8,003 research outputs found
Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials
Integrated Computational Materials Engineering (ICME) calls for the
integration of computational tools into the materials and parts development
cycle, while the Materials Genome Initiative (MGI) calls for the acceleration
of the materials development cycle through the combination of experiments,
simulation, and data. As they stand, both ICME and MGI do not prescribe how to
achieve the necessary tool integration or how to efficiently exploit the
computational tools, in combination with experiments, to accelerate the
development of new materials and materials systems. This paper addresses the
first issue by putting forward a framework for the fusion of information that
exploits correlations among sources/models and between the sources and `ground
truth'. The second issue is addressed through a multi-information source
optimization framework that identifies, given current knowledge, the next best
information source to query and where in the input space to query it via a
novel value-gradient policy. The querying decision takes into account the
ability to learn correlations between information sources, the resource cost of
querying an information source, and what a query is expected to provide in
terms of improvement over the current state. The framework is demonstrated on
the optimization of a dual-phase steel to maximize its strength-normalized
strain hardening rate. The ground truth is represented by a
microstructure-based finite element model while three low fidelity information
sources---i.e. reduced order models---based on different homogenization
assumptions---isostrain, isostress and isowork---are used to efficiently and
optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table
Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement
A placement problem for multiple Battery Energy Storage System (BESS) units
is formulated towards power system transient voltage stability enhancement in
this paper. The problem is solved by the Cross-Entropy (CE) optimization
method. A simulation-based approach is adopted to incorporate higher-order
dynamics and nonlinearities of generators and loads. The objective is to
maximize the voltage stability index, which is set up based on certain
grid-codes. Formulations of the optimization problem are then discussed.
Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on
the New England 39-Bus system. Results indicate that installing BESS units at
the optimized location can alleviate transient voltage instability issue
compared with the original system with no BESS. The CE placement algorithm is
also compared with the classic PSO (Particle Swarm Optimization) method, and
its superiority is demonstrated in terms of fewer iterations for convergence
with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at
Atlanta, GA in August 201
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