visible in both figures after T=50s. In scenario (b), where intelligent maintenance is activated, the first set of workflows is completed before the second set starts, whereas in the second case, the first set of workflows are still going on when the second set gets assigned. These figures imply that crew members can utilize their time more efficiently when intelligent maintenance technologies are used. The advantage is also further exemplified when the machine health curves in Fig 13 are considered. This result suggests that intelligent maintenance technologies can help reduce the manning requirements onboard ships by allowing crew members to utilize their time more efficiently. The overall utilization of crew members is also lower and the machine availability increases. This result is significant because manning today represents the largest operating cost onboard ships and there is an ongoing initiative to decrease costs by reducing manning requirements. A note to be made is that false positives have not been included in the automated diagnostics, because a false positive will affect the factors such as machine down time and crew workload the same way as a correct diagnosis. Further the IM is a continuous monitoring system with the diagnostic algorithm evaluated after every 10 MB of new data has been received. So, if the IM misses a diagnosis, it might get caught in the next evaluation. A negative effect of intelligent maintenance technologies is the data load on the network. Fig. 12 shows the comparison of the network activity pattern for the two scenarios. The load does not appear significant since intelligent maintenance is enabled for only one machine. With multiple machines and multiple sensors on each machine, the load might become significant. This problem can be alleviated by introducing new technologies such as a wireless network with sufficient coverage and redundancy. The cost of this extra investment will be offset by lower manning requirement costs. The following set of results clarifies the advantages of investing in wireless technology onboard ships.


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1

Wireless access points TABLE III LEGEND FOR FIGURES 14 AND 15

B. Return on Investment Two networks with the same layout and same number of network nodes are compared. In the first case, a wired LAN is studied and in the second case a WLAN on a wired backbone is studied. Figs. 14 and 15 show the comparison of average workflow completion times for the same workflow routine. All wired 450

Case Number

Description

1 2 3 4 5 6

All Access points/Workstations Operational Access Point/Workstations 1 failed Access Point/Workstations 2 failed Access Point/Workstations 3 failed Access Point/Workstations 4 failed Access Point/Workstations 5 failed

400 350

Cost

300

Spread

Nominal

by diminishing the cascading effect of catastrophic single point failures in the network.

250

C. Effect of Changes in Wireless Range on Efficiency

200 150 100 50 0

1

2

3

4

5

6

Case

Fig. 14.

Wired workstations

Case 1 represents the baseline scenario (i.e., all nodes are working). Other cases represent situations where one of the nodes is rendered inoperable due to an exigency, for instance, a fire, ship to ship collision, or an external attack. Fig. 14 and 15 make it clear that having a wireless network reduces the average completion times. The overall variance is also lower thereby increasing the confidence in the result. This result implies that a wireless infrastructure increases the overall robustness of the network to failures (such as access point failures due to fires, explosion, and random failures). This robustness will also have a direct effect on crew utilization

A wireless network increases robustness but the technology used for implementing the network must be selected with care. Fig. 16 shows the comparison of workflow finishing times as a function of wireless range and demonstrates the dependence that workflows have on different technologies. The vertical bars in the figure represent the standard deviation in finishing times. When the wireless range is small, there is no overlap of signals from two or more access points. In this situation, the variance of finishing times of workflows when crew members are using wireless devices is small. As the wireless range increases, it is seen that overlap occurs in certain regions as shown in Fig. 17. Because of the assumptions that the signal strength from all access points in the overlap region is the same, the access point to which the wireless device connects is chosen randomly. This leads to variability in workflow finishing times represented by the error bars in Fig. 16. The result also explains the increasing cost of workflow completion as the range is increased above 30m — as the access point gets chosen randomly, some access points may become overcrowded resulting in each connection getting a smaller bandwidth. This result implies that wireless technology used for implementation of a network depends on the type


Simulation 90 Scale [0−1]

180

1 Utilization

170

0.5

150

0

140 Machine availability

Finishing Times

160

130 120 110

Fig. 16.

0

10

20 30 Wireless Range (m)

40

0

5

10

15

20

25

0

5

10

15

20

25

0

5

10 15 Time (hours)

1 0.9 0.8 0.7

0.8

50

Work completed

100

12

Effect of changing wireless range

0.6 0.4 0.2 0

10 3

20 25 Without Rescheduling With Rescheduling

9

Fig. 18.

Range AP 2

Comparison of outputs with and without rescheduling algorithm

2 8

The agent can access AP 2 as well as 4

7 4 6

5

4

1 Range AP 4

4

0

Fig. 17.

1

2

3

4

5

6

Explanation for decreasing efficiency

of application. In this case, 30m is chosen to be the range because some amount of overlap between connection ranges for the access points is crucial to maintain robustness. D. Effect of Rescheduling Efforts to improve crew efficiency and reduce manning requirements onboard ships using new technologies can be enhanced by introducing an optimization strategy for scheduling and reassigning crew workflows. To demonstrate this improvement, a scenario is implemented with a specified number of failures, preventive maintenance jobs, and watch duties over a period of 24 hours in the section of ship shown in Fig. 4. Important outputs defined are: • Utilization: This is the ratio of the time a crew member is engaged in completing tasks to total time. • Machine availability: This is the ratio of the amount of time a machine is available to perform its tasks to total time.

Total workflow: This is the ratio of total number of activities completed to total number assigned. Fig. 18 shows typical outputs obtained from a 24 hour run. The solid line represents the case when the workflow is assigned at the beginning of the simulation. As can be seen, the overall utilization is relatively high at 80% meaning the crew members are busy 80% of the time. It would be advisable to maintain the crew utilization around 60% to avoid over working some of the crew members. To accomplish this, a simple ad hoc workflow rescheduling strategy is employed. The strategy minimizes the variance of lengths of workflows of the crew members and allocates recreational activities (e.g., sleeping, exercising, resting, etc.) to crew members with high utilization. The algorithm also reassigns tasks from crew members with a large backlog to other crew members. The strategy is represented in Algorithm 1. Instead of assigning a few crew members excessive amounts of work, the strategy aims to redistribute the work so that all crew members are equally utilized. The effect of the strategy on the same 24 hour scenario is plotted in Fig. 18 with a dotted line. The utilization has been maintained at around 60%, and at the same time, the machine availability has increased and the amount of total work done has also increased. A more complicated strategy, based on integer programming concepts can be implemented by taking into consideration the following factors: • The skill of the crew members, • The training level of the crew members, • The length of time required for all the tasks in the queue, • The tasks of other crew members and their network utilization, and • Other external conditions such as emergency scenarios. •

An important conclusion of this analysis is that it


Algorithm 1 Rescheduling Algorithm currentAgent = agn currentAgentWorkflow = AgentWorkflowagn if utilizationagn > 0.6 then addRecreationalActivity(agn) workflowLengthagn = calculateWorkflowLength(agn) for i = 1 to Nagents do workflowLengthi = calculateWorkflowLength(i) end for P workflowLengthi averageLength = i Nagents if workflowLengthagn > averageLength then lessWorkedAgents = findAgentsWithSmallerWorkflowQueue() lessWorkedAgents = sort(lessWorkedAgents) for i = averageLength to workflowLengthagn do priority = getPriority(AgentWorkflowagn(i)) insert(AgentWorkflowagn(i), priority, lessWorkedAgents(1)) remove(AgentWorkflowagn(i),agn) end for end if end if

is possible to estimate the manning requirements onboard ships. If at the end of the simulation time, it is found that the machine availability is too low or the amount of work done is too low, the result implies that the number of crew members is less than the number required. On the other hand, if it found that the utilization drops below 60% with rescheduling while maintaining the availability and workflow requirements at allowable levels, the number of crew members are more than required and can be reduced. E. Effect of Fire In the last section it was demonstrated how manning can be reduced, but it is critical to evaluate impacts of emergency situations (e.g., fire) with the available number crew. The effect of a fire on the workflow completion and crew utilization varies depending on the location and severity of the event. The protocol implemented during a fire scenarios is given in section III-E. This simulation is a first step towards understanding how manning is affected by these events. This section presents results for a 24 hour scenario; the fire is started in different locations at hour 1 and takes about 4 hours to extinguish due to fire severity and crew capabilities. The four locations considered are shown in Fig. 19: 1) G1: General corner location away from other equipment. 2) G2: General central location, away from other equipment. 3) S1: A location near the server. In this case, the server becomes inoperable resulting in loss of access points connected to it and the corresponding network coverage. 4) AP1: A location near an access point. In this case, only the access point becomes inoperable resulting in loss of

13

Fig. 19. Fire Locations. G1: General corner location, G2: General central location, S1: A location near the server, AP1: A location near an access point/workstation

coverage due to that access point. Fig. 20 shows the results for the 5 cases — 4 fire scenarios and one benchmark scenario without any emergency situation. The main outputs plotted are the crew utilization, the total task completion and the machine availability. The utilization graph clearly shows the branching of the trajectories due to the fire event. A second branching is observed at hour 5, (as shown in the inset Fig. 20), after the fire has been extinguished and can be attributed to the different locations of the fires. From the workflow completion plot (Fig. 20(b)), it is clear that a fire at the server has the maximum impact on the workflow completion followed by a fire at an access point, followed by one in a corner location, and a fire at a central location has the least impact, since fire in a central location is more easily accessible and can be extinguished easily (the loss of human life is not considered here). The utilization also follows the same order as workflow completion and reinforces the fact that fire in a central location is least disruptive. The workflow plot shows an artificial jump at hour 5, the time at which the fire gets extinguished. This is due to the fact that evacuation is also considered as a workflow activity and when the fire is extinguished, this activity is completed and hence the jump at hour 5 is seen. This jump does not change the interpretation of the results since the size of jump is the same in all the cases. This analysis implies that if loss of human life is not considered, a fire in a central location will allow crew members to extinguish it more efficiently and thus finish more amount of work in the same time. V. V ERIFICATION

AND

VALIDATION

Verification and validation of agent-based models is difficult because of the heterogeneity of agents and the possibility of emergence of new patterns of behaviour at the macro-level as a


G2 − central G1 − corner AP1 − access−point S1 − server No Fire 0.9 0.3 0.8

0.25

0.7

0.2 4.5

5

5.5

6

22

23

24

25

Average total Utilization

1

0.5

(a)

0

0

5

10

15

20

25

0

5

10

15

20

25

0

5

10 15 Time (hours)

20

25

Average total Work

1

0.5

(b)

0

Average total Availability

1

0.5

(c)

Fig. 20.

0

Effect on outputs due to fires in different locations

result of interactions at the micro-level. This section describes the work that was done to verify and validate the model. A. Verification Verification is the process of ascertaining that the software correctly implements the conceptual model. A number of formal and informal methodologies have been proposed by researchers for verification purposes. Some of the formal methods include source code analysis, automatic theoretic verification, finite state verification, automata based verification, extreme bounds testing and sensitivity analysis [21]. Over a period of 4 months, the code was extensively tested by: 1) Extreme bound testing: A total of 83 variables and parameters were identified in the code and were grouped to obtain 42 independent factors. A level IV fraction factorial design developed 128 different scenarios that systematically explored the simulation space using the maximum ranges of the 42 factors. The results of this analysis did not yield any unexplainable outliers or nonsensical results. Extreme bound testing also helped identify some minor coding errors, which were corrected and subsequent simulation runs have been error free. 2) Sensitivity Analysis: The same factors identified in the

14

extreme bound testing were also used for the screening sensitivity analysis. Four outputs examined in the sensitivity analysis were crew utilization, machine availability, workflow completed, and average server load. Each of the 128 simulation developed by the fraction factorial design represented a ship tour of about 2 weeks. The reason for choosing this duration was to allow the outputs to reach a steady state. A typical day schedule implemented in a scenario is shown in Table. IV. Analysis of variance (ANOVA) was used to examine the main effects for each of the four outputs. The p-value was used to determine the factors that have significant effect on the outputs. The null hypothesis for the sensitivity analysis is that the factor will not have an effect on the output and the p-value gives the probability that a factor satisfies the null hypothesis. A significant factor threshold of 0.0005 was used for the pvalues. Table V lists some of the important results from this analysis. The highlighted cells indicate that the pvalue is less than the threshold, meaning the factor has significant influence on the corresponding output. For example, the agent stress level has a significant impact on the overall crew utilization, machine availability and total task completion. This result is logical because stress level is directly related to the time required to complete tasks and the quality of completed tasks. However, the stress level does not contribute to the server load because the network access will not change much. The LAN bandwidth factor was also considered. This is the bandwidth of the wired local area network and does not have an impact on any of the outputs considered. This result expected because the bottleneck is the wireless network. 3) Source code analysis: The factorial design was implemented by a third party and in the process the code was thoroughly examined. B. Validation If verification is the process of solving equations correctly, validation refers to the process of making sure that the correct equations, which are represented by the simulation model, are solved [21]. Validation is typically performed by comparing historical time-series data with the data obtained from simulating similar scenarios. A living lab was constructed at a naval establishment to validate the network and crew mobility models and the environment also has been tested in limited operational experiments with the Navy. However, this data and other historical data for different scenarios onboard ships cannot be publicly released. Although it has not yet been possible to validate the entire model, it will be possible to validate parts of the model such as the: • agent movement model and time required to reach locations; • time required to complete simple tasks; and, • network access delays etc. The results of these simple validation experiments are currently being scaled to measure the correctness of the developed


15

TABLE IV T YPICAL DAILY SCENARIO WITH 9 AGENTS , 15 REPAIRS AND 18 SCHEDULED EVENTS , R EPAIR Agent 1

Midnight

2 3

1:00

2:00

3:00

4:00

’

‘

‘ ‘

’

‘

’

‘

5:00

6:00

’

7:00

‘

‘

8:00

’

‘ ’

, P REVENTIVE M AINTENANCE 9:00

‘

10:00

, WATCH 11:00

’

’ ’

‘

’

‘

4

’

‘

5

’ ‘

6

’

‘

’

‘

’

7 8 9 12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

1 2 3 4

‘

5

‘

6

‘

’

‘

’

‘

’ ’

’

‘

’

‘

’

7

‘

8

‘

9

’

‘

‘

’

‘

’

‘

’

‘

’

‘

’

‘

’ ’

’

‘

’

‘

’

TABLE V S ELECTED RESULTS FROM LEVEL IV FRACTIONAL FACTORIAL DESIGN EXPERIMENT

Crew Utilization

Machine Availability

Total Task Completed

Average Server Load

p-value

AP3 Operational

0.0001

0.8317

0.0161

0.0255

AP4 Operational

0.1294

0.4199

0.3077

0.0002

AP Range

0.0000

0.9120

0.0111

0.7574

Agent Stress Level

0.0000

0.0002

0.0000

0.3523

Total Number of Crew

0.0000

0.3418

0.0034

0.0638

Intelligent Maintenance

0.0000

0.0000

0.0000

0.0000

Failure Times

0.0001

0.0000

0.0000

0.2920

Repair Assignment

0.0000

0.0000

0.0000

0.0149

LAN Bandwidth

0.7512

0.7092

0.7613

0.9882

simulation model. A lab has been constructed to validate the above mentioned aspects using non-Navy specific data, based on an established framework described in the verification, validation, and accreditation implementation handbook prepared by the Department of Navy [38]. VI. C ONCLUSION It is important to understand how new technologies on a Navy ship will impact manning requirements and mission readiness. A systems-of-systems modeling approach is required to develop this understanding. The system-of-systems modeling approach presented in this paper answers some of

the questions raised during the design phase of such systems by addressing problems in human, operational, infrastructural, and technological domains. This paper is a contribution to the field because it has successfully modeled the systemof-systems aspects of the Navy Warfighter and this type of model is currently lacking in the literature [12]. The systemof-systems modeling effort and analysis discussed here accomplished the following results: 1) Identified that intelligent maintenance technologies can positively impact manning requirements by reducing crew utilization; these technologies can also increase the efficiency of crew members by providing vital diag-


nostic information; increased machine availability can be obtained by isolating impending failures; and the dependency on supporting technologies (e.g. wireless networks) can also be identified. 2) Identified that care should be taken when choosing between different technologies. 3) Estimated manning requirements by running different scenarios. 4) Studied the effects of fires in different locations on the crew, operations, and infrastructure. 5) Used fraction factorial design to verify the model through sensitivity analysis and extreme bound testing; validation efforts are ongoing. The kind of analysis presented in the paper gives the decision makers a qualitative understanding of the impact of new technologies, specifically wireless technologies, on various aspects of a ship’s operations. This tool thus provides the Navy the ability to anticipate and manage the impacts of wireless technologies on ships capabilities and the crew especially relating to manning levels. Despite the tool presented above, various design question questions still remain unanswered. For example, consider the US Navy’s DD(X) program. To achieve all initiative in the DD(X) program, a fully instrumented destroyer will require approximately 200,000 sensors. There are numerous questions to answer before this sensor network can be designed such as: • Where should sensors be placed to monitor infrastructure and workflow to optimize manning? • How will automation and sensorization affect the ship’s response to external threats? • Should commercial of-the-shelf wireless sensors be used or will higher bandwidths be required? etc. The new research direction should thus focus on answering these questions and developing tools that would help during the design process. ACKNOWLEDGMENT The authors acknowledge Mr. David Bartlett, Program Manager at Naval Surface Warfare Center Crane for supporting this research under contract N00164-05-C-6411. The authors would also like to thank Tejas Bhatt and Chih-hui Hsieh from the Purdue Homeland Security Institute for helping in developing the architecture for communication with the outside world. R EFERENCES [1] D. A. DeLaurentis, D. N. Fry, O. V. Sindy, and S. Ayyalasomayajula, “Modeling framework and lexicon for system-of-systems problems,” in IEEE Transactions On Systems, Man, And CyberneticsPart A: Systems And Humans, May submitted for publication. [2] G. Slabodkin. (1998) Gcn. [Online]. Available: http://www.gcn.com/ print/17 17/33727-1.html# [3] Wikipedia. (2007) Uss yorktown (cv-5). [Online]. Available: http: //en.wikipedia.org/wiki/USS Yorktown (CV-5) [4] Wired. (1998) Sunk by windows nt. [Online]. Available: http: //www.wired.com/science/discoveries/news/1998/07/13987 [5] A. P. Sage and C. D. Cuppan, “On the systems engineering and management of systems of systems and federation of systems,” in Information•Knowledge•Systems Management 2, 2001, pp. 325–345.

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[6] P. G. Carlock and R. E. Fenton, “System of systems (sos) enterprise systems engineering for information-intensive organizations,” Systems Engineering, vol. 4, no. 4, pp. 242–261, October 2001. [7] R. S. Pei, “System of systems integration (sosi) - a smart way of acquiring army c4i2ws systems,” in Proceedings of the Summer Computer Simulation Conference, 2000, pp. 574–579. [8] S. J. Lukasik, “Systems, systems of systems, and the education of engineers.” Artificial Intelligence for Engineering Design, Analysis and, Manufacturing, vol. 12, no. 1, pp. 55–60, 1998. [9] V. Kotov, “Systems of systems as communicating structures,” Computer Systems Laboratory, Hewlett Packard, Tech. Rep. HPL-97-124, October 1997. [10] W. H. J. Manthorpe, Jr., “The emerging joint system of systems: A systems engineering challange and opportunity for APL,” in Johns Hopkins APL Technical Digest, vol. 17, no. 3, 1999. [11] J. Boardman and B. Sauer, “System of systems the meaning of of,” in IEEE Conference on System of Systems, April 2006. [12] C. Keating, R. Rogers, R. Unal, D. Dryer, A. Sous-Poza, R. Safford, W. Peterson, and G. Rabadi, “System of systems engineering,” Engineering Management Journal, vol. 15, no. 3, pp. 36–45, September 2003. [13] “System of systems systems engineering guide: Considerations for systems engineering in a system of systems environment,” Director, Systems and Software Engineering Deputy Under Secretary of Defense (Acquisition and Technology) Office of the Under Secretary of Defense (Acquisition, Technology and Logistics), Tech. Rep. Version 9, December 2006. [14] M. W. Maier, “Architecting principles for systems-of-systems,” Systems Engineering, vol. 1, no. 4, pp. 267–284, 1998. [15] D. S. Alberts, J. J. Garstka, and F. P. Stein, “Network centric warfare: Developing and leveraging information superiority,” Department of Defence, Tech. Rep., February 2000. [16] E. Zivi, “Integrated shipboard power and automation control challenge problem,” Power Engineering Society Summer Meeting, 2002 IEEE, vol. 1, pp. 325–330 vol.1, July 2002. [17] M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 1079–1187, 2003. [Online]. Available: citeseer.ist.psu.edu/newman03structure.html [18] R. Axtell, “Why agents? on the varied motivations for agent computing in the social sciences,” Working Paper No. 17, Center on Social and Economic Dynamics, November 2000. [19] E. Bonabeau, “Predicting the unpredictable,” May 2002. [Online]. Available: http://hbswk.hbs.edu/archive/2934.html [20] ——, “Agent-based modeling: Methods and techniques for simulating human systems,” in Proceedings of the National Academy of Sciences, vol. 99, Icosystem Corp., 545 Concord Avenue, Cambridge, MA 02138, May 2002, pp. 7280–7287. [21] D. Midgley, R. Marks, and D. Kunchamwar, “Building and assurance of agent-based models: An example and challenge to the field,” Journal of Business Research, vol. 60, no. 8, pp. 884–893, August 2007. [Online]. Available: http://ideas.repec.org/a/eee/jbrese/v60y2007i8p884-893.html [22] A. Chaturvedi, S. Mehta, D. Dolk, and R. Ayer, “Agent-based simulation for computational experimentation: Developing an artificial labour market,” European Journal of Operational Research, vol. 166, pp. 694–716, March 2004. [23] E. W. Dijkstra, “A note on two problems in connection with graphs,” Numerische Math., vol. 1, pp. 269–271, 1959. [24] Wikipedia. (2007) Dijkstra’s algorithm. [Online]. Available: http: //en.wikipedia.org/wiki/Dijkstra’s algorithm [25] ——. (2005) IEEE 802.11. [Online]. Available: http://en.wikipedia.org/ wiki/IEEE 802.11 [26] G. Bianchi, “Performance analysis of the ieee 802.11 distributed coordnation function,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 535–547, March 2000. [27] A. Catlin. (2005) Knowledge projection - smartship. [Online]. Available: http://www.cs.purdue.edu/kpxsd/smartship/ [28] A. H. C. Tsang, “Condition-based maintenance: tools and decision making,” ournal of Quality in Maintenance Engineering, vol. 1, no. 3, pp. 3–17, 1995. [29] H. Wang, “A survey of maintenance policies of deteriorating systems,” European Journal of Operational Research, vol. 139, no. 3, pp. 469 – 489, 2002. [Online]. Available: http://www.sciencedirect.com/science/ article/B6VCT-45DBB6C-1/2/dd62d731c7c0205e853d8f09d1c111ea [30] A. S. Willsky, “A survey of design methods for failure detection in dynamic systems,” Automatica, vol. 12, no. 6, pp. 601–611, Nov 1976. [31] R. Isermann, “Process fault detection based on modeling and estimation methods – a survey,” Automatica, vol. 20, no. 4, pp. 387–404, 1984.


[32] ——, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer, 2005. [33] O. Wasynczuk, E. A. Walters, and H. J. Hegner, “Simulation of a zonal distribution system for shipboard applications,” in Energy Conversion Engineering Conference, IECEC-97. Proceedings of the 32nd Intersociety, vol. 1, July 1997, pp. 268–273. [34] K. McGrattan, “Fire dynamics simulator (version 4) technical reference guide,” National Institute of Standards and Technology, Fire Research Division, Building and Fire Research Laboratory, Tech. Rep. NIST Special Publication 1018, March 2006. [35] A. R. Chaturvedi, S. A. Filatyev, J. P. Gore, A. Hanna, J. MEans, and A. K. Mellema, Integrating Fire, Strucutre and Agent Models, ser. Lecture Notes in Computer Science. Berlin / Heidelberg: Springer, 2005, vol. 3515/2005. [Online]. Available: http://www.springerlink. com/content/u0ccdfejvd7qpqtg/ [36] A. R. Chaturvedi, J. Chi, S. R. Mehta, and D. R. Dolk, “Samas: Scalable architecture for multi-resolution agent-based simulation,” in International Conference on Computational Science, 2004, pp. 779–788. [37] M. J. Ashby and W. J. Scheuren, “Intelligent maintenance advisor for turbine engines,” in Aerospace Conference Proceedings 2000 IEEE, vol. 6, 2000, pp. 211–219. [38] N. Modeling and S. M. Office, “Modeling and simulation verification, validation, and accreditation implementation handbook,” Department of Navy, Tech. Rep. Volume I VV&A Framework, March 2003.

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