10,491 research outputs found
Stochastic Model for Power Grid Dynamics
We introduce a stochastic model that describes the quasi-static dynamics of
an electric transmission network under perturbations introduced by random load
fluctuations, random removing of system components from service, random repair
times for the failed components, and random response times to implement optimal
system corrections for removing line overloads in a damaged or stressed
transmission network. We use a linear approximation to the network flow
equations and apply linear programming techniques that optimize the dispatching
of generators and loads in order to eliminate the network overloads associated
with a damaged system. We also provide a simple model for the operator's
response to various contingency events that is not always optimal due to either
failure of the state estimation system or due to the incorrect subjective
assessment of the severity associated with these events. This further allows us
to use a game theoretic framework for casting the optimization of the
operator's response into the choice of the optimal strategy which minimizes the
operating cost. We use a simple strategy space which is the degree of tolerance
to line overloads and which is an automatic control (optimization) parameter
that can be adjusted to trade off automatic load shed without propagating
cascades versus reduced load shed and an increased risk of propagating
cascades. The tolerance parameter is chosen to describes a smooth transition
from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the
viewpoint of complex network
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
Understanding the dynamical processes that govern the performance of
functional materials is essential for the design of next generation materials
to tackle global energy and environmental challenges. Many of these processes
involve the dynamics of individual atoms or small molecules in condensed
phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten
atoms at interfaces, etc., which are difficult to understand due to the
complexity of local environments. In this work, we develop graph dynamical
networks, an unsupervised learning approach for understanding atomic scale
dynamics in arbitrary phases and environments from molecular dynamics
simulations. We show that important dynamical information can be learned for
various multi-component amorphous material systems, which is difficult to
obtain otherwise. With the large amounts of molecular dynamics data generated
everyday in nearly every aspect of materials design, this approach provides a
broadly useful, automated tool to understand atomic scale dynamics in material
systems.Comment: 25 + 7 pages, 5 + 3 figure
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks
Modern buildings encompass complex dynamics of multiple electrical,
mechanical, and control systems. One of the biggest hurdles in applying
conventional model-based optimization and control methods to building energy
management is the huge cost and effort of capturing diverse and temporally
correlated dynamics. Here we propose an alternative approach which is
model-free and data-driven. By utilizing high volume of data coming from
advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could
accurately represent the operation's temporal dynamics of building complexes.
The trained network is then directly fitted into a constrained optimization
problem with finite horizons. By reformulating the constrained optimization as
an unconstrained optimization problem, we use iterative gradient descents
method with momentum to find optimal control inputs. Simulation results
demonstrate proposed method's improved performances over model-based approach
on both building system modeling and control
Gradient Preserved And Artificial Neural Network Method For Solving Heat Conduction Equations In Double Layered Structures
Layered structures have appeared in many engineering systems such as biological tissues, micro-electronic devices, thin  lms, thermal coating, metal oxide semiconductors, and DNA origami. In particular, the multi-layered metal thin  lms, gold-coated metal mirrors for example, are often used in high-powered infrared-laser systems to avoid thermal damage at the front surface of a single layer  lm caused by the high-power laser energy. With the development of new materials, functionally graded materials are becoming of more paramount importance than materials having uniform structures. For instance, in semiconductor engineering, structures can be synthesized from di erent polymers, which result in various values of conductivity. Analyzing heat transfer in layered structure is crucial for the optimization of thermal processing of such multi-layered materials. There are many numerical methods dealing with heat conduction in layered structures such as the Immersed Interface Method, the Matched Interface Method, and the Boundary Method. However, development of higher-order accurate stable nite di erence schemes using three grid points across the interface between layers for variable coe cient case is mathematically challenging. Having three grid points ensures that the nite di erence scheme leads to a tridiagonal matrix that can be solved easily using the Thomas Algorithm. But extension of such methods to higher dimensions is very tedious. Recently there have been some solution to such complex systems with the use of neural networks, that can be easily extended to higher dimensions. For the above purposes, in this dissertation, we  rst develop a gradient preserved method for solving heat conduction equations with variable coe cients in double layers. To this end, higher-order compact  nite di erence schemes based on three grid points are developed. The  rst-order spatial derivative is preserved across the interface. Unconditional stability and convergence with O(  2 + h4) are analyzed using the discrete energy method, where   and h are the time step and grid size, respectively. Numerical error and convergence rates are tested in an example. We then present an arti cial neural network (ANN) method for solving the parabolic two-step heat conduction equations in double-layered thin  lms exposed to ultrashort-pulsed lasers. Convergence of the ANN solution to the analytical solution is theoretically analyzed using the energy method. Finally, both developed methods are applied for predicting electron and lattice temperature of a solid thin  lm padding on a chromium  lm exposed to the ultrashort-pulsed lasers. Compared with the existing results, both methods provide accurate solutions that are promising
An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model
Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types
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