scholarly journals Data-Driven Identification of Nonlinear Flame Models

2020 ◽  
Author(s):  
Abdulla Ghani ◽  
Isaac Boxx ◽  
Carrie Noren
Keyword(s):  
Limit Cycle ◽  
Laminar Flame ◽  
Network Models ◽  
Industrial Applications ◽  
Data Driven ◽  
Model Parameters ◽  
Turbulent Premixed Flame ◽  
Physical Constraints ◽  
Unstable Combustion ◽  
Combustion Processes

Abstract This paper presents a data-driven identification framework with the objective to retrieve a flame model from the nonlinear limit cycle. The motivation is to identify a flame model for configurations, which do not allow the determination of the flame dynamics: that is commonly for industrial applications where (i) optical access for non-intrusive measurements of velocity and heat release fluctuations are not feasible and (ii) unstable combustion is monitored via multiple pressure recordings. To demonstrate the usefulness of the method, we chose three test cases: (i) a classical Rijke tube; (ii) an experiment of a laminar flame (EM2C case) (iii) and a high-pressure, turbulent premixed flame (DLR case). The procedure is as follows: First, acoustic network models for the three cases are generated for which the in-house software taX is employed. Next, the acoustic network models are embedded in an optimization framework with the objective to identify flame parameters that match the experimental limit cycle data: based on the instability frequency and pressure amplitudes, we formulate physical constraints and an objective function in order to identify the flame model parameters gain nopt and time delay τopt in the nonlinear regime. We demonstrate for the three cases that the identified flame parameters reproduce the unstable combustion processes and highlight the usefulness of the method for control purposes.

scholarly journals Data-Driven Identification of Nonlinear Flame Models

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Abdulla Ghani ◽  
Isaac Boxx ◽  
Carrie Noren
Keyword(s):  
Limit Cycle ◽  
Laminar Flame ◽  
Network Models ◽  
Industrial Applications ◽  
Data Driven ◽  
Model Parameters ◽  
Turbulent Premixed Flame ◽  
Physical Constraints ◽  
Unstable Combustion ◽  
German Aerospace

Abstract This paper presents a data-driven identification framework with the objective to retrieve a flame model from the nonlinear limit cycle. The motivation is to identify a flame model for configurations, which do not allow the determination of the flame dynamics: that is commonly for industrial applications where (i) optical access for nonintrusive measurements of velocity and heat release fluctuations are not feasible and (ii) unstable combustion is monitored via multiple pressure recordings. To demonstrate the usefulness of the method, we chose three test cases: (i) a classical Rijke tube; (ii) an experiment of a laminar flame (EM2C case), (iii) and a high-pressure, turbulent premixed flame (German Aerospace Center (DLR) case). The procedure is as follows: First, acoustic network models for the three cases are generated for which the in-house software taX is employed. Next, the acoustic network models are embedded in an optimization framework with the objective to identify flame parameters that match the experimental limit cycle data: based on the instability frequency and pressure amplitudes, we formulate physical constraints and an objective function in order to identify the flame model parameters gain nopt and time delay τopt in the nonlinear regime. We demonstrate for the three cases that the identified flame parameters reproduce the unstable combustion processes and highlight the usefulness of the method for control purposes.

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

scholarly journals NetPyNE: a tool for data-driven multiscale modeling of brain circuits

2018 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

AbstractBiophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, e.g., a connectivity rule, instead of tens of loops to create millions of cell-to-cell connections. Users can then generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing using NeuroML and SONATA standardized formats. NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate different brain regions and phenomena.

scholarly journals Replicating the complexity of natural surfaces: technique validation and applications for biomimetics, ecology and evolution

2018 ◽  
Vol 377 (2138) ◽  
pp. 20180265 ◽  
Author(s):  
Charchit Kumar ◽  
Alejandro Palacios ◽  
Venkata A. Surapaneni ◽  
Georg Bold ◽  
Marc Thielen ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Industrial Applications ◽  
Model Parameters ◽  
Future Directions ◽  
Small Surface ◽  
Surface Information ◽  
Cross Covariance ◽  
Unique Method ◽  
Structural Aspects

The surfaces of animals, plants and abiotic structures are not only important for organismal survival, but they have also inspired countless biomimetic and industrial applications. Additionally, the surfaces of animals and plants exhibit an unprecedented level of diversity, and animals often move on the surface of plants. Replicating these surfaces offers a number of advantages, such as preserving a surface that is likely to degrade over time, controlling for non-structural aspects of surfaces, such as compliance and chemistry, and being able to produce large areas of a small surface. In this paper, we compare three replication techniques among a number of species of plants, a technical surface and a rock. We then use two model parameters (cross-covariance function ratio and relative topography difference) to develop a unique method for quantitatively evaluating the quality of the replication. Finally, we outline future directions that can employ highly accurate surface replications, including ecological and evolutionary studies, biomechanical experiments, industrial applications and improving haptic properties of bioinspired surfaces. The recent advances associated with surface replication and imaging technology have formed a foundation on which to incorporate surface information into biological sciences and to improve industrial and biomimetic applications. This article is part of the theme issue ‘Bioinspired materials and surfaces for green science and technology’.

scholarly journals Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 737
Author(s):  
Chaitanya Sampat ◽  
Rohit Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Data Driven ◽  
Wet Granulation ◽  
Manufacturing Processes ◽  
High Shear ◽  
Process Data ◽  
Physical Constraints ◽  
Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Methods for the Calculation of Thermoacoustic Stability Margins and Monte Carlo-Free Uncertainty Quantification

2017 ◽  
Author(s):  
Georg A. Mensah ◽  
Luca Magri ◽  
Jonas P. Moeck
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Risk Factor ◽  
Uncertainty Quantification ◽  
Frequency Domain ◽  
Gas Turbines ◽  
Network Models ◽  
System Stability ◽  
Risk Factor Analysis ◽  
Model Parameters

Thermoacoustic instabilities are a major threat for modern gas turbines. Frequency-domain based stability methods, such as network models and Helmholtz solvers, are common design tools because they are fast compared to compressible CFD computations. Frequency-domain approaches result in an eigenvalue problem, which is nonlinear with respect to the eigenvalue. Nonlinear functions of the frequency are, for example, the n–τ model, impedance boundary conditions, etc. Thus, the influence of the relevant parameters on mode stability is only given implicitly. Small changes in some model parameters, which are obtained by experiments with some uncertainty, may have a great impact on stability. The assessment of how parameter uncertainties propagate to system stability is therefore crucial for safe gas turbine operation. This question is addressed by uncertainty quantification. A common strategy for uncertainty quantification in thermoacoustics is risk factor analysis. It quantifies the uncertainty of a set of parameters in terms of the probability of a mode to become unstable. One general challenge regarding uncertainty quantification is the sheer number of uncertain parameter combinations to be quantified. For instance, uncertain parameters in an annular combustor might be the equivalence ratio, convection times, geometrical parameters, boundary impedances, flame response model parameters etc. Assessing also the influence of all possible combinations of these parameters on the risk factor is a numerically very costly task. A new and fast way to obtain algebraic parameter models in order to tackle the implicit nature of the eigenfrequency problem is using adjoint perturbation theory. Though adjoint perturbation methods were recently applied to accelerate the risk factor analysis, its potential to improve the theory has not yet been fully exploited. This paper aims to further utilize adjoint methods for the quantification of uncertainties. This analytical method avoids the usual random Monte Carlo simulations, making it particularly attractive for industrial purposes. Using network models and the open-source Helmholtz solver PyHoltz it is also discussed how to apply the method with standard modeling techniques. The theory is exemplified based on a simple ducted flame and a combustor of EM2C laboratory for which experimental validation is available.

scholarly journals Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

2020 ◽  
Author(s):  
Willem A.M. Wybo ◽  
Jakob Jordan ◽  
Benjamin Ellenberger ◽  
Ulisses M. Mengual ◽  
Thomas Nevian ◽  
...  
Keyword(s):  
Action Potentials ◽  
Network Models ◽  
Data Driven ◽  
Fast Method ◽  
Average Difference ◽  
Neuron Models ◽  
Shape Information ◽  
Proximal Dendrite ◽  
Conductance Fluctuations ◽  
Software Toolbox

AbstractDendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. We present a flexible and fast method to obtain simplified neuron models at any level of complexity. Through carefully chosen parameter fits, solvable in the least squares sense, we obtain optimal reduced compartmental models. We show that (back-propagating) action potentials, calcium-spikes and NMDA-spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping the affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input impedance between the ablated branches and the next proximal dendrite. Further, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide a software toolbox that automatizes the simplification, eliminating a common hurdle towards including dendritic computations in network models.

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