A Nested-LES Approach for Computation of High-Reynolds Number, Equilibrium and Non-Equilibrium Turbulent Wall-Bounded Flows.

Computation of high Reynolds number, complex, non-equilibrium wall-bounded turbulent flows presents a major challenge for large-eddy simulation (LES), due to the stringent resolution requirements in the near-wall region in conventional LES, and the inability of existing wall models to accurately capture the near-wall dynamics in flows involving complex physics in the near-wall region. In this study, a novel nested-LES approach for computation of high Reynolds number, equilibrium and non-equilibrium, wall-bounded turbulent flows is proposed. The method couples well-resolved LES in a minimal flow unit with coarse-resolution LES in the full domain to provide high-fidelity simulations of the flow physics in both the inner and outer layers. The coupling between the two domains of nested-LES is achieved by dynamically renormalizing the velocity fields in each domain at each time-step during the course of the simulation to match the wall-normal profiles of the single-time ensemble-averaged kinetic energies of the components of mean and fluctuating velocities in both domains to those of the minimal flow unit in the inner layer, and to those of the full domain in the outer layer. The proposed nested-LES approach can be applied to any flows with at least one direction of local or global homogeneity, while reducing the required number of grid points from O(Re_t^2) of conventional LES to O(log{Re_t}) and O(Re_t^1) in flows with two or one directions of homogeneity, respectively. The proposed nested-LES approach has been applied to LES of equilibrium turbulent channel flow at Re_t ~= 1000 - 10000, and non-equilibrium, strained turbulent channel flow at Re_t ~=2000. In application to equilibrium turbulent channel flow, the nested-LES approach predicts the skin-friction coefficient, first-order turbulence statistics, higher-order moments, two-point correlations, correlation maps, and structural features of the flow in agreement with available direct numerical simulation (DNS) and experimental data. In application to non-equilibrium, strained turbulent channel flow, nested-LES predicts the evolution of skin-friction coefficients and one-point turbulence statistics in good agreement with experimental data in shear-driven, three-dimensional turbulent boundary-layer (TBL).PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120800/1/yifeng_1.pd

Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage

Interactive dynamic influence diagrams (I-DIDs) provide an explicit way of modeling how a subject agent solves decision making problems in the presence of other agents in a common setting. To optimize its decisions, the subject agent needs to predict the other agents' behavior, that is generally obtained by solving their candidate models. This becomes extremely difficult since the model space may be rather large, and grows when the other agents act and observe over the time. A recent proposal for solving I-DIDs lies in a concept of value equivalence (VE) that shows potential advances on significantly reducing the model space. In this paper, we establish a principled framework to implement the VE techniques and propose an approximate method to compute VE of candidate models. The development offers ample opportunity of exploiting VE to further improve the scalability of I-DID solutions. We theoretically analyze properties of the approximate techniques and show empirical results in multiple problem domains

Characterization of transient groundwater flow through a high arch dam foundation during reservoir impounding

AbstractEven though a large number of large-scale arch dams with height larger than 200 m have been built in the world, the transient groundwater flow behaviors and the seepage control effects in the dam foundations under difficult geological conditions are rarely reported. This paper presents a case study on the transient groundwater flow behaviors in the rock foundation of Jinping I double-curvature arch dam, the world's highest dam of this type to date that has been completed. Taking into account the geological settings at the site, an inverse modeling technique utilizing the time series measurements of both hydraulic head and discharge was adopted to back-calculate the permeability of the foundation rocks, which effectively improves the uniqueness and reliability of the inverse modeling results. The transient seepage flow in the dam foundation during the reservoir impounding was then modeled with a parabolic variational inequality (PVI) method. The distribution of pore water pressure, the amount of leakage, and the performance of the seepage control system in the dam foundation during the entire impounding process were finally illustrated with the numerical results

Team Composition in PES2018 using Submodular Function Optimization

With the development of computer game technologies, gameplay becomes very realistic in many sports games, therefore providing appealing play experience to game players. To get the victory in a football pitch, the team composition is pretty important. There is little research on the automatic team composition in sports games particularly in a popular game of Pro Evolution Soccer (PES). In this paper, we consider the team composition as one team player recommendation problem since a team is composed of several players in a game. Subsequently, we aim to recommend a list of sufficiently good football players to game players. We convert the team player recommendation into one optimization problem and resort to the greedy algorithm-based solutions. We propose a coverage function that quantifies the degree of soccer skills to be covered by the selected players. In addition, we prove the submodularity of the coverage function and improve a greedy algorithm to solve the function optimization problem. We demonstrate the performance of our techniques in PES2018.</p

Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches

Learning a Planning Domain Model from Natural Language Process Manuals

Artificial intelligence planning techniques have been widely used in many applications. A big challenge is to automate a planning model, especially for planning applications based on natural language (NL) input. This requires the analysis and understanding of NL text and a general learning technique does not exist in real-world applications. In this article, we investigate an intelligent planning technique for natural disaster management, e.g. typhoon contingency plan generation, through natural language process manuals. A planning model is to optimise management operations when a disaster occurs in a short time. Instead of manually building the planning model, we aim to automate the planning model generation by extracting disaster management-related content through NL processing (NLP) techniques. The learning input comes from the published documents that describe the operational process of preventing potential loss in the typhoon management. We adopt a classical planning model, namely planning domain definition language (PDDL), in the typhoon contingency plan generation. We propose a novel framework of FPTCP, which stands for a Framework of Planning Typhoon Contingency Plan , for learning a domain model of PDDL from NL text. We adapt NLP techniques to construct a ternary template of sentences of NL inputs from which actions and their objects are extracted to build a domain model. We also develop a comprehensive suite of user interaction components and facilitate the involvement of users in order to improve the learned domain models. The user interaction is to remove semantic duplicates of NL objects such that the users can select model-generated actions and predicates to better fit the PDDL domain model. We detail the implementation steps of the proposed FPTCP and evaluate its performance on real-world typhoon datasets. In addition, we compare FPTCP with two state-of-the-art approaches in applications of narrative generation, and discuss its capability and limitations

Group sparse optimization for learning predictive state representations

Predictive state representations (PSRs) are a commonly used approach for agents to summarize the information from history generated during their interaction with a dynamical environment and the agents may use PSRs to predict the future observation. Existing works have shown the benefits of PSRs for modelling partially observable dynamical systems. One of the key issues in PSRs is to discover a set of tests for representing states, which is called core tests. However, there is no very efficient technique to find the core tests for a large and complex problem in practice. In this paper, we formulate the discovering of the set of core tests as an optimization problem and exploit a group sparsity of the decision-making matrix to solve the problem. Then the PSR parameters can be obtained simultaneously. Hence, the model of the underlying system can be built immediately. The new learning approach doesn’t require the specification of the number of core tests. Furthermore, the embedded optimization method for solving the considered group Lasso problem, called alternating direction method of multipliers (ADMM), can achieve a global convergence. We conduct experiments on three problem domains including one extremely large problem domain and show promising performances of the new approach