Energy Concern in Biomolecular Simulations Involving Transitions From Coarse to Finer Grain Models

2009 ◽  
Author(s):  
Kurt S. Anderson ◽  
Mohammad Poursina
Keyword(s):  
Optimization Methods ◽  
Scale Model ◽  
Order Model ◽  
Timely Manner ◽  
Number Of Solutions ◽  
Biomolecular Systems ◽  
Reduced Order ◽  
Molecular Systems ◽  
Biomolecular Simulations ◽  
Coarse Model

Generating self-adjusting multiscale models is necessary to analyze the complex behavior of biomolecular systems in an accurate, yet timely manner. The model transitions are achieved by effectively imposing or releasing certain systems constraints from a fine scale model to a reduced order model or vice versa. In the process of model reduction of such molecular systems, naturally existing higher modes are frozen out in the modeling because the internal metric had previously indicated these modes of motion as less relevant. In the transition from a coarse model back to a finer one, the appropriate amount of energy must be put back to the system. Herein, the non-uniqueness or even the presence of infinite number of solutions in this transition is addressed. Optimization and non-optimization methods are proposed to arrive at the finite number of solutions.

Optimization Problem in Biomolecular Simulations With DCA-Based Modeling of Transition From a Coarse to a Fine Fidelity

2009 ◽  
Author(s):  
Kurt S. Anderson ◽  
Mohammad Poursina
Keyword(s):  
Optimization Problem ◽  
Optimization Procedure ◽  
Scale Model ◽  
Biomolecular Systems ◽  
Reduced Order ◽  
Knowledge Based ◽  
Biomolecular Simulations ◽  
Coarse Model ◽  
Traditional Approaches ◽  
Coordinate Partitioning

In multiscale modeling of highly complex biomolecular systems, it is desirable to switch the system model either to coarser, or higher fidelity models to achieve the appropriate accuracy and speed. These transitions are achieved by effectively imposing (or releasing) certain systems constraints from a fine scale model to a reduced order model (or vice versa). The transition from a coarse model to a fine one may not result in a unique solution. Therefore, a knowledge-based or physics-based optimization procedure may be used to arrive at the finite number of solutions. In this paper, it is shown that traditional approaches to address and solve the optimization problem such as Lagrange multipliers or changing the constrained optimization problem to an unconstrained one based on coordinate partitioning or basic linear algebra methods are computationally expensive for biomolecular systems. It is demonstrated that using a DCA based approach in modeling the transition can reduce dramatically the computational expense associated with the manipulations performed as part of optimization as well as the ones performed to derive the dynamics of the transition.

Model Order Reduction of Transmission Line Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Transmission Line ◽  
Large Scale ◽  
Original System ◽  
Reduced Order Model ◽  
Benchmark Problems ◽  
Transmission Line Model ◽  
Order Model ◽  
Line Model ◽  
Reduced Order ◽  
Numerical Effort

Transmission Line model are an important role in the electrical power supply. Modeling of such system remains a challenge for simulations are necessary for designing and controlling modern power systems.In order to analyze the numerical approach for a benchmark collection Comprehensive of some needful real-world examples, which can be utilized to evaluate and compare mathematical approaches for model reduction. The approach is based on retaining the dominant modes of the system and truncation comparatively the less significant once.as the reduced order model has been derived from retaining the dominate modes of the large-scale stable system, the reduction preserves the stability. The strong demerit of the many MOR methods is that, the steady state values of the reduced order model does not match with the higher order systems. This drawback has been try to eliminated through the Different MOR method using sssMOR tools. This makes it possible for a new assessment of the error system Offered that the Observability Gramian of the original system has as soon as been thought about, an H∞ and H2 error bound can be calculated with minimal numerical effort for any minimized model attributable to The reduced order model (ROM) of a large-scale dynamical system is essential to effortlessness the study of the system utilizing approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which consist of benchmark problems. The time response of approximated system, assessed by the proposed method, is also shown which is excellent matching of the response of original system when compared to the response of other existing approaches .

Theoretical and practical identifiability of a reduced order model in an activated sludge process doing nitrification and denitrification

1998 ◽  
Vol 37 (12) ◽  
pp. 309-316 ◽  
Author(s):  
S. Julien ◽  
J. P. Babary ◽  
P. Lessard
Keyword(s):  
Activated Sludge ◽  
Reduced Order Model ◽  
Actual Behavior ◽  
Activated Sludge Process ◽  
Order Model ◽  
Identifiability Analysis ◽  
Reduced Order ◽  
3 Dimensional ◽  
Nitrification And Denitrification ◽  
Line Oxygen

This paper deals with the structural identifiability and the identification of the parameters of a reduced order model used for control of a single reactor activated sludge process doing nitrification and denitrification. This reduced order model is splitted into two submodels, one 3-dimensional state submodel in aerobic conditions and one 2-dimensional state submodel in anoxic conditions. The identifiability analysis is based on on-line oxygen and nitrate concentrations data. It has been shown that the reduced order model is structurally identifiable. The parameter identification has been carried out by using the simplex method of Nelder and Mead. Simulation results performed over a range of six hours (two aerobic/anoxic cycles), show that there exists a good fit between the simulated solution and the actual behavior of a lab scale pilot plant.

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