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DARPA in recent years has focused heavily on the need to disaggregate complex military systems and to evolve a portfolio of “system-of-systems” architectures to better manage national security applications and improve the survivability and mission success of military platforms. A core remaining challenge, however, has been the lack of sophisticated tools to model and systematically design complex systems of systems. DARPA’s Complex Adaptive System Composition And Design Environment (CASCADE) program is addressing this shortcoming by developing novel mathematical foundations that can provide a unified view of system behavior and, ultimately, a formal language and tool kit for complex adaptive-system composition and design.
Complex interconnected systems are increasingly becoming part of everyday life in both military and civilian environments. Complex adaptive systems are those systems which have the additional important property of being adaptive—i.e., the structure and behavior of the system changes over time in a way which tends to increase its success.
In civilian settings such as urban “smart cities”, critical infrastructure systems—water, power, transportation, communications and cyber—are similarly integrated within complex networks. Exponential growth of Internet and integration of Cyber into military operations and rise of cyber warfare has also turned cyber domain as the complex adaptive system.
Military also has to deal with complex adaptive systems. The Army’s new operating concept includes dispersed operations for anti-access environments. The Marine Corps is experimenting with distributed operations across the littorals. The Naval Postgraduate School is researching aerial swarm combat. DARPA’s System of Systems Integration Technology and Experimentation program aims to disaggregate aircraft capabilities into a swarm of cooperative, low cost expendable air vehicles to operate in this A2/AD environment.
Adaptive search and rescue capability for downed airmen or forward operators in hostile environments, with options including novel autonomous and/or manned land, sea or air assets with coordination using land, sea, air, space and cyber systems. Dynamic systems such as these promise capabilities that are greater than the mere sum of their parts, as well as enhanced resilience when challenged by adversaries or natural disasters.
To overcome this challenge, DARPA has announced the Complex Adaptive System Composition and Design Environment (CASCADE) program. CASCADE aims to fundamentally change how we design systems for real-time resilient response within dynamic, unexpected environments,” said John Paschkewitz, DARPA program manager.
The goal of CASCADE is to provide both a unified view of system behavior, allowing understanding and exploitation of these complex interactions, and a formal language for complex adaptive system composition and design. This unified view of system behavior, enabled by appropriate mathematical foundations, will also enable adaptation to unanticipated environments using arbitrary system components by providing a framework to dynamically identify and correct deficient system capabilities.
Under another program, The Defense Advanced Research Projects Agency has developed mathematical tools and methods to help designers understand risks associated with the design and modeling of large military systems such as aerospace vehicles and engines.Researchers in DARPA’s Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) program are developing theoretical foundations to simplify design processes for unconventional defense systems, where the number of parameters, or system features, can be in the thousands. Teams have developed uncertainty quantification tools to increase chances that new military systems will perform as designed, DARPA said.
Existing modeling and design tools Inadequate
As an example, the typical design approach for a military SoS architecture is to use a federated modeling infrastructure that couples campaign- or mission-level models via requirements to physics-based platform models, with the resulting behaviors evaluated using engagement models. To manage complexity, interfaces and interactions are controlled as much as possible with modularity being a preferred outcome.
While these federated models work for traditional monolithic platforms, they fail to capture the networked, coordinated effects that cross layers of abstraction characteristic of SoS architectures. These networked effects are therefore unanticipated and are described as emergent behaviors. These models also do not include logistics or sustainment constraints explicitly in the design space, leading to challenges in addressing attrition and overall systems cost. System adaptability is limited to scenarios contemplated in the campaign- or mission-level models and is manifested as “playbooks” of behaviors in response to known scenarios. System resilience is weak because of the optimization to known threats and the limited number of reconfiguration options imposed by modularity and interfacial control.
“Existing modeling and design tools invoke static ‘playbook’ concepts that don’t adequately represent the complexity of, say, an airborne system of systems with its constantly changing variables, such as enemy jamming, bad weather, or loss of one or more aircraft,” said DARPA Program Manager John Paschkew.
DARPA’s Complex Adaptive System Composition and Design Environment (CASCADE) program
The goal of CASCADE is to advance and exploit novel mathematical techniques able to provide a deeper understanding of system component interactions and a unified view of system behaviors. The program also aims to develop a formal language for composing and designing complex adaptive systems.
CASCADE could also help the Department of Defense fulfill its role of providing humanitarian assistance in response to a devastating earthquake, hurricane or other catastrophe, by developing comprehensive response models that account for the many components and interactions inherent in such missions, whether in urban or austere environs.
As another example, this program could inform the design of future forward-deployed military surgical capabilities by making sure the functions, structures, behaviors and constraints of the medical system—such as surgeons, helicopters, communication networks, transportation, time, and blood supply—are accurately modeled and understood.”
“CASCADE could help develop models that would provide civil authorities, first responders and assisting military commanders with the sequence and timing of critical actions they need to take for saving lives and restoring critical infrastructure,” said DARPA Program Manager John Paschkewitz. “In the stress following a major disaster, models that could do that would be invaluable.”
In addition to improving warfighting systems, CASCADE’s research could help improve activities such as forward-deployed medical care, by ensuring that the components involving “surgeons, helicopters, communication networks, transportation, time, and blood supply … are accurately modeled and understood,” Paschkewitz said.
Models, Dynamics and Learning (MoDyL)
Complex, nonlinear, multiscale dynamical systems are ubiquitous. Examples include weather, fluids, materials, biological systems, communication networks, and social systems. These systems often evolve to a critical state built up from a series of irreversible and unexpected events, which severely limits development and implementation of mathematical models to accurately predict formation and evolution of patterns in such systems.
The Models, Dynamics and Learning (MoDyL) aims to build rigorous data-driven models for non-equilibrium dynamics to address this challenge, leveraging existing data to enable robust prediction in complex systems. Collaboration among researchers from disciplines such as dynamical system theory, computational topology, statistics, spectral analysis, as well as domain experts in the various application problems is critical to address such a complex challenge. MoDyL will bring disparate researchers together to develop fundamental mathematics and computational algorithms for extracting models from dynamic data sets.
DARPA’s Enabling Quantification of Uncertainty in Physical Systems program
Computational models and simulations can be enormously helpful when designing complex military systems such as new aerospace vehicles and engines, reducing development costs and times. However, realistic, high-fidelity models require enormous amounts of computing power in order to be able to accommodate all of the different factors that may affect predictive accuracy. To mitigate this computational cost, researchers often use simplified models, but these models contain assumptions, ambiguities, incomplete information, and inputs that vary unpredictably. This problem is exacerbated when these uncertainties interact with each other in a complex system. As a result, engineers typically rely on extensive testing to validate their modeling results—a repetitive process of design, test, verify, re-design, re-test, re-verify that can add years to the development process and significantly increase cost.
DARPA’s Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) program has recently made a number of seminal advances addressing this problem by developing mathematical tools and methods to tackle the challenges associated with large systems of many variables and account for the uncertainty in every step of the modeling and design process.
The program is advancing the field of uncertainty quantification, or UQ, which focuses on methods to estimate how accurate a prediction may be. With advanced UQ tools, designers can better understand the risks involved in pursuing certain designs. With that information in hand, the chances increase that new designs for complex military vehicles, vessels, air- and spacecraft will perform as anticipated when a prototype is first built and tested.
Many times, when systems are being developed, models will prove inadequate because data may be missing or information may be incomplete about conditions the system may have to operate in. These uncertainties make it difficult to predict how a system will respond or how much confidence the designer can reasonably have in the design. While advanced computational tools and improved testbeds such as special wind tunnels or sea-simulation facilities have enabled engineers to develop and test designs for existing systems, newer designs looking at completely different regimes cannot be physically modelled. Hypersonic airspeeds in excess of 13,000 miles per hour or sea vessels able to slice through water at 120 knots, for example, have many uncertain parameters and therefore, need UQ methods to help produce better designs.
“With EQUiPS we’re drastically changing the way we model and simulate physical-world, engineered systems,” said Fariba Fahroo, DARPA program manager. “We aim to make UQ a tractable part of simulation and modeling even for the most complex of design problems. And the mathematical tools we’re developing should apply widely in areas ranging from new aerospace structures to leading-edge integrated circuits.”
Begun in 2015, the EQUiPS program has completed its first phase and is showing success by accounting for uncertainties that would otherwise interfere with performance predictions for newly envisioned high-speed marine vessels and exhaust nozzles for advanced supersonic aircraft engines.
One group of EQUiPS research teams, led by Brown University, is developing theoretical foundations for what they call Design Under Uncertainty (DUU). DUU aims to simplify design processes for unconventional defense systems where the number of parameters, or system features, can be in the thousands and the design requires taking into account such variables as uncertain operating conditions, novel materials whose behavior may not be fully understood, and manufacturing imperfections whose relevance has yet to be determined. The team has been designing an unconventional hydrofoil surface sea vessel that in foilborne mode would achieve speeds of more than 120 knots in calm sea states, and 60 knots in extreme sea states – a performance unmatched by any such vessel today. With no historical data for such a watercraft or its hydrofoil design, engineers needed a model that could account for the overwhelming number of uncertainties. Through the framework developed under the program, the team was able to look at the entire design process, utilize different models, and deliver not only the best design candidate, but also all other possible designs complete with predicted outcomes.
“One of the critical aspects of designing this kind of vessel is the complex physics associated with super-cavitating hydrofoils—structural components that enable an optimum lift-over-drag performance ratio,” Fahroo said. “Few experimental data are available for even simple super-cavitating foils, and resolving turbulent multi-phase flow around 3-D complex structures like these is a computational grand challenge. EQUiPS-based research has led to the development of new concepts of multi-fidelity simulation and risk-based optimization that have reduced the simulation and optimization costs by orders of magnitude.”
The Massachusetts Institute of Technology, Virginia Tech, University of California, Santa Cruz, and the Naval Postgraduate School are working with Brown on the hydrofoil design effort.
Another team of EQUiPS researchers, at Stanford University is using EQUiPS methods in a study that seeks to optimize the design for a supersonic jet engine exhaust nozzle to ensure maximum thrust efficiency. Current advanced nozzle design practices are unable to take into account the large number of variables involved, including operating conditions and nozzle characteristics, along with the uncertainties associated with these variables. Focusing on a model similar to an actual aircraft engine, the researchers used new mathematical modeling tools to reduce the number of parameters to a manageable subset. Specifically, applying EQUiPS methods to the aero-thermal-structural modeling of the jet nozzle, researchers were able to reduce the number of nozzle shape parameters, such as size and material thickness, from 28 different features down to seven, turning an intractable design problem into one that is relatively solvable and potentially reducing the design cycle time.
“We see the world in three dimensions, but imagine how complicated everything would appear if it were composed of 28 dimensions—and how much simpler life would be if we could get those 28 dimensions down to seven,” Fahroo said. “That’s what a huge difference EQUiPS approaches can make.”
The Colorado School of Mines, the University of Michigan, and Sandia National Laboratories are working with Stanford on the supersonic nozzle effort.
“Ultimately, we want higher levels of confidence in the performance of a particular design,” Fahroo said. We want to be able to increase the fidelity of the models and account for the uncertainty of the operating environment, but at a lower computational cost and turnaround time. The EQUiPS research in these various areas is enabling highly effective, robust and reliable design of systems by creating technologies to manage risk and uncertainty during all steps of the design process.”
CASCADE program technical areas
Performance in the CASCADE program will occur in two technical areas (TAs):
1. TA1: Mathematical foundations for unified abstraction, composition and adaptive behavior
The goal of abstraction is describing system structures, behaviors, constraints and events in a way that exposes only the information required to define the resulting function. New frameworks are required to synthesize functions from structures and behaviors, subject to constraints, in response to events. To achieve adaptation, the capability to sense, reason and act on the design
composition space is also required.
2. TA2: Domain applications: design knowledge, problems and data in the areas of military SoS or resilient urban infrastructure
TA2 performers must outline an integration strategy for inclusion of TA1 frameworks into a modeling and design environment
The CASCADE program seeks expertise in the following areas:
• Applied mathematics, especially in category theory, algebraic geometry and topology, and sheaf theory
• Operations research, control theory and planning, especially in stochastic and non-linear control
• Modeling and applications responsive to challenges in battlefield medicine logistics and platforms, adaptive logistics, reliability, and maintenance
• Search and rescue platforms and modeling
• Adaptive and resilient urban infrastructure
References and Resources also include:
https://www.darpa.mil/news-events/2017-05-17
http://www.darpa.mil/program/models-dynamics-and-learning
