18,982 research outputs found
Oscillation energy based sensitivity analysis and control for multi-mode oscillation systems
This paper describes a novel approach to analyze and control systems with
multi-mode oscillation problems. Traditional single dominant mode analysis
fails to provide effective control actions when several modes have similar low
damping ratios. This work addresses this problem by considering all modes in
the formulation of the system kinetic oscillation energy. The integral of
energy over time defines the total action as a measure of dynamic performance,
and its sensitivity allows comparing the performance of different
actuators/locations in the system to select the most effective one to damp the
oscillation energy. Time domain simulations in the IEEE 9-bus system and IEEE
39-bus system verify the findings obtained by the oscillation energy based
analysis. Applications of the proposed method in control and system planning
are discussed.Comment: Conference paper, IEEE PESGM 201
Thermodynamics of climate change: generalized sensitivities
Using a recent theoretical approach, we study how global warming impacts the thermodynamics of the climate system by performing experiments with a simplified yet Earth-like climate model. The intensity of the Lorenz energy cycle, the Carnot efficiency, the material entropy production, and the degree of irreversibility of the system change monotonically with the CO2 concentration. Moreover, these quantities feature an approximately linear behaviour with respect to the logarithm of the CO2 concentration in a relatively wide range. These generalized sensitivities suggest that the climate becomes less efficient, more irreversible, and features higher entropy production as it becomes warmer, with changes in the latent heat fluxes playing a predominant role. These results may be of help for explaining recent findings obtained with state of the art climate models regarding how increases in CO2 concentration impact the vertical stratification of the tropical and extratropical atmosphere and the position of the storm tracks
Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments
In many chemical and biological applications, systems of differential
equations containing unknown parameters are used to explain empirical
observations and experimental data. The DEs are typically nonlinear and
difficult to analyze, requiring numerical methods to approximate the solutions.
Compounding this difficulty are the unknown parameters in the DE system, which
must be given specific numerical values in order for simulations to be run.
Estrogen receptor protein dimerization is used as an example to demonstrate
model construction, reduction, simulation, and parameter estimation.
Mathematical, computational, and statistical methods are applied to empirical
data to deduce kinetic parameter estimates and guide decisions regarding future
experiments and modeling. The process demonstrated serves as a pedagogical
example of quantitative methods being used to extract parameter values from
biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie
Analysis of high load dampers
High load damping requirements for modern jet engines are discussed. The design of damping systems which could satisfy these requirements is also discusseed. In order to evaluate high load damping requirements, engines in three major classes were studied; large transport engines, small general aviation engines, and military engines. Four damper concepts applicable to these engines were evaluated; multi-ring, cartridge, curved beam, and viscous/friction. The most promising damper concept was selected for each engine and performance was assessed relative to conventional dampers and in light of projected damping requirements for advanced jet engines
Universally Sloppy Parameter Sensitivities in Systems Biology
Quantitative computational models play an increasingly important role in
modern biology. Such models typically involve many free parameters, and
assigning their values is often a substantial obstacle to model development.
Directly measuring \emph{in vivo} biochemical parameters is difficult, and
collectively fitting them to other data often yields large parameter
uncertainties. Nevertheless, in earlier work we showed in a
growth-factor-signaling model that collective fitting could yield
well-constrained predictions, even when it left individual parameters very
poorly constrained. We also showed that the model had a `sloppy' spectrum of
parameter sensitivities, with eigenvalues roughly evenly distributed over many
decades. Here we use a collection of models from the literature to test whether
such sloppy spectra are common in systems biology. Strikingly, we find that
every model we examine has a sloppy spectrum of sensitivities. We also test
several consequences of this sloppiness for building predictive models. In
particular, sloppiness suggests that collective fits to even large amounts of
ideal time-series data will often leave many parameters poorly constrained.
Tests over our model collection are consistent with this suggestion. This
difficulty with collective fits may seem to argue for direct parameter
measurements, but sloppiness also implies that such measurements must be
formidably precise and complete to usefully constrain many model predictions.
We confirm this implication in our signaling model. Our results suggest that
sloppy sensitivity spectra are universal in systems biology models. The
prevalence of sloppiness highlights the power of collective fits and suggests
that modelers should focus on predictions rather than on parameters.Comment: Submitted to PLoS Computational Biology. Supplementary Information
available in "Other Formats" bundle. Discussion slightly revised to add
historical contex
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