Patent Grant
6945781
The present invention relates generally to the field of simulating military weapon systems. In particular, the present invention relates to a system for use in conjunction with designing complex military weapon systems, by performing sophisticated design concept analyses and by simulating operations on virtual representations of advanced naval gun systems interactively with the design work.
The development of complex military equipment traditionally has been based on a rigid, top-down approach, originating with a publication of a customer operational requirements document. A prime contractor decomposes the operational requirements document to allocate requirements across the weapon system level, which in turn are further decomposed and allocated across the subsystem and component levels. This top-down, hierarchical approach ensures that customer requirements are reflected in lower-level components and become integral to an objective weapon system design. This approach, however, does very little to optimally allocate limited resources across a weapon system design, and objective characteristics of an operational design often exceed program constraints. In addition to suboptimized designs, the top-down approach often leads to misallocated development resources and development processes that are incapable of rapidly responding to inevitable changes in operational, fiscal, and technological considerations.
Customer recognition of the above-described dilemmas, the realities of tight fiscal budgets, and changes in the geopolitical climate during the past decade have had a noticeable philosophical effect on how future weapon systems will be developed and procured. The development of future weapon systems will be cost constrained so that a weapon system's capabilities will be partially determined by a customer's ability to procure funding. In addition, most forces are no longer forward deployed, but instead are forward deployable. The ability to project force around the world, and the ability to sustain a force outside a customer's sovereign territory, has placed a tremendous burden on the logistical and tactical operations of customers. With respect to naval gun systems involved in indirect fire engagement, ships must be capable of rapidly reaching a destination or target area and engaging in a live fire exercise with limited collateral damage to a civilian population and structures nearby the targets. Moreover, sequentially fired munitions or munitions fired from multiple guns must be capable of reaching a target area as a coordinated delivery for maximum combat effectiveness.
Because of these fiscal and geopolitical changes, some customers have established a mission need and a partial list of non-negotiable, operational requirements for future weapon systems. These customers also have requested prospective weapon system developers to design, develop, and demonstrate credible simulated modeling approaches to satisfying operational weapon system requirements and to developing weapon system designs that allocate constrained resources while optimizing performance according to specified measures of effectiveness.
Previous efforts to develop software for weapon systems have focused on stand alone simulation software or software that provides analysis at the subsystem or component level only, because methods such as the above-described top-down approach were used to manage the overall design and development process. For example, R. Carnes et al., U.S. Pat. No. 4,926,362, Airbase Sortie Generation Analysis Model (ABSGAM), describes a computer simulation model for analyzing the sortie generation capabilities and support requirements of air vehicle designs and for performing effectiveness analyses on these designs. The model cannot be used to allocate resources across a system or various subsystems or components of a design nor used concurrently and interactively with design work. Another similar invention is described by R. Adams, U.S. Pat. No. 5,415,548, System and Method for Simulating Targets for Testing Missiles and Other Target Driven Devices.
It would be advantageous to have an evaluation and simulation system that functions integrally and interactively with the conceptualization, design, and development of weapon systems, and particularly advanced naval gun systems, under conditions whereby design concepts can be analyzed, constrained resources can be allocated across a weapon system architecture in a manner that optimizes the weapon system's combat effectiveness, and a virtual representation of the weapon system can be tested under simulated combat conditions for combat effectiveness. Moreover, it would be advantageous if a user of such an evaluation and simulation system could establish performance levels for operational, system, subsystem, and component requirements while optimizing the advanced naval gun system's effectiveness and satisfying the resource constraints.
An integrated evaluation and simulation system for advanced naval gun systems interactively evaluates concept design decisions and design requirements in the context of a virtual representation of an operational advanced naval gun system. The combat effectiveness of an advanced naval gun system may also be concurrently tested by virtual simulation. A computer system is programmed to implement a causal network model comprising an integrated collection of analysis models for creating a virtual representation of an advanced naval gun system. The integrated evaluation and simulation system also includes a user interface operatively connected to at least the computer system, for selectively inputting data into the causal network model and receiving information therefrom, and preferably at least one virtual simulation system. The virtual simulation system may be operatively connected to the causal network model either directly as part of the computer system or indirectly through a virtual simulation system interface.
Preferred embodiments of the present invention relate to an integrated evaluation and simulation system for advanced naval gun systems for concurrently and interactively evaluating the benefits and burdens of concept design decisions and design requirements with design work. The combat effectiveness of an naval gun system built according to a set of design parameters also can be concurrently tested by virtual simulation. Thus, the present invention enables system designers to efficiently, comprehensively, interactively, and concurrently evaluate and optimize overall naval gun system performance by manipulating basic system design inputs and parameters. The invention is easily adapted to a wide variety of analyses, including single step analysis, dependencies analysis, sensitivity and trade-off analysis, Monte Carlo analysis, and optimization analysis based on predetermined input parameters and resource constraints.
Preferred embodiments of the integrated evaluation and simulation system for advanced naval gun systems include a computer system programmed to implement a computational engine having at least one causal network model factoring at least one interrelationship among a plurality of critical, advanced naval gun system combat effectiveness functional attributes and constrained resources, and programmed to create a virtual representation of a naval gun system. The computational engine implements a modular software architecture down to the gun system's component level, so that each module can be represented by a separate subroutine. Preferred embodiments also include a user interface operatively connected to at least the computer system to selectively input data into and receive information from the computational engine, and preferably include at least one virtual simulation system operatively connected to the computational engine to simulate an naval gun system. The user interface may have a menu driven graphical user interface with a display feature for depicting a two- or three-dimensional view or picture of the virtual representation of the naval gun system. The computer system may communicate with the at least one virtual simulation system and receive information from the virtual simulation system in other ways to be described herein.
The combat effectiveness functional attributes of an advanced naval gun system include the attributes of gun composition, propellant characteristics, projectile composition, aerodynamic characteristics of the projectile, and lethality. Gun composition includes parameters related to the gun barrel such as physical characteristics, assembly, and performance. Projectile characteristics includes parameters related to the ammunition, such as nose cone, rocket motor, warhead, and control surface characteristics, general projectile characteristics, and guidance. The effects of these attributes can be observed by running a simulation on proprietary virtual environment software or software that is governmentally or commercially available.
Preferred embodiments of the integrated evaluation and simulation system are based on several performance criteria: system usability, system modularity, system speed, and system accuracy. Usability is defined as the level of accessibility to input data and output information, and the level of user friendliness of the user interface design. All input and output is accessible to a user via a graphical user interface and/or data files. A user is not encumbered with “window confusion,” i.e., having too many windows open simultaneously, as preferred embodiments allow for no more than six windows to be open concurrently.
The integrated evaluation and simulation system is easy to maintain and upgrade because of its modular software design. Preferred embodiments use a modular subroutine for each “node” within the causal network model to facilitate the maintenance, removal, and replacement of each “blackbox” for each node, as the need arises, without disrupting the balance of the system.
Computational speed is defined for each mode of operation in terms of execution on currently available UNIX Silicon Graphics workstations. In the single-run mode, which involves propagating all inputs through the causal network model and into a virtual simulator, 2 minutes or less is required. In the dependencies mode, a run time of less than 10 seconds is required. In the sensitivities mode, a run time of 2 minutes or less is required for each increment of the independent variables. In the Monte Carlo mode, a run time of 2 minutes of less is required for each random variable selection. In the optimization mode, a run time of 1 hour or less for 6 independent variables is acceptable, and a run time of 2 days or less is acceptable for global optimization. These times are established for output having a computational error that does not exceed a predetermined percentile for any single computed variable, presently ten percent, when compared to actual test data.
The present invention also includes a method of integrated evaluation and simulation for determining design parameters and allocating resources across a system architecture of an advanced naval gun system to optimize the gun system's combat effectiveness, by providing a computer system having a user interface and a computational engine having a causal network model factoring an interrelationship among a plurality of critical combat effectiveness functional attributes and constrained resources for the gun system; by providing at least one virtual simulation system; by selectively inputting data into the computational engine to create a virtual representation of an optimally effective naval gun system; by selectively running the virtual representation of the optimally effective naval gun system in the at least one virtual simulation system; and by utilizing information obtained from the simulation run to enhance the virtual representation of the optimally effective naval gun system.
The computer system alternatively can be described as having a computer-readable storage media storing at least one computer program that operates as an integrated evaluation and simulation system for determining design parameters and allocating resources across a system architecture of an advanced naval gun system to optimize the naval gun system's combat effectiveness. This implementation is accomplished by storing a computational engine having a causal network model factoring at least one interrelationship among a plurality of critical combat effectiveness functional attributes and constrained resources in the computer system; by obtaining data necessary for the program to create a virtual representation of an optimally effective naval gun system; by running the computational engine to create the virtual representation of the optimally effective naval gun system; by selectively sending the virtual representation to a virtual simulation system for simulating an operation of the naval gun system; and by receiving information from the virtual simulation system about the simulated operation of the naval gun system.
The preferred embodiment of the present invention implements an integrated evaluation and simulation computer system for assisting system designers of advanced naval gun systems to select values for correlated design parameters and performance requirements and to allocate limited resources across a system architecture of a naval gun system. By establishing design input parameters and performance requirements for operational, system, subsystem, and/or component levels, users of the integrated evaluation and simulation system can determine optimal equipment designs, as measured by naval gun systems' combat effectiveness and given resource constraints. The integrated evaluation and simulation system also is capable of concurrently and interactively modeling the performance of a naval gun system by simulating the naval gun system's combat effectiveness in a virtual simulation system. The integrated evaluation and simulation system implements a modular software architecture down to the equipment component level and can be operated by selectively using a menu driven graphical user interface.
As shown in
The control system 60 is used to control the states and modes of operation of the invention and to control the optimization process that operates upon the causal network model 40. The control system 60 is preferably at least partly based on gradient search methodology, and the optimization process may be a commercially available product. Preferably, the integrated evaluation and simulation system can run in any of five different modes: a single-run mode for propagating specified inputs once through the causal network model; a dependencies mode for identifying all parameters downstream from any input parameter; a sensitivities mode for providing a venue for performing sensitivity and trade-off analysis between any variables within the causal network model; a Monte Carlo mode for including technological and manufacturing uncertainty in an analysis; and an optimization mode for optimizing a weapon system's combat effectiveness at a local or global level, i.e., a component, subsystem, or system level. The integrated evaluation and simulation system also can perform sensitivity analyses between a weapon system's operational performance and a system, subsystem, or component input parameter; design attributes; or performance requirement. A control system algorithm 61, as illustrated in
The single-run mode (at step 62) performs a single run or iteration through the causal network model 40, producing a set of intermediate and final results. Input variables can be changed one at a time or in any combination. This mode finds a point solution for a given set of input parameters and/or requirements and displays the results requested by a user, such as interior ballistics, maximum range, lethality, minimum and maximum time of flight for a specified range, and minimum and maximum time of flight at each target altitude above sea level, and each range at each quadrant elevation. The computational process begins when a run button is activated to propagate all of the input data through the entire causal network model 40.
The dependencies mode (at step 63) rapidly and visually identifies the interrelationships between design and performance parameters within the causal network model 40. A user can select any input value and generate visual cues, for example check boxes, of all downstream parameters that would be affected by a change to this input. First, the control system 60 is initiated and the causal network model 40 is pulsed to identify the downstream parameters. Then the results are returned to the user interface 20.
The sensitivities mode (at steps 64 and 65) is designed to evaluate weapon system performance in terms of any design parameter in the causal network model 40. When this mode is selected, any input design parameter can be varied over a specified range to evaluate the effects on any performance parameter. The control system 60 performs multiple single-run passes through the causal network model 40, varying the input to analyze each variation or combination of selected input parameters according to a range and/or increment specified by the user, and the results are displayed and stored as they are generated. The results of the analysis are presented in an analysis window and can be displayed graphically. This mode allows for brute force search of a number of design parameters to determine their effect on the overall system. It is also an alternative means of optimization, by running a sensitivity analysis and inspecting the results by sight.
The Monte Carlo mode allows a user to insert technological and manufacturing uncertainty into an analysis to assess a probability of meeting specified requirements. The concept is similar to the sensitivities mode but can work with computed or derived parameters including intermediate parameters. This mode allows a user to vary parameters by specifying their means and standard deviation sigmas. The code performs a random draw on each of the selected parameters and then executes the equivalent of a single run. Statistics are collected on the parameters and the results. As shown in
The optimization mode provides for determining the best mix of design parameters that meet specified performance requirements and resource constraints while optimizing a naval gun system's combat effectiveness as measured, for example, by lethality. A user can select which design parameters will be included in an optimization. These selections are used to configure the control system 60 to optimize combat effectiveness by varying the selected design parameters and satisfying the resource constraints and performance requirements.
The causal network model 40 integrates lower level design algorithms with higher-level mission effectiveness simulation results so that system designers or analysts can modify any portion of a gun, magazine, or projectile design and assess the modification's impact on all other areas of a gun system. In addition, the causal network model can be used to evaluate a system design and for test support of specific contractor projectiles or other equipment. The causal network model 40 performs all the computations required by the user interface 20 and the control system 60, and provides a means for analyzing complex interactions and interrelationships among parameters and constraints within the naval gun system under study. As shown in
The gun data 100 has a barrel physical model for calculating the performance parameters of a gun barrel. As shown in
The propellant data 102 combines the gun chamber volume input with the designed loading density to generate a propellant mass. As shown in
The projectile data 104 contains models relating to physical properties of a projectile. The design parameters and performance requirements for nose cones, control surfaces, rocket motors, and warheads as well as for general projectile features are combined with the designed breech pressure and charge-to-mass ratio to iterate to a solution that yields physical properties of a projectile. As shown in
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Aerodynamic data 106 for a projectile takes physical data for an exterior of a projectile and its control surfaces as well as the projectile's center of gravity and uses MissileDatCom to generate aerodynamic coefficients. Alternatively, the coefficients can be read from a file, such as are contractor supplied values. As shown in
The lethality data 108 is derived from a model that includes building up a target area from a description of a layout of the target area and characteristics of targets within the area. As shown in
The user interface 20 allows a user to control all aspects of the system's behavior. The user interface has a level of user friendliness that is acceptable to engineers, analysts, and project managers. A user may selectively control the preferred embodiment either from a command line or through the graphical user interface 21. When the command line is used, a user uses a text editor to directly edit input files as needed. The user then types the appropriate command to run the causal network model 40. Control is returned to the user at the command prompt when the run is completed. A graphical user interface is developed using the Builder Xcessory (BX)™ toolkit, which generates motif GUI code in C++. When the graphical user interface 21 is used, this interface interacts with the causal network model 40 on behalf of the user. The user interface 20 is in a separate software class from the class holding the virtual simulation system 80, as this separation facilitates implementing the control system 60, especially when the control system 60 utilizes a commercially available optimizer. As with other parts of the integrated evaluation and simulation system, the graphical user interface 21 is designed to be highly modular and easily modifiable and expandable. Input and output often used within a single working session, as described above, have their own user interface panels, while input and output that is infrequently accessed, or accessed only after multiple working sessions, is accessible via data files or the database. The graphical user interface detailed design preferably takes the form of a series of panel designs that contain the detail on behavior, functionality, and parameters accessible by the respective panels.
The preferred embodiment has a virtual simulation system that includes models for system update, system performance, and system effectiveness analysis. The system performance models include models for interior ballistics, maximum range, specific minimum and maximum time of flight, and overall minimum and maximum time of flight. The interior ballistics results are generated by a barrel wear model, computing relative barrel wear using a Smith/Obrasky equation and input propellant temperature, propellant impetus, chamber pressure, and firing rate, and a muzzle velocity model, using input projectile mass, peak chamber pressure, barrel travel, chamber volume, and propellant information to generate a muzzle velocity. The muzzle velocity model is always run so that there will be a current muzzle velocity for other models. The maximum range models exercise a projectile flyout routine to find the minimum and maximum time of flight to the specified range at each quadrant elevation. A user specifies the range of quadrant elevations of interest and can specify either guided flight or assume flight only at maximum lift over drag (L/D). The specific minimum and maximum time of flight results are generated by exercising the projectile flyout routine to find the minimum and maximum time of flight to the specified range at each quadrant elevation. The overall minimum and maximum time of flight results are generated by exercising the projectile flyout routine to find the minimum and maximum time of flight at each target altitude and each range at each quadrant elevation. The system effectiveness analysis models include lethality models, which calculate fractional damage results generated by inputting the specific minimum and maximum time of flight table, either generated by the above method or making one specifically for a mission, into the lethality model. This model uses data from the lethality input parameters and a Carlton Damage equation to compute an estimated fractional damage value.
Preferably the integrated evaluation and simulation system 10 has no unique requirements of its operational environments, including hardware and software environments. The preferred embodiment of the present invention runs in a UNIX or LINUX operating environment and is accessible from any Sun or Silicon Graphics Incorporated (SGI) workstation; an SGI system is used to generate plots of analysis results. Those skilled in the art are aware that other present and future computing system platforms may be used to support the integrated evaluation and simulation system 10. The preferred embodiment is presently written in the object oriented language C++, and the computational engine accepts input from ASCII text input files and is capable of creating three-dimensional plots and numerical tables. The system utilizes two classes as shown in
The purpose of the computer system for advanced naval gun systems is to design optimal naval gun systems, as measured by the gun systems' combat effectiveness and given specified design input parameters, performance requirements, and resource constraints. When the integrated evaluation and simulation system is first called, as shown in
After the required input has been entered on the various tabs, and the compute results button has been clicked, the data is sent to the causal network by the control system where the specified mode is run using the input and information from the database. Design parameters are calculated first for the gun, propellant, and projectile. Next aerodynamic coefficients are calculated and then the time of flight of the projectile. This intermediate information is sent to the database for storage and to the virtual simulation system to calculate results using the system update, system performance, and system effectiveness models. This results information is then sent to the computational engine and to the database and can be viewed through the graphical user interface.
Computational speed is defined for each mode of operation in terms of execution on a currently available UNIX Silicon Graphics workstation. In the single-run mode, which involves propagating all inputs through the causal network model and into a virtual simulator, 2 minutes or less is required. In the dependencies mode, a run time of less than 10 seconds is required. In the sensitivities mode, a run time of 2 minutes or less is required for each increment of the independent variables. In the Monte Carlo mode, a run time of 2 minutes of less is required for each random variable selection. In the optimization mode, a run time of 1 hour or less for 6 independent variables is acceptable, and a run time of 2 days or less is acceptable for global optimization. These times are established for output having a computational error that does not exceed a predetermined percentile for any single computed variable, presently ten percent, when compared to actual test data.
Although preferred embodiments using data 100-108 have been described herein, those skilled in the art understand that some or all of the described preferred data may be used together, separately, or with additional kinds of data.
Although the preferred embodiment of the integrated evaluation and simulation system for advanced naval gun systems has been described herein, it should be recognized that numerous changes and variations can be made and that the scope of the present invention is to be defined by the claims.
This application is a continuation-in-part application of a co-pending nonprovisional application, Integrated Evaluation and Simulation System for Military Weapon Systems, Ser. No. 09/824,512, filed Apr. 2, 2001, and incorporated herein by reference.
| Number | Name | Date | Kind |
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| 5661668 | Yemini et al. | Aug 1997 | A |
| 5719797 | Sevachko | Feb 1998 | A |
| 6106298 | Pollak | Aug 2000 | A |
| 6208955 | Provan et al. | Mar 2001 | B1 |
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| Number | Date | Country | |
|---|---|---|---|
| 20020192622 A1 | Dec 2002 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 09824512 | Apr 2001 | US |
| Child | 10115148 | US |
