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Pursuit-Evasion Games in Dynamic Flow Fields via Reachability Set Analysis

Sun, W., P. Tsiotras, T. Lolla, D. N. Subramani, and P. F. J. Lermusiaux, 2017. Pursuit-Evasion Games in Dynamic Flow Fields via Reachability Set Analysis. 2017 American Control Conference. In press.

In this paper, we adopt a reachability-based approach to deal with the pursuit-evasion differential game between two players in the presence of dynamic environmental disturbances (e.g., winds, sea currents). We give conditions for the game to be terminated in terms of reachable set inclusions. Level set equations are defined and solved to generate the reachable sets of the pursuer and the evader. The corresponding time-optimal trajectories and optimal strategies can be retrieved immediately afterwards. We validate our method by applying it to a pursuit-evasion game in a simple flow field, for which an analytical solution is available.We then implement the proposed scheme to a problem with a more realistic flow field.

Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation

Evangelinos, C., P.F.J. Lermusiaux, J. Xu, P.J. Haley, and C.N. Hill, 2009. Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation. Conference on High Performance Networking and Computing, Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers (Portland, OR, 16 November 2009), 10pp. doi.acm.org/10.1145/1646468.1646482.

Error Subspace Statistical Estimation (ESSE), an uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble-generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation and their uncertainties. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for the: (i) perturbation of the initial mean state, (ii) subsequent ensemble of stochastic PE model runs, (iii) continuous generation of the covariance matrix, (iv) successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (v) data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of short-in-duration acoustics runs.

The development and demonstration of an advanced fisheries management information system

Robinson, A.R., B.J. Rothschild, W.G. Leslie, J.J. Bisagni, M.F. Borges, W.S. Brown, D. Cai, P. Fortier, A. Gangopadhyay, P.J. Haley, Jr., H.S. Kim, L. Lanerolle, P.F.J. Lermusiaux, C.J. Lozano, M.G. Miller, G. Strout and M.A. Sundermeyer, 2001. The development and demonstration of an advanced fisheries management information system. Proc. of the 17th Conference on Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, Albuquerque, New Mexico. American Meteorological Society, 186-190.

Fishery management regulates size and species-specific fishing mortality to optimize biological production from the fish populations and economic production from the fishery. Fishery management is similar to management in industries and in natural resources where the goals of management are intended to optimize outputs relative to inputs. However, the management of fish populations is among the most difficult. The difficulties arise because (a) the dynamics of the natural production system are extremely complicated; involving an infinitude of variables and interacting natural systems and (b) the size-and species-specific fishing mortality (i.e. system control) is difficult to measure, calibrate, and deploy. Despite the difficulties, it is believed that significant advances can be made by employing a fishery management system that involves knowing the short-term (daily to weekly) variability in the structures of environmental and fish fields. We need new information systems that bring together existing critical technologies and thereby place fishery management in a total-systems feedback-control context. Such a system would monitor the state of the structure of all stocks simultaneously in near real-time, be adaptive to the evolving fishery and consider the effects of the environment and economics. To do this the system would need to (a) employ new in situ and remote sensors in innovative ways, (b) develop new data streams to support the development of new information, (c) employ modern modeling, information and knowledge-base technology to process the diverse information and (d) generate management advice and fishing strategies that would optimize the production of fish.

The Advanced Fisheries Management Information System (AFMIS), built through a collaboration of Harvard University and the Center for Marine Science and Technology at the University of Massachusetts at Dartmouth, is intended to apply state-of-the-art multidisciplinary and computational capabilities to operational fisheries management. The system development concept is aimed toward: 1) utilizing information on the “state” of ocean physics, biology, and chemistry; the assessment of spatially-resolved fish-stock population dynamics and the temporal-spatial deployment of fishing effort to be used in fishing and in the operational management of fish stocks; and, 2) forecasting and understanding physical and biological conditions leading to recruitment variability. Systems components are being developed in the context of using the Harvard Ocean Prediction System to support or otherwise interact with the: 1) synthesis and analysis of very large data sets; 2) building of a multidisciplinary multiscale model (coupled ocean physics/N-P-Z/fish dynamics/management models) appropriate for the northwest Atlantic shelf, particularly Georges Bank and Massachusetts Bay; 3) the application and development of data assimilation techniques; and, 4) with an emphasis on the incorporation of remotely sensed data into the data stream.

AFMIS is designed to model a large region of the northwest Atlantic (NWA) as the deep ocean influences the slope and shelves. Several smaller domains, including the Gulf of Maine (GOM) and Georges Bank (GB) are nested within this larger domain (Figure 1). This provides a capability to zoom into these domains with higher resolution while maintaining the essential physics which are coupled to the larger domain. AFMIS will be maintained by the assimilation of a variety of real time data. Specifically this includes sea surface temperature (SST), color (SSC), and height (SSH) obtained from several space-based remote sensors (AVHRR, SeaWiFS and Topex/Poseidon). The assimilation of the variety of real-time remotely sensed data supported by in situ data will allow nowcasting and forecasting over significant periods of time.

A real-time demonstration of concept (RTDOC) nowcasting and forecasting exercise to demonstrate important aspects of the AFMIS concept by producing real time coupled forecasts of physical fields, biological and chemical fields, and fish abundance fields took place in March-May 2000. The RTDOC was designed to verify the physics, to validate the biology and chemistry but only to demonstrate the concept of forecasting the fish fields, since the fish dynamical models are at a very early stage of development. In addition, it demonstrated the integrated system concept and the implication for future coupling of a management model. This note reports on the RTDOC.