Modeling and Simulation of an Electric Warship Integrated Engineering Plant
for Battle Damage Response
Aaron M. Cramer
Department of Electrical and
Computer Engineering
University of Kentucky
cramer@engr.uky.edu
Edwin L. Zivi
Weapons and Systems
Engineering Department
United States Naval Academy
zivi@usna.edu
Keywords: Complex systems, integrated engineering
plants, survivability, electric ships
Abstract
Novel continuity-of-service metrics are applied to
perform proof-of-concept simulation-based design of a
complex, dynamically interdependent electro-thermal-fluidspatial-control system subjected to hostile disruptions. The
power system models are based on experimentally validated
reduced-scale and reduced-complexity testbed models
which are representative of U.S. Navy Next Generation
Integrated Power Systems. In collaboration with an industry
partner, representative thermal and spatial models were
incorporated into the layered simulation. This time-domain
simulation was used to quantify performance to a specific
disruption in terms of a weighted aggregate continuity of
service to vital loads. By extension, system vulnerability
was quantified using a population of likely threats.
Optimization-based early design space exploration was
shown to dramatically decrease the notional ship integrated
engineering plant vulnerability by improving the
performance of a worst-case casualty by a factor of 6. These
achievements establish the metrics, methods, and tools to
perform quantitative optimization-based early design space
exploration for complex, dynamically interdependent
systems such as an electric warship.
1.
INTRODUCTION
As the transition to electric warships evolves, the need
to predict and understand the behavior of shipboard
integrated engineering plants (IEPs) becomes primary. The
IEP of an electric warship is the infrastructure that provides
propulsion as well as vital services including electric power
and thermal management to mission-critical loads [1].
Moreover, as engineering casualty control and damage
control responsibilities shift from crew to automation, the
IEP must continue to function under disruptive battle
conditions. The IEP is a complex dynamically
interdependent system that is capable of exhibiting
unforeseen behavior [2].
A particular source of this unforeseen behavior is the
dynamic interdependence of subsystems that have
Scott D. Sudhoff
School of Electrical and
Computer Engineering
Purdue University
sudhoff@purdue.edu
traditionally been designed and analyzed independently.
Moreover, many traditional design procedures impose large
design margins to compensate for uncertainty and neglected
system dynamics. For example, there is a mutual
dependence between the thermal management system and
the electric power system of an electric warship. The power
generation, conversion, and distribution equipment requires
cooling fluid to maintain safe operating temperatures.
Simultaneously, the thermal management system requires
electric power in order to operate the pumps required to
move cooling fluid through the system. This
interdependence cannot be adequately analyzed via static
analysis. While the subsystems are dependent on each other,
there is a temporal aspect to this relationship. If the cooling
system fails, the power system may be able to continue to
operate while temperatures rise. Since the thermal time
scales are generally slower, the temperatures of power
system components will rise over time until the component
shuts down or fails. This type of dynamic interaction is best
analyzed through time-domain simulation of the shipboard
IEP.
A particular situation in which the dynamic
interdependence of the IEP subsystems must be understood
is in battle damage scenarios. Increasing interdependence
creates the possibility of cascading failures in which the
effects of damage inflicted by a hostile adversary are not
limited to the equipment that is directly damaged. These
cascading failures give rise to specific vulnerabilities by
which the enemy could dramatically impact the ability of
the ship to carry out its mission.
Herein, a time-domain simulation model of a notional
electric warship IEP is described. This model encompasses
the electro-thermal-fluid-spatial-control aspects of the IEP
and its dynamic performance following hostile disruptions.
This model enables worst-case and average continuity-ofservice metrics to be incorporated into conventional early
design space exploration.
The remainder of this paper is organized as follows.
First, the simulation problem, including a description of the
notional IEP, is presented. Then, a layered simulation
approach that is used to implement the simulation model is
described. Next, simulation model results are described and
Figure 1. Electric system overview
Figure 2. Seawater network overview
specialized algorithms are used to explore the design space.
Finally, future research directions are discussed.
2.
SIMULATION PROBLEM
To investigate new computational approaches for
simulation-based design of electric warship IEPs, a notional
electric warship IEP is set forth. This system is based on the
land-based, reduced-scale Naval Combat Survivability
(NCS) testbed at Purdue University [3] and is described in
[4]. In order to employ experimentally validated power
system models, the notational IEP is composed of the
reduced scale, reduced complexity NCS testbed models. As
in the NCS testbed, the notional IEP considered herein has
two ac power networks and a zonal dc power distribution
network with three zones. As shown in Figure 1, one of the
ac networks is located forward, and the other aft. In this
figure, each ac network is supplied with power from a prime
mover (PM) driving the corresponding synchronous
machine (SM) and establishing the ac electrical frequency.
A brushless exciter/voltage regulator (BE/VR) controls the
output voltage of each SM. Each SM is connected to an ac
bus that feeds two loads: a propulsion drive (PD), which
supplies the propulsion motor, and a power supply (PS),
which delivers power to the zonal distribution network. The
PSs from each of the ac networks supply a primary dc bus
(PDCB) power on each side of the ship. A converter module
(CM) is connected to each PDCB in each zone. Opposite
CMs are connected to a zonal dc bus (ZDCB), which
supplies power to the inverter module (IM) in each zone.
The IM provides ac power to the ship service loads located
in that zone.
Shipboard electrical components are cooled by the
seawater network depicted in Figure 2. In this figure,
numbers enclosed in squares indicate seawater nodes
(SWNs), numbers enclosed in circles indicate seawater
valves (SWVs), and unenclosed numbers indicate seawater
branches (SWBs). In each zone of the ship, a seawater pump
(SWP) provides pressure to the network. Component heat
exchangers (CHXs) are used to cool larger loads
(particularly SMs and PDs) directly from the seawater
network. Freshwater loop systems (FWLSs) are used to cool
the smaller loads. As shown in Figure 3, each FWLS
contains a fluid heat exchanger (FHX) that transfers heat to
the seawater network.
Figure 3. Freshwater loop system overview
The baseline control scheme used herein is called
anarchist because it only relies on local supervisory
controllers to determine when to operate each device. There
is no communication between devices. Each device is
operated when the local measurements suggest that
conditions are viable to operate. The overall layout of the
reduced scale and reduced complexity notional ship is
shown in Figure 4. Supervisory control algorithms and
intercommunication models are reserved for future work.
The goal of the simulation model presented herein is to
assess the effects of hostile disruptions on the performance
of the IEP. In particular, the weapons effects are considered
and are represented by a spherical region of disruption. Any
component that intersects with this sphere is rendered
inoperable at the moment of the event. Likewise, any
electrical line that intersects experiences a bolted ground
fault, and any pipe that intersects ruptures.
3.
LAYERED SIMULATION APPROACH
To manage the complexity of this multidisciplinary
system, a layered simulation approach is implemented. This
approach is depicted in Figure 5. The various layers
encompass different aspects of the system’s overall
behavior. The spatial layer describes the geographic location
of each of the components of the IEP. This description and
information about a potential weapon event are used to
determine which components would survive that event. The
automation layer contains models of the supervisory
controllers that govern the operation of IEP components.
This layer combines information from other layers to
determine when a given device can operate. This includes
checking whether the device has survived the weapon event.
The ac layer contains models of the two ac networks in the
system and interacts extensively with the dc layer that
models the zonal distribution network. The seawater layer
models the pressure and flow of seawater through the
seawater network, while the thermal layer models the heat
flow through heat exchangers and freshwater cooling loops.
Figure 5. Layered simulation approach
Figure 4. Layout of integrated engineering plant
The simulation itself is implemented in the Advanced
Continuous Simulation Language (ACSL) [5]. ACSL
provides several useful advantages for this application. The
first is a textual model description language including macro
capabilities which facilitates the parameterization of models
which span the trade space. The second advantage is the
ease with which parallel ordinary differential equation
solvers can be implemented. This allows for a cosimulation
approach in which subsystems with large time scale
separation or that interact infrequently can be simulated
separately. This increases the speed at which the overall
system can be studies can be performed. In this case, the
thermal and seawater systems are simulated using 0.5-s time
steps, and the electrical systems are simulated using a
variable time step algorithm with steps as small as 0.1 ns but
typically 0.1 ms.
Configuration, interconnection, and parametric data
describing the of the IEP system components is maintained
in Excel. An ACSL representation of the system can be
automatically generated from the data contained in the
spreadsheet using Visual Basic for Applications macros.
This process is shown in Figure 6.
Figure 6. Model automation
This simulation approach can be expanded to
encompass additional layers that model more complex
aspects of system behavior. It also helps to manage the
complexity of the simulation by restricting interaction
between the layers to well-defined interfaces. Finally, it
allows for cosimulation of various subsystems, resulting in
reduced simulation time.
The response of the system starting at 800 s (100 s
before the explosion) is shown in Figure 8. Therein, v bus is
the line-to-line rms voltage of the two ac buses. The dotted
and solid traces correspond to the forward and aft ac buses.
The variable i out , sm is the rms output current of the SMs.
Again, the dotted and solid traces correspond to the forward
(SM 1) and aft (SM 2) generators.
The power supply output voltages and currents are
designated v out , ps and i out , ps . In each case, the dotted trace
is the forward power supply (PS 1) and the solid trace the
aft power supply (PS 2). The output current ( i out , cm ) of the
Zone 2 conversion modules is shown next (CM 2—dotted;
CM 5—solid). The forward most (IM 1—dotted) and aft
most (IM 3—solid) inverter module input voltages are
labeled v in , im .
Seawater flow rates for SWP 1 in Zone 1 (dotted) and
SWP 3 in Zone 3 (solid) are labeled q P . The cold plate
temperature of the heat exchangers cooling the generators is
designated T hx , chx
(CHX 1—dotted; CHX 4—solid).
Freshwater flow rates
T hx , fwl
w cf
and cold plate temperatures
of the FWLs cooling IMs 1 and 3 are also shown
(FWL 4—dotted; FWL 11—solid). The scenario that
unfolds as a result of the weapons impact is illustrated in
Figure 8.
4.
BASIC SIMULATION RESULTS
To examine the IEP model, the following scenario is
considered. The system has been running for 15 minutes
under full load, at which time the IEP has reached steadystate thermal operation. The propulsion drives (PD 1 and
PD 2) are each consuming 37 kW and each inverter module
(IM 1, IM 2, and IM 3) is providing 5 kW to ship-service
loads. Then, a weapon with an explosion radius of 2.00 m
detonates at the point (100.00,−4.18,3.38) m as shown in
Figure 7. This particular explosion damages pipe SWB 20 in
the seawater cooling network.
Figure 7. Example event
Cascading Damage Scenario
1. Plots begin at t = 800 s when the system has reached
thermal steady state.
2. At t = 900 s a weapon with an explosion radius of
2.00 m detonates at the point (100.00,−4.18,3.38) m
as indicated in Figure 7. This particular explosion
damages pipe SWB 20 in the seawater cooling
network.
3. Sea water valves SWV 3, 4, 7, 8, and 14 close
isolating this fault Zone 3 from the rest of the
seawater network. SWP 3 flow rate increases due to
the unsecured rupture. The remainder of the seawater
network settles to a new equilibrium point.
4. In Zone 3, the loss of seawater cooling causes the
temperature of both CHX 4 and FWL 11 to rise
following the weapon detonation. At approximately
1016 s (116 s after the initial event), SM 2 overheats
and is forced to shut down.
5. The SM 2 overheat shutdown causes PD 2 and PS 2
to shut down.
6. The PS 2 shutdown causes CM 4, CM 5, and CM 6 to
shut down, as evidenced by drop in the input voltage
to IMs 1 and 3. The input voltages drop due to the
increased output current of CMs 1 and 3 caused by
the loss of CMs 4 and 6. In addition, the output
currents of CM 2 and CM 5 are shared prior to this
point, but at 1016 s, the current that CM 5 is
providing has to shift to CM 2. The output currents of
both SM 1 and PS 1 increase to cover the load that
was previously shared with SM 2 and PS 2.
7. At 3363 s (2347 s after SM 2 shutdown), IM 3
overheats. This causes the pumps in FWLs 8, 9, 10,
and 11, and SWP 3 to shut down, as seen in the pump
flow rates. The shutdown of IM 3 causes the input
voltage of IM 3 to rise as the CM 3 output current
decreases. Also, the output currents of SM 1 and PS 1
drop due to the decreased load in the dc system. At
this point, the system stabilizes in its new
configuration. The following devices are shut down:
SM 2, PD 2, PS 2, CMs 4, 5, and 6, IM 3, SWP 3, and
FWLs 8, 9, 10, and 11.
AC bus rms voltages, forward is dotted
4. SM 2 over temp. shutdown
SM AC generator currents, forward is dotted
6. Partial load shift to SM 1
4. SM 2 over temp. shutdown
DC PS power supply voltages, forward is dotted
5. PS 2 cascading shutdown
DC power supply output currents, forward is dotted
5. PS 2 cascading. shutdown
Conversion module currents, CM 2 (dotted) & CM 5
6. Partial load shift to SM 1
5. CM 5 cascading shutdown
AC inverter input voltages, IM 1 (dotted) & IM 3
6. Droop due to increased load
Seawater flow rates, SWP 1 (dotted) & SW 3
2. SWB 20 pipe rupture, 3. Rupture isolated
Generator cold plate temperatures, forward is dotted
4. SM 2 over temp. shutdown
Fresh water flow rates, CHX 1 (dotted) + CHX 4
7. IM 3 shutdown causes pump shutdown
Inverter cold plate temperatures, IM 1 (dotted) & IM3
1. Steady-state initial conditions
Figure 8. Example event scenario
7. IM 3 over temp. shutdown
5.
RESULTS OBTAINED THROUGH SIMULATION
By defining a metric called operability, it is possible to
quantitatively assess the effects of a given event on a given
IEP design [6]. The operability metric, ranging from 0% to
100%, quantifies the (weighted) degree to which vital
engineering services are provided to mission-critical loads
following a hostile disruption. Operability is defined as
follows
tf
O ( ) 
I
*
t  i  1 w i ( t ,  ) o i ( t ) o i ( t ) dt
0
tf
(1)
I
*
t  i  1 w i ( t ,  ) o i ( t ) dt
0
use approximately 31,350 operability evaluations to arrive
at this value.
The event that causes this worst case is shown in
Figure 10. This occurs when the missile strikes (39.82,2.73,
3.83) m, hitting PS 1 and CM 4, as well as the electric line
from CM 4 to PDCB 2, the CM 4 part of ZDCB 1, SWB 1,
and FWL 2. Because PS 1 is hit, the port dc bus goes down.
Hitting the line from CM 4 to the starboard dc bus shorts the
starboard bus. Within 10 s, the entire dc system is down.
Since all of the cooling pumps require the zonal dc
distribution system to operate, after 100 s, both PDs
overheat.
where w i ( t ,  )  0 , o i (t ) , and o i* ( t ) are the weight,
operating status, and commanded operating status,
respectively, of load i at time t for event  . For the event
described above, the operability can be calculated from the
simulation results as 95.03%.
Operability can be averaged over a set of possible
events, yielding average system dependability. System
dependability is defined as
D s   O ( ) f  ( ) d 
(2)
E
where E is the set of events being considered and f  () is
the probability density function over the set of events. The
average system dependability over the set of events
uniformly distributed over the volume shown in Figure 9 is
96.38%. This calculation is performed by approximating the
integral in (2) as a sum over a grid of 31,350 points
contained in the volume shown in Figure 9. The value is
relatively close to 100%, but the event set contains many
events in which no critical equipment is damaged. Even
very small improvements in this value can reflect large
improvements in average IEP performance.
Figure 9. Set of events
It is also possible to use optimization algorithms to
locate the worst-case event. The worst-case operability
establishes the minimum system dependability:
(3)
D s , min  min O ( ) .
 E
Genetic algorithms or particle swarm optimization are used
in [6] to calculate the minimum system dependability of the
notional ship as 9.16%. Both of these optimization methods
Figure 10. Worst-case event
This simulation model can be further combined with
advanced optimization algorithms such as those in [7] to
improve the design of the IEP with respect to the two
system dependability metrics described above. In [8], the
average system dependability is improved from 96.38% to
96.42% by modifying the locations of the components in the
electrical network and adjusting the settings of the valves in
the seawater network. More importantly, the minimum
system dependability is improved from 9.16% to 60%. The
new worst-case event is shown in Figure 11. This event is a
sphere centered at (66.05,−1.65,12.04) m that disrupts IM 2.
This causes IM 2 to shut down, but nothing further occurs.
This value is absolutely optimal for the loads and load
weightings. In essence, the adversary attacks the load with
the highest priority. From the perspective of the IEP
designer, no greater performance can be obtained. The
adversary can obtain no further advantage (e.g., through
cascading failures or by interdependencies) by attacking the
IEP, so the adversary resorts to attacking the most valuable
load.
Figure 11. Improved worst-case event
Each of these optimizations requires 3,375,000
operability evaluations. These evaluations are performed on
the Genetic Optimization Processing Array (GOPA), a 216processor array located at Purdue University. Additionally,
reduced-order models are used to reduce the runtime
required for operability evaluation.
6.
CONCLUSIONS
This investigation demonstrates the capability of
performing simulation-based design using dynamically
interdependent electro-thermal-fluid-spatial-control timedomain simulation. Moreover, a proof-of-concept
demonstration of novel operability and dependability
continuity-of-service metrics is accomplished. Furthermore,
optimization-based early design space exploration is shown
to dramatically decrease the ship vulnerability by improving
the performance of a worst-case casualty by a factor of 6.
These achievements establish the metrics, methods and tools
to perform quantitative optimization based early design
space exploration for complex, dynamically interdependent
systems such as an electric warship.
7.
FUTURE DIRECTIONS
Although this investigation represents significant
progress towards understanding, quantifying, and
optimizing the behavior of dynamically interdependent
systems such as an electric warship IEP, this work
represents a new beginning rather than a final
accomplishment.
First, it is possible to incorporate more advanced
control strategies into the simulation model. The baseline
control method used herein does not represent the current
state of practice. More advanced control systems require a
communication infrastructure that is itself vulnerable to the
effects of disruption. These features can be represented
within the layered modeling approach. This will also allow
the behavior of the control systems themselves to be studied
during battle damage conditions.
Second, the electrical models need to become aligned
with the current Next Generation Integrated Power System
(NGIPS) baseline systems. This requires that the library of
average-value models needs to be improved and expanded.
Since the power system models bound the computational
load, the balance between fidelity and computational
complexity should be reconsidered.
Third, energy storage modules need to be incorporated
to quantify the effects of distributed energy storage and to
learn how to optimize the sizing and location of energy
storage modules throughout the IEP.
Finally, more advanced simulation techniques can be
applied. As an example, cosimulation improves simulation
performance by allowing slower dynamics to be simulated
separately from faster dynamics. The inherent separation of
time scales associated with this multidisciplinary system
creates this opportunity. However, techniques for event (i.e.,
zero crossing or root) detection must be employed that
provide consistent results when cosimulation is applied.
Further, in this system there is not a clear distinction
between slow and fast dynamics. While the electrical
dynamics are generally faster, the stiffly stable, variable step
solver used to simulate the electrical dynamics will take
very large time steps when the electrical transients have
settled. Sometimes, the electrical solver takes larger time
steps than the ―slower‖ fixed step thermal solver. Consistent
event detection between solvers is necessary for dependable
simulation results.
References
[1] J.P. Walks and J.F. Mearman, ―Integrated engineering
plant,‖ presented at ASNE Reconfiguration and
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[2] C.N. Calvano and P. John, ―Systems engineering in an
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Jan. 2004.
[3] S.D. Sudhoff, S. Pekarek, B. Kuhn, S. Glover, J. Sauer,
and D. Delisle, ―Naval combat survivability testbeds for
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based power and propulsion systems,‖ in Power Eng.
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[4] A.M. Cramer, R.R. Chan, S.D. Sudhoff, Y. Lee, M.R.
Surprenant, N.S. Tyler, E.L. Zivi, and R.A. Youngs,
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[5] Advanced Continuous Simulation Language (ACSL)
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Huntsville, AL, 1999.
[6] A.M. Cramer, S.D. Sudhoff, and E.L. Zivi,
―Performance metrics for electric warship integrated
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[7] A.M. Cramer, S.D. Sudhoff, and E.L. Zivi,
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[8] A.M. Cramer, S.D. Sudhoff, and E.L. Zivi, ―Metric
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Humans, to be published.
Biography
Aaron M. Cramer received the B.S. (summa cum
laude) degree in electrical engineering from the University
of Kentucky, Lexington, in 2003. He received the Ph.D.
degree from Purdue University, West Lafayette, IN, in
2007.
He is an Assistant Professor at the University of
Kentucky. From 2007 to 2010, he was a Senior Engineer
with PC Krause and Associates, West Lafayette, IN. From
2004 to 2007, he was a Graduate Research Assistant at
Purdue University. His interests include simulation
techniques, optimization methods, and power electronics.
Having performed the bulk of this investigation as his
dissertation research and with years of professional
simulation experience, Aaron is ideally positioned to
contribute to future work in this area.
Edwin L. Zivi received the B.S. degree in engineering
science and mechanics from the Virginia Polytechnic
Institute and State University, Blacksburg, in 1975. He
received the M.S. and Ph.D. degrees in mechanical
engineering from the University of Maryland, College Park,
in 1983 and 1989, respectively.
He is presently an Associate Professor of Systems
Engineering at the United States Naval Academy,
Annapolis, MD. Prior to 1998, he was a Senior Research
Engineer and Technical Advisor at the Naval Surface
Warfare Center, Annapolis, MD. His research focuses on
the integration of survivable naval automation systems with
advanced shipboard engineering and damage control
systems.
Scott D. Sudhoff received the B.S. (highest
distinction), M.S., and Ph.D. degrees in electrical
engineering from Purdue University, West Lafayette, IN, in
1988, 1989, and 1991, respectively.
Currently, he is a Professor at Purdue University. From
1991 to 1993, he served as Visiting Faculty with Purdue
University. From 1993 to 1997, he was a Faculty Member at
the University of Missouri-Rolla. He has published over 50
journal papers, including six prize papers. His interests
include electric machines, power electronics, and finiteinertia power systems, applied control, and genetic
algorithms.