Manifold Model Research Articles

Ammonia-methane combustion in a swirl burner: Experimental analysis and numerical modeling with Flamelet Generated Manifold model

Ammonia/methane flames gained significant attention since this is a probable step toward a sustainable, carbon-free economy in the near future. Therefore, three such flames were numerically and experimentally investigated in a swirl burner, focusing on robust modeling methods to facilitate the spreading of ammonia combustion in the industry. Chemistry was considered by the mechanism of Okafor and employed through the Flamelet Generated Manifold model. Both Particle Image Velocimetry and OH* measurements confirmed the appropriateness of the numerical model in all cases. It was demonstrated that the modeling approaches are applicable at both near stoichiometric conditions and close to the flammability limit of ammonia. The comparison of steady-state and the mean unsteady results implied that the steady calculations are appropriate only for the chemical conversion and fall behind in flow field modeling. The Root Mean Square velocity was identical up to the reaction zone for all cases, while its value decreased with the increase of ammonia concentration. The CO emission matched underpredicted experiments by a magnitude; however, the concentration was very low. Regarding the NOx emission, the CFD underpredicted it by a factor of two.

From snapshots to manifolds – a tale of shear flows

We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and,$K$-nearest neighbours ($K$NN) algorithm as decoder. The proposed technique is applied to numerical and experimental datasets including the fluidic pinball, a swirling jet and the wake behind a couple of tandem cylinders. Analysing the fluidic pinball, the manifold is able to describe the pitchfork bifurcation and the chaotic regime with only three feature coordinates. These coordinates are linked to the vortex-shedding phases and the force coefficients. The manifold coordinates of the swirling jet are comparable to the POD mode amplitudes, yet allow for a more distinct and less noise-sensitive manifold identification. A similar observation is made for the wake of two tandem cylinders. The tandem cylinders are aligned and located at a streamwise distance which corresponds to the transition between the single bluff body and the reattachment regimes of vortex shedding. Isomap unveils these two shedding regimes while the Lissajous plot of the first two POD mode amplitudes features a single circle. The reconstruction error of the manifold model is small compared with the fluctuation level, indicating that the low embedding dimensions contain the coherent structure dynamics. The proposed Isomap–$K$NN manifold learner is expected to be of great importance in estimation, dynamic modelling and control for a large range of configurations with dominant coherent structures.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling.

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.

Investigation of the effective hydro-mechanical properties of soil-rock mixtures using the multiscale numerical manifold model

This study focuses on determination of the effective hydro-mechanical properties of a widespread geological mélange, the Soil-Rock Mixture (SRM), using the multiscale Numerical Manifold Method (NMM). Influences of the Volumetric Block Proportion (VBP), block size, block shape, block orientation angle and micro-dynamics on the effective properties are investigated thoroughly. The SRM structural models are generated within the NMM framework in a statistical manner. The effective properties are extracted using the multiscale theory where micro-dynamics is fully considered based on the extended Hill-Mandel lemma. Through numerical simulations, the VBP and block orientation angle prove to be the first and second most influential factors. A growing VBP leads to increasing deformation moduli but decreasing effective permeability and Biot’s factor. Normal components of permeability and deformation tensors are positively proportional to the corresponding normal intersecting areas formed by sample cross-sections and blocks while shear components are positively proportional to sums of the normal and shear intersecting areas. Introducing micro-dynamics and diminishing the time step make the effective permeability and deformation moduli rise while Biot’s coefficient decrease. The accuracy and stability of the multiscale NMM are validated by comparing present results with those of empirical formulae and single-scale numerical methods.

New energy vehicle engine speed control method based on vehicle networking technology

In view of the current new energy vehicles only implement torque control on the drive motor, resulting in the engine speed fluctuation, affecting the vehicle running stability and reducing the engine efficiency and energy saving effect, a new energy vehicle engine speed control method based on Internet of vehicles technology is proposed. The battery and operation data are obtained through the sensor of the new energy vehicle Internet of vehicles, and the data are controlled and transmitted by the controller area network. The overall external dynamic characteristics of the engine are analyzed by using the average value model. According to the principle of engine dynamic system, the throttle intake manifold model, combustion torque model and power output sub model are constructed respectively. The speed is controlled by double working points According to the variable parameter PID control of the engine, the load prediction is introduced to optimize the speed control, and the internal pressure value of the accumulator is obtained through the SOP value to realize the speed control of the new energy vehicle engine. The experimental results show that the engine speed control method can effectively improve the energy-saving effect and efficiency of the engine, reduce the engine speed fluctuation, and improve the vehicle running stability under the condition of meeting the driver’s torque demand.

Application of machine learning in low-order manifold representation of chemistry in turbulent flames

The Uniform Conditional State (UCS) and the Multidimensional Flamelet Manifold (MFM) models are methods for the tabulation of chemistry in simulations of turbulent flames. The high-dimensionality of the tables these models generate and many possible combinations of the values for the input variables necessitate the allocation of a considerable size of memory during CFD calculations. This issue becomes even more problematic when adding more conditioning variables to the model. In this study, two Artificial Intelligence (AI)-based approaches referred to as Decision Tree (DT) and Artificial Neural Network (ANN) are developed and tested to provide in situ chemistry representation. The goal is to predict four parameters (outputs) accurately with low memory demand and computational cost. The trained AI models are then employed for simulation of a turbulent premixed flame. Comparison of the results from the AI-based approaches to those from the conventional UCS model shows acceptable agreement. The memory and CPU requirements from the different approaches are compared. It is found that the ANN model reduces the size of the chemistry table by around 92%. Conversely, the DT-based model reduces the size of the chemistry model by only 40%. The CPU time for using the DT model during the CFD calculations was around 10% shorter than the conventional approach while it was 8% higher for the ANN model. It was concluded that, based on the particular applications, different AI-based methods can facilitate an efficient representation of the chemistry manifold.

Modeling Spray C and Spray D with FGM within the framework of RANS and LES

In this study, two different diesel-like igniting sprays are investigated: Engine Combustion Network (ECN) Spray C and D. In particular, this study focuses on the respective performances of the RANS and LES models to predict a turbulent, igniting spray using the OpenFOAM platform. The breakup model, discretization schemes, and case setups, including the combustion model, are kept constant in order to mitigate any potential effect on the simulation apart from intrinsic differences due to turbulence modeling. A classic κ-ε model is applied for the RANS approach, while a dynamic structure model is used to solve the momentum equation in the LES approach. The κ-ε model constants are tuned to obtain a suitable prediction of inert experiments. Both approaches exhibit a reasonable agreement with the inert experiments regarding the global spray characteristics, the liquid length, and the vapor penetration. However, the transient local properties, including the spatial distribution of mixture fraction variance and the species distributions, are not identical. For reacting conditions, the Flamelet Generate Manifold (FGM) model is adopted in both the LES and RANS simulations, using several enthalpy levels as the fourth dimension in the tabulation to account for local heat loss. The results show good agreement between the two turbulence models, in terms of liquid length, vapor penetration, and lift-off length, while a short ignition delay is registered for both sprays and turbulence frameworks. Turbulence–chemistry interaction (TCI) is considered by applying a presumed probability density function (β-PDF) to the mixture fraction, and is found to play a key role in the reproduction of species distribution in the domain.

A novel approach for suppressing flow maldistribution in mini-channel heat exchangers

Mini-channel heat exchangers are extensively used to achieve heat dissipation with high heat flux, while the problem of flow maldistribution seriously impairs the thermal efficiency and operational reliability of heat exchangers. In this paper, the flow distribution characteristics of mini-channel heat exchangers with the conventional manifold model are numerically studied, and the influence of flow maldistribution on heat transfer is analyzed. To suppress the problem of flow maldistribution, an innovative approach for improving fluid uniformity by the built-in spiral baffle in the inlet manifold is proposed, and the effects of spiral baffles with different pitches and radii on flow distribution are discussed. Besides, the heat transfer characteristics and pressure drop under different manifold models are also compared. Finally, the variable-pitch spiral baffle is introduced to realize the stepped regulation of flow distribution. The results show that the conventional manifold model has serious flow maldistribution and causes the deterioration of heat transfer in front-end channels, especially at high Reynolds numbers. The improved manifold model greatly improves the uniformity of the fluid and temperature distribution inside mini-channel heat exchangers on the premise of sacrificing a small amount of pressure drop. The optimal pitch and radius of the spiral baffle are 20 mm and 9 mm respectively, and the flow maldistribution in almost all channels is controlled within the range of ±15% at this case. In addition, the variable-pitch spiral baffle further improves the flow uniformity of the mini-channel heat exchanger, and shows better overall performance than the equal-pitch spiral baffle.

ELAA Channel Characterization with Parameter Estimation Based on a Generalized Array Manifold Model

The extremely large antenna arrays (ELAAs) and millimeter-wave systems have become key techniques for obtaining higher frequency spectrum efficiency in sixth-generation (6G) communication systems. It is necessary to determine appropriate statistical models to describe the channel characteristics for the ELAAs, such as spatial non-stationarity, dispersion in angular domain, and spatial consistency. Thus, a signal model based on the generalized array manifold (GAM) that describes the dispersion in direction using the definition of the slightly distributed scatterer (SDS) is proposed in this work. An estimator for the parameters of the GAM model, namely GAM Space-Alternating Generalized Expectation-maximization (GAM-SAGE), is also designed. Moreover, a method to obtain a stochastic SDS-based channel model (SBCM) that is capable of reproducing the spatial consistency is proposed. The method is then used to establish measurement-based models for line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios using a 40×40 receiver (Rx) planar antenna array at a carrier frequency of 40 GHz. The results demonstrate that the SBCM is capable of achieving spatially consistent results and outperforms the specular-path (SP) models in completely characterizing the ELAA channels at millimeter-wave bands, which are fundamental for the design of 6G.

An extended FGM model with transported PDF for LES of spray combustion

An enhanced flamelet generated manifold (FGM) model for large eddy simulation (LES) of turbulent spray combustion is presented. In the enhanced FGM model, a transported probability density function (TPDF) description of the FGM variables is employed. The TPDF is represented using the Eulerian stochastic fields (ESF) approach, and the method is applied to LES of spray combustion under conditions relevant to internal combustion engines. The new ESF/FGM method achieves an improved accuracy of predictions due to the ESF modelling of the subgrid-scale turbulence-chemistry interaction. It also achieves high computational efficiency due to the FGM tabulation of the chemical kinetic mechanism. The performance of the new ESF/FGM model is assessed by simulation of the Spray-A flames from Engine Combustion Network (ECN) and comparison of the results, firstly, with experimental measurements, and secondly, with conventional FGM model simulation results. It is shown that the ESF/FGM method is capable of predicting both global and local combustion characteristics, i.e., pressure rise, ignition delay time, flame lift-off length and the thermo-chemical structure of the spray flames with improved accuracy compared to the conventional FGM model that is based on the presumed PDF description of FGM variables. The sensitivity of the predictions using ESF/FGM to the number of stochastic fields is examined by varying the number of these fields in the range of 4–128. Furthermore, the influence of different FGM reaction progress variables on the simulations is investigated, and a new reaction progress variable based on the local consumption of oxygen is proposed. The results show that the new progress variable improves predictions of spray combustion, including the prediction of the start of injection, the quasi-steady state liftoff length, the post-injection oxidation, and the pressure evolution.

Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models

Many genetic syndromes are associated with distinctive facial features. Several computer-assisted methods have been proposed that make use of facial features for syndrome diagnosis. Training supervised classifiers, the most common approach for this purpose, requires large, comprehensive, and difficult to collect databases of syndromic facial images. In this work, we use unsupervised, normalizing flow-based manifold and density estimation models trained entirely on unaffected subjects to detect syndromic 3D faces as statistical outliers. Furthermore, we demonstrate a general, user-friendly, gradient-based interpretability mechanism that enables clinicians and patients to understand model inferences. 3D facial surface scans of 2471 unaffected subjects and 1629 syndromic subjects representing 262 different genetic syndromes were used to train and evaluate the models. The flow-based models outperformed unsupervised comparison methods, with the best model achieving an ROC-AUC of 86.3% on a challenging, age and sex diverse data set. In addition to highlighting the viability of outlier-based syndrome screening tools, our methods generalize and extend previously proposed outlier scores for 3D face-based syndrome detection, resulting in improved performance for unsupervised syndrome detection.

Linear Manifold Modeling and Graph Estimation based on Multivariate Functional Data with Different Coarseness Scales

We develop a high-dimensional graphical modeling approach for functional data where the number of functions exceeds the available sample size. This is accomplished by proposing a sparse estimator for a concentration matrix when identifying linear manifolds. As such, the procedure extends the ideas of the manifold representation for functional data to high-dimensional settings where the number of functions is larger than the sample size. By working in a penalized setting it enriches the functional data framework by estimating sparse undirected graphs that show how functional nodes connect to other functional nodes. The procedure allows multiple coarseness scales to be present in the data and proposes a simultaneous estimation of several related graphs. Its performance is illustrated using a real-life fMRI dataset and with simulated data.

Dynamical Signature: Complex Manifolds, Gauge Fields and Non-Flat Tangent Space

Theoretical possibilities of models of gravity with dynamical signature are discussed. The different scenarios of the signature change are proposed in the framework of Einstein-Cartan gravity. We consider, subsequently, the dynamical signature in the model of the complex manifold with complex coordinates and complex metrics are introduced, a complexification of the manifold and coordinates through new gauge fields, an additional gauge symmetry for the Einstein-Cartan vierbein fields, and non-flat tangent space for the metric in the Einstein-Cartan gravity. A new small parameter, which characterizes a degree of the deviation of the signature from the background one, is introduced in all models. The zero value of this parameter corresponds to the signature of an initial background metric. In turn, in the models with gauge fields present, this parameter represents a coupling constant of the gauge symmetry group. The mechanism of metric determination through induced gauge fields with defined signatures in the corresponding models is considered. The ways of the signature change through the gauge field dynamics are reviewed, and the consequences and applications of the proposed ideas are discussed as well.

Connectivity-aware Graph: A planar topology for 3D building surface reconstruction

Multi-view Stereo (MVS) meshes suffer from occlusions or missing data, making most surface reconstruction methods invalid. Due to data imperfections, existing methods, just like PolyFit, have made a trade-off between reconstruction accuracy and time consumption. We propose a novel approach that automatically reconstructs a building surface model from raw triangular mesh with data incompleteness. Unlike existing methods that extract high-quality primitives, our method focuses on assemblies of primitives to control the level of geometric detail in the reconstructed models. We design a topological relationship of the primitives to form a plane connection graph. To be precise, we first combine the scalability of shapes with plane primitives. Then strong and soft connections between primitives are constructed by calculating the confidence of the plane intersection in space. Furthermore, the topological relations of all primitives are encoded into an undirected graph. Finally, a watertight and manifold model is extracted from the faces of a candidate set by energy minimization. Experiments on the Helsinki 3D dataset demonstrate the superiority of our method in time consumption and reconstruction error, as measured by Hausdorff distance. Our method outperforms other primitive-based algorithms in handling non-planar structures. Even when dealing with imperfect data, a watertight model is still obtained.

Flood forecasting with machine learning models in an operational framework

Abstract. Google's operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the long short-term memory (LSTM) networks and the linear models. Flood inundation is computed with the thresholding and the manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the linear model, while the thresholding and manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area close to 470 000 km2, home to more than 350 000 000 people. More than 100 000 000 flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations and improving modeling capabilities and accuracy.

Manifold alignment-based multi-fidelity reduced-order modeling applied to structural analysis

This work presents the application of a recently developed parametric, non-intrusive, and multi-fidelity reduced-order modeling method on high-dimensional displacement and stress fields arising from the structural analysis of geometries that differ in the size of discretization and structural topology. The proposed approach leverages manifold alignment to fuse inconsistent field outputs from high- and low-fidelity simulations by individually projecting their solution onto a common subspace. The effectiveness of the method is demonstrated on two multi-fidelity scenarios involving the structural analysis of a benchmark wing geometry. Results show that outputs from structural simulations using incompatible grids, or related yet different topologies, are easily combined into a single predictive model, thus eliminating the need for additional pre-processing of the data. The new multi-fidelity reduced-order model achieves a relatively higher predictive accuracy at a lower computational cost when compared to a single-fidelity model.

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

Heat release and transfer of premixed propane/air flames in high g-load centrifugal force fields

Rapid combustion and propagation are crucial for energy conversion and utilization equipment, such as power plants and thrust engines. High g-load in centrifugal flow fields has been demonstrated to be able to elevate turbulent flame speed. However, more details of flame behaviors in strong centrifugal force fields still need to be investigated. This paper focuses on flame bubble behaviors and related aspects by conducting Large Eddy Simulations (LES) of high g-load centrifugal flames stabilized by a backward step in an annual combustor. A centrifugal force was produced by setting different circumference inlet velocities (0 ms/, 4 m/s, 8 m/s, 12 m/s and 16 m/s) while axial velocity was constant (12 m/s). Wall-adapting Local Eddy-viscosity (WALE) model was used as the subgrid-scale model. Flamelet Generated Manifold (FGM) model and a 31-species and 107-step propane/air reaction mechanism were used for combustion modeling. The centrifugal body force exerted on flame bubbles and cold gas parcels is found to be able to make them move oppositely in the radial direction, and thus makes the flame surface more wrinkled. This effect leads to reinforced heat exchange between hot burned gas and cold fresh mixture. The consequent results are not only that the flame propagation speed is increased and the flame zone is broadened, but also that the local heat loss is enhanced, which leads to a weaker local heat release rate and might even lead to flame extinction. Additionally, the time-averaged flame front consists of a preheating zone and a reaction zone, along with a centrifugal-force-induced heat transfer from the post-flame zone to the cold fresh mixture.

A review of computer-based methods for classification and reconstruction of 3D high-density scanned archaeological pottery

• A complete and critical analysis of the state-of-the-art until the 2021 is proposed. • It refers on methods for pottery analysis, classification, and reconstruction. • The review considers only the methods starting from a 3D discrete model. • The pros and cons of the most significant methods are critically discussed. • Some recommendations for future researches for each analysed field are provided. Ceramics analysis, classification, and reconstruction are essential to know an archaeological site's history, economy, and art. Traditional methods used by the archaeologists for their investigation are time-consuming and are neither reproducible nor repeatable. The results depend on the operator's subjectivity, specialization, personal skills, and professional experience. Consequently, only a few indicative samples with characteristic components are studied with wide uncertainties. Several automatic methods for analysing sherds have been published in the last years to overcome these limitations. To help all the involved researchers, this paper aims to provide a complete and critical analysis of the state-of-the-art until the end of 2021 of the most important published methods on pottery analysis, classification, and reconstruction from a 3D discrete manifold model. To this end, papers in English indexed by the Scopus database are selected by using the following keywords: “computer methods in archaeology”, “3D archaeology”, “3D reconstruction”, “3D puzzling”, “automatic feature recognition and reconstruction”. Additional references complete the list found through the reading of selected papers. The 125 selected papers, referring to only archaeological potteries, are divided into six groups: 3D digitalization, virtual prototyping, Fragment features processing, geometric model processing of whole-shape pottery, 3D Vessel reconstruction from its fragments, classification, and 3D information systems for archaeological pottery visualization and documentation. In the present review, the techniques considered for these issues are critically analysed to highlight their pros and cons and provide recommendations for future research.

Numerical analysis of dilute methanol spray flames in vitiated coflow using extended flamelet generated manifold model

This work focuses on the large eddy simulation and the study of turbulent dilute methanol spray flames in vitiated coflow using the secondary-oxidizer Flamelet Generated Model (FGM). The modified FGM model uses an additional secondary oxidizer parameter in addition to the three other parameters previously used for spray flames—progress variable, mixture fraction, and enthalpy. The results for gas phase and droplet properties are validated against the dilute methanol spray flame database for varying fuel injection amounts. The droplet statistics and the liftoff flame heights are accurately captured for all the cases. A proper orthogonal decomposition (POD) of the scalar fluctuating hydroxyl radical (OH) field and the velocity–temperature field captures the flame structures in the downstream region of ignition kernels. The detailed POD analysis reveals that the base frequency of the dominant OH field equals that of the dominant vortical structure of 67.3 Hz. The flame propagation happens around these dominant vortical structures because of the less-strained fluid mixing.