Combustion Modeling Software Development, Verification and Validation

2018 ◽  
Author(s):  
Alejandro M. Briones ◽  
Robert Olding ◽  
Joshua P. Sykes ◽  
Brent A. Rankin ◽  
Kyle McDevitt ◽  
...  
Keyword(s):  
Species Composition ◽  
Mixture Fraction ◽  
Jet Fuel ◽  
Dimensional Manifold ◽  
Combustion Modeling ◽  
Progress Variable ◽  
Combustion Theory ◽  
Tabulated Chemistry ◽  
Low Dimensional ◽  
Low Dimensional Manifold

Historically, combustion modeling is important for many transportation- and ground-based applications. More recently, modeling has been offered as an early screening tool in the evaluation of a potential alternative aviation jet fuel. This combustion evaluation path would in theory be conducted by gas turbine Original Equipment Manufacturers (OEMs) on proprietary geometries and conditions. Ideally, OEMs would have access to the latest combustion theory models and would thus have the highest predictive confidence in their model predictions. Unfortunately, the latest combustion theory codes are not written for commercial purposes. This work identifies and develops a conduit for OEM usage of latest flamelet theory for use in the evaluation of alternative jet fuel combustion properties. A so-called “common format routine” (CFR) software with two low-dimensional manifold combustion models that can be used for laminar and turbulent applications is developed, which can be implemented by OEMs on proprietary hardware. The two models are the flamelet prolongation of the intrinsic low-dimensional manifold (FPI), used for premixed combustion, and the flamelet progress variable (FPV), utilized for nonpremixed combustion. The three branches of combustion are computed using a hybrid tool that combines homotopic flamelet calculations with scaling laws and the two- and one-point flamelet continuation methods in order to resolve bifurcations. The mixture fraction and progress variable definitions can be chosen to be any summation of atomic and species composition, respectively. Diffusivity coefficients can be computed using unity Lewis number, mixture-averaged and multicomponent species composition. The turbulence-chemistry interaction is tabulated a priori using Beta probability density function (PDF) for the mixture fraction and Beta or Dirac-delta PDF for the progress variable. Parallel computing is necessary for industrial quality tabulation. The tabulated table can be used for k-ε and k-ω RANS, SAS, DES, and LES simulations. The software can also interact with liquid spray and exchange mass between the liquid and gaseous phase. The software is verified against previous numerical simulations of canonical triple flames, piloted flames and single-cup combustor. The numerical results are validated against experimental measurements of temperature and species mass fractions. The CFR software advances Cantera 2.3. Hence, the software contains an inner layer of C++ code, an intermediate layer of Python wrappers, and an upper layer (GUI) of C# code. The pre-tabulated chemistry is used for CFD simulations. The tables are bi-linearly interpolated for laminar simulations and tri-linearly interpolated for turbulent simulations. The tabulated chemistry can be hooked to commercial software such as Fluent through C and Scheme codes. The simulated flames presented here were computed with this software. The developed software is reliable for modeling and simulation of complex combustion phenomena.

scholarly journals Low-dimensional manifold of actin polymerization dynamics

2017 ◽  
Vol 19 (12) ◽  
pp. 125012 ◽  
Author(s):  
Carlos Floyd ◽  
Christopher Jarzynski ◽  
Garegin Papoian
Keyword(s):  
Actin Polymerization ◽  
Dimensional Manifold ◽  
Low Dimensional ◽  
Low Dimensional Manifold

scholarly journals Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

2020 ◽  
Author(s):  
Wei Guo ◽  
Jie J. Zhang ◽  
Jonathan P. Newman ◽  
Matthew A. Wilson
Keyword(s):  
Hippocampal Neurons ◽  
Dimensional Manifold ◽  
Latent Learning ◽  
Neural Ensembles ◽  
Correlated Activity ◽  
Place Fields ◽  
Internal States ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
The Brain

AbstractLatent learning allows the brain the transform experiences into cognitive maps, a form of implicit memory, without reinforced training. Its mechanism is unclear. We tracked the internal states of the hippocampal neural ensembles and discovered that during latent learning of a spatial map, the state space evolved into a low-dimensional manifold that topologically resembled the physical environment. This process requires repeated experiences and sleep in-between. Further investigations revealed that a subset of hippocampal neurons, instead of rapidly forming place fields in a novel environment, remained weakly tuned but gradually developed correlated activity with other neurons. These ‘weakly spatial’ neurons bond activity of neurons with stronger spatial tuning, linking discrete place fields into a map that supports flexible navigation.

Large-eddy simulation of direct injection natural gas combustion in a heavy-duty truck engine using modified conditional moment closure model with low-dimensional manifold method

2018 ◽  
Vol 21 (5) ◽  
pp. 824-837 ◽  
Author(s):  
Jian Huang ◽  
Gordon McTaggart-Cowan ◽  
Sandeep Munshi
Keyword(s):  
Natural Gas ◽  
Direct Injection ◽  
Moment Closure ◽  
Dimensional Manifold ◽  
Conditional Moment ◽  
Gas Combustion ◽  
Conditional Moment Closure ◽  
Natural Gas Combustion ◽  
Low Dimensional ◽  
Low Dimensional Manifold

This article describes the application of a modified first-order conditional moment closure model used in conjunction with the trajectory-generated low-dimensional manifold method in large-eddy simulation of pilot ignited high-pressure direct injection natural gas combustion in a heavy-duty diesel engine. The article starts with a review of the intrinsic low-dimensional manifold method for reducing detailed chemistry and various formulations for the construction of such manifolds. It is followed by a brief review of the conditional moment closure method for modelling the interaction between turbulence and combustion chemistry. The high computational cost associated with the direct implementation of the basic conditional moment closure model was discussed. The article then describes the formulation of a modified approach to solve the conditional moment closure equation, whose reaction source terms for the conditional mass fractions for species were obtained by projecting the turbulent perturbation onto the reaction manifold. The main model assumptions were explained and the resulting limitations were discussed. A numerical experiment was conducted to examine the validity the model assumptions. The model was then implemented in a combustion computational fluid dynamics solver developed on an open-source computational fluid dynamics platform. Non-reactive jet simulations were first conducted and the results were compared to the experimental measurement from a high-pressure visualization chamber to verify that the jet penetration under engine relevant conditions was correctly predicted. The model was then used to simulate natural gas combustion in a heavy-duty diesel engine equipped with a high-pressure direct injection system. The simulation results were compared with the experimental measurement from a research engine to verify the accuracy of the model for both the combustion rate and engine-out emissions.

W-LDMM: A Wasserstein driven low-dimensional manifold model for noisy image restoration

2020 ◽  
Vol 371 ◽  
pp. 108-123 ◽  
Author(s):  
Ruiqiang He ◽  
Xiangchu Feng ◽  
Weiwei Wang ◽  
Xiaolong Zhu ◽  
Chunyu Yang
Keyword(s):  
Image Restoration ◽  
Dimensional Manifold ◽  
Noisy Image ◽  
Low Dimensional ◽  
Low Dimensional Manifold

scholarly journals Exploring Chemical Reaction Space With Reaction Difference Fingerprints and Parametric t-SNE

2021 ◽  
Author(s):  
Mikhail Andronov ◽  
Maxim Fedorov ◽  
Sergey Sosnin
Keyword(s):  
Neural Network ◽  
Chemical Reaction ◽  
Deep Neural Network ◽  
Visual Representations ◽  
Dimensional Manifold ◽  
Reaction Space ◽  
Large Databases ◽  
Global Reaction ◽  
Low Dimensional ◽  
Low Dimensional Manifold

<div>Humans prefer visual representations for the analysis of large databases. In this work, we suggest a method for the visualization of the chemical reaction space. Our technique uses the t-SNE approach that is parameterized by a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints can provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape, been projected onto a 2D plane, corresponds well with already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions on a global reaction space. We validated the feasibility of this approach for two marketed drugs: darunavir and oseltamivir. We believe that our method can help explore reaction space and inspire chemists to find new reactions and synthetic ways. </div><div><br></div>

scholarly journals Can grid cell ensembles represent multiple spaces?

2019 ◽  
Author(s):  
Davide Spalla ◽  
Alexis Dubreuil ◽  
Sophie Rosay ◽  
Remi Monasson ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Grid Cell ◽  
Place Cells ◽  
Dimensional Manifold ◽  
Grid Cells ◽  
Rodent Brain ◽  
Low Dimensional ◽  
Universal Grid ◽  
Low Dimensional Manifold ◽  
Spatial Maps

The way grid cells represent space in the rodent brain has been a striking discovery, with theoret-ical implications still unclear. Differently from hippocampal place cells, which are known to encode multiple, environment-dependent spatial maps, grid cells have been widely believed to encode space through a single low dimensional manifold, in which coactivity relations between different neurons are preserved when the environment is changed. Does it have to be so? Here, we compute - using two alternative mathematical models - the storage capacity of a population of grid-like units, em-bedded in a continuous attractor neural network, for multiple spatial maps. We show that distinct representations of multiple environments can coexist, as existing models for grid cells have the po-tential to express several sets of hexagonal grid patterns, challenging the view of a universal grid map. This suggests that a population of grid cells can encode multiple non-congruent metric rela-tionships, a feature that could in principle allow a grid-like code to represent environments with a variety of different geometries and possibly conceptual and cognitive spaces, which may be expected to entail such context-dependent metric relationships.

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