Manifold Parameters Research Articles

Water management and mass transport of a fractal metal foam flow-field based polymer electrolyte fuel cell using operando neutron imaging

Metal foam flow-fields (MFFs) exhibit immense potential for enhancing the performance of polymer electrolyte fuel cells (PEFCs) owing to their advantageous pore connectivity and abundant gas pathways. Nevertheless, challenges remain with the conventional MFF concerning reactant homogeneity and water management. To address these concerns, this study incorporates a fractal manifold into the MFF design. By employing operando neutron imaging, device-level testing, and electrochemical impedance spectroscopy (EIS), a comprehensive understanding of mass transfer and water management characteristics across the fractal manifold MFF is obtained. This novel design delivers better cell performance and lower mass transport resistance compared to the conventional MFF under all experimental conditions investigated. Notably, neutron imaging reveals that the fractal manifold MFF consistently exhibits a reduced liquid water content and more uniformly distributed liquid water compared to the conventional MFF. These superior characteristics of the design contribute to a substantial ∼15% increase in maximum power density compared to the conventional MFF-based PEFC. The results indicate the potential for further performance improvement by optimizing manifold parameters.

Numerical investigation of air flow characteristics for a compact 5 [formula omitted] 10 array tubular segmented-in-series solid oxide fuel cell stack

Tubular segmented-in-series solid oxide fuel cells (SIS-SOFCs) offer the advantages of high voltage, low current, easy sealing, and high thermal shock resistance. Tubular SIS-SOFCs can be assembled into a highly integrated power-generation stack module to increase the volumetric power density. This study investigated the air flow characteristics of a kilowatt-class tubular SIS-SOFC stack with a 5 × 10-cell array, each containing 59 segments. A three-dimensional stack model that considers practical stack structures, coupled electrochemical reactions, and the mass/momentum/heat transfer processes was developed. The results show that a large flow nonuniformity existed in the conventional tubular stack design, which resulted in a severe temperature distribution. Local hot spots at the marginal positions and circumferential temperature difference were found to be non-negligible. To improve the flow uniformity, an air distributor with optimized diameters at different positions was designed, and manifold parameters were adjusted to balance the flow resistance. The maximum temperature was reduced by 35 K and temperature uniformity was significantly improved.

A model-tuned predictive sliding control approach for steering angle following of full self-driving vehicles

Steer-by-wire is a key technology to realize full self-driving for intelligent vehicles, where this is a challenge for steering angle following accurately. Therefore, a hybrid control thinking is proposed to design a model-tuned predictive sliding control approach, which is utilized to realize angle following of the electric motor steer-by-wire system. Here, sliding mode control is used as a core algorithm to be fit for the dynamics characteristics of the steer-by-wire system. Model tuning control is proposed to update model parameters by a receding horizon method, which means that some posteriori knowledge of the control system is utilized to make the model more accurate. Model predictive control is used to optimize the sliding manifold parameters of sliding mode control, which means that some priori knowledge of the control system is used to make the control law much fitter. Then we discuss a series of studies on the steer-by-wire system model and the control algorithm that, collectively, develop an approach of how the hybrid control algorithm precisely makes the front wheel angles follow desired steering commands. The designed approach has been deployed into a steering control unit, and tested in a steering test vehicle to realize the angle following of the electric motor steer-by-wire system. Based on the experimental results and statistical analysis, it can be concluded that the hybrid control thinking is a good idea of how to fuse several algorithms into a control approach, and the model-tuned predictive sliding control approach is a good candidate for the steering angle following of full self-driving vehicles.

Cosmic topology. Part IIa. Eigenmodes, correlation matrices, and detectability of orientable Euclidean manifolds

If the Universe has non-trivial spatial topology, observables depend on both the parameters of the spatial manifold and the position and orientation of the observer. In infinite Euclidean space, most cosmological observables arise from the amplitudes of Fourier modes of primordial scalar curvature perturbations. Topological boundary conditions replace the full set of Fourier modes with specific linear combinations of selected Fourier modes as the eigenmodes of the scalar Laplacian. We present formulas for eigenmodes in orientable Euclidean manifolds with the topologies E 1–E 6, E 11, E 12, E 16, and E 18 that encompass the full range of manifold parameters and observer positions, generalizing previous treatments. Under the assumption that the amplitudes of primordial scalar curvature eigenmodes are independent random variables, for each topology we obtain the correlation matrices of Fourier-mode amplitudes (of scalar fields linearly related to the scalar curvature) and the correlation matrices of spherical-harmonic coefficients of such fields sampled on a sphere, such as the temperature of the cosmic microwave background (CMB). We evaluate the detectability of these correlations given the cosmic variance of the observed CMB sky. We find that topologies where the distance to our nearest clone is less than about 1.2 times the diameter of the last scattering surface of the CMB give a correlation signal that is larger than cosmic variance noise in the CMB. This implies that if cosmic topology is the explanation of large-angle anomalies in the CMB, then the distance to our nearest clone is not much larger than the diameter of the last scattering surface. We argue that the topological information is likely to be better preserved in three-dimensional data, such as will eventually be available from large-scale structure surveys.

Brief Review of Recent Achievements in the Flamelet Manifold Selection and Probability Density Distribution for Flamelet Manifold Variables

Abstract The flamelet model is a commonly used tool for turbulent combustion simulations in the engineering field due to its computational efficiency and compatibility with complex chemical reaction mechanisms. Despite being widely used for decades, the flamelet model still faces challenges when applied to complex flame configurations, such as partially premixed flames, inhomogeneous inlets, supersonic combustion, or multiphase combustion. The principal challenges are posed by the uncertainty of the presumed shapes for probability density functions (PDFs) of the flamelet tabulation variables and the coupled process of turbulent diffusion and chemical reaction in turbulent combustion. Recent progress is reviewed from the viewpoint of the reaction manifold, with connections made to other combustion models, as well as the determination of joint (or conditional) PDFs for flamelet manifold parameters (e.g., progress variable, scalar dissipation rates, etc.). Promising improvements have been outlined in computational efficiency and the accuracy of predicted variable fields in simulating complex combustion systems (such as turbulent inhomogeneous combustion, combustion with multi-regime modes, and two-phase combustion). Advances in computational resources, direct numerical simulation data, artificial intelligence, stochastic simulation methods, and other dimension-reduction combustion models will contribute to the development of more accurate and efficient flamelet-like models for engineering applications.

Determination of N-Acetyl-L-cysteine Ethyl Ester (NACET) by Sequential Injection Analysis.

New sequential injection analysis (SIA) methods with optical sensing for the determination of N-acetyl-L-cysteine ethyl ester (NACET) have been developed and optimized. NACET is a potential drug and antioxidant with advantageous pharmacokinetics. The methods involve the reduction of Cu(II) in its complexes with neocuproine (NCN), bicinchoninic acid (BCA), and bathocuproine disulfonic acid (BCS) to the corresponding chromophoric Cu(I) complexes by the analyte. The absorbance of the Cu(I) complexes with NCN, BCA, and BCS was measured at their maximum absorbance wavelengths of 458, 562, and 483 nm, respectively. The sensing manifold parameters and experimental conditions were optimized for each of the Cu(II) complexes used. Under optimal conditions, the corresponding linear calibration ranges, limits of detection, and sampling rates were 8.0 × 10-6-2.0 × 10-4 mol L-1, 5.5 × 10-6 mol L-1, and 60 h-1 for NCN; 6.0 × 10-6-1.0 × 10-4 mol L-1, 5.2 × 10-6 mol L-1, and 60 h-1 for BCA; and 4.0 × 10-6-1.0 × 10-4 mol L-1, 2.6 × 10-6 mol L-1, and 78 h-1 for BCS. The Cu(II)-BCS complex was found to be best performing in terms of sensitivity and sampling rate. Usual excipients in pharmaceutical preparations did not interfere with NACET analysis.

Thermal management of high flux electronics using film evaporation with an enhanced fluid delivery system (FEEDS)

The increasing power densities of electronic devices due to more compact package requirements make their thermal management a major challenge. Adequate cooling of these devices is required to increase their lifespan and maintain operational capabilities. Over the past couple of decades, microchannel heat sinks, among other solutions, have been implemented to dissipate high heat fluxes under operational parameters of practical significance. Furthermore, convective heat transfer coefficients have been increased by using a two-phase flow and by reducing the hydraulic diameters of the channels. However, this incurs an increase in pressure drop and pumping power. A novel cooling technique, film evaporation with an enhanced fluid delivery system (FEEDS), has been demonstrated to simultaneously enhance the heat transfer coefficient while minimizing the increased pressure drop and pumping power requirements. The FEEDS cooler is a manifold-microchannel system in which an array of manifolds is positioned perpendicularly on a system of parallel microchannels. In the present work, two FEEDS coolers were designed and developed to manage high heat fluxes of electronic components. The geometrical parameters of the manifolds were varied to investigate the effect of manifold parameters on thermal and hydrodynamic performances. Both single-phase and two-phase tests were performed with R-245fa refrigerant as the working fluid at different mass fluxes, and heat transfer and pressure drop performances were measured. Heat fluxes in excess of 1 kW/cm2 with a surface superheat of 38 °C and with a low-pressure drop penalty were achieved. Additionally, this FEEDS cooler showed a relatively constant heat transfer coefficient even after the optimal vapor quality was exceeded, indicating a stable liquid film.

Tracking model predictive control and moving horizon estimation design of distributed parameter pipeline systems

This manuscript proposes moving horizon control and state/parameter estimation designs for pipeline networks modelled by partial differential equations (PDEs) with boundary actuation. The spatial–temporal pressure and velocity dynamics within the pipelines are described by a system of six coupled one-dimensional first-order nonlinear hyperbolic PDEs. To address the discrete-time modelling challenge and preserve the infinite-dimensional nature of the pipeline system, the Cayley–Tustin transformation is deployed for model time discretization without any spatial discretization or model reduction. Considering the lack of full state information across the entire pipeline manifold, unknown states and uncertain parameters are estimated using moving horizon estimation (MHE). Based on the estimated states and parameters, a tracking model predictive control (MPC) strategy for the discrete-time infinite-dimensional pipeline system is proposed, which enables specific operation while ensuring physical constraint satisfaction. The effectiveness of the proposed controller and estimator designs is demonstrated via numerical examples.

Characterizing and understanding galaxies with two parameters

ABSTRACT We report the discovery of a 2D Galaxy Manifold within the multidimensional luminosity space of local galaxies. The multidimensional luminosity space is constructed using 11 bands that span from far ultraviolet to near-infrared for redshift < 0.1 galaxies observed with GALEX, SDSS, and UKIDSS. The manifold captures the diversity of observed galaxies in terms of stellar-dominated emissions and ties the correlations of various physical properties to the manifold. We find that two latent parameters are sufficient to express 93.2 per cent of the variance in the galaxy sample, suggesting that this Galaxy Manifold is one of the most efficient representations of galaxies. The transformation between the observed luminosities and the manifold parameters as an analytic mapping is provided. The manifold representation provides accurate (precision = 0.85) morphological classifications with a simple linear boundary, and galaxy properties can be estimated with minimal scatter (0.12 and 0.04 dex for star formation rate and stellar mass, respectively) by calibrating with the 2D manifold location. Under the assumption that the manifold expresses the possible parameter space of galaxies, the evolution on the manifold is considered. We find that constant and exponentially decreasing star formation histories form almost orthogonal modes of evolution on the manifold. Through these simple models, we understand that the two modes are closely related to gas content, which suggests the close relationship of the manifold to gas accretion. Lastly, the found manifold suggests a paradigm where galaxies are characterized by their mass/scale and specific SFR, which agrees with previous studies of dimensionality reduction.

Critical state generators from perturbed flatbands

One-dimensional all-bands-flat lattices are networks with all bands being flat and highly degenerate. They can always be diagonalized by a finite sequence of local unitary transformations parameterized by a set of angles θi. In a previous work, we demonstrated that quasiperiodic perturbations of a specific one-dimensional all-bands-flat lattice give rise to a critical-to-insulator transition and fractality edges separating critical from localized states. In this study, we generalize these studies and results to the entire manifold of all-bands-flat models and study the effect of the quasiperiodic perturbation on the entire manifold. For weak perturbation, we derive an effective Hamiltonian and we identify the sets of manifold parameters for which the effective model maps to extended or off diagonal Harper models and hosts critical states. For all the other parameter values, the spectrum is localized. Upon increasing the perturbation strength, the extended Harper model evolves into a system with energy dependent critical-to-insulator transitions, which we dub fractality edges. Additionally, the fractality edges are perturbation-independent, i.e., remain constant as the perturbation strength varies. The case where the effective model maps onto the off diagonal Harper model features a tunable critical-to-insulator transition at a finite disorder strength.

Theory of glide symmetry protected helical edge states in a WTe2 monolayer

Helical edge states in quantum spin Hall (QSH) materials are central building blocks of topological matter design and engineering. Despite their principal topological protection against elastic backscattering, the level of operational stability depends on manifold parameters such as the band gap of the given semiconductor system in the 'inverted' regime, temperature, disorder, and crystal orientation. We theoretically investigate electronic and transport properties of QSH edge states in large gap 1-T' WTe$_{2}$ monolayers. We explore the impact of edge termination, disorder, temperature, and interactions on experimentally addressable edge state observables, such as local density of states and conductance. We show that conductance quantization can remain surprisingly robust even for heavily disordered samples because of an anomalously small edge state decay length and additional protection related to the large direct gap allowed by glide symmetry. From the simulation of temperature-dependent resistance, we find that moderate disorder enhances the stability of conductance by localizing bulk states. We evaluate the edge state velocity and Luttinger liquid parameter as functions of the chemical potential, finding prospects for physics beyond linear helical Luttinger liquids in samples with ultra-clean and well-defined edges.

Flow Analysis of Intake Manifold Using Computational Fluid Dynamics

The main element of the air intake system is an intake manifold. These are the main components which control the flow value that will be used in the combustion chamber. Thisstudyaimsto analyse the velocity of flow distribution in all runners and to compare the design of intake manifold in previous study. Simulations are performed on two types of intake manifold design which are Design 1, the inlet end is located next to the regulator chamber whilst for Design 2, the inlet end is inthe middle of regulator chamber. Intake manifold parameters such as velocity distribution, unevenness and pressure losses along the runner are used to determine the better design for better performance. From the results, the velocity,and the pressure of intake manifold of these twotypes of design are determined. Compared with previous study, the velocity difference is 0.05%. This showed that the simulation result obtained in this study is in accordance with previous study. Next, the percentage difference between Design 1 and previous design is approximately 4%. Furthermore, the pressure losses between Design 1 and Design 2 are 0.3% and Design 1 achieved the range of acceptable values between 2500Pa to 3000Pa. The results of the velocity distribution, the evenness of each runner and the value of pressure losses shows that Design 1 met all the criteria and exhibited the best design improvement as compared to the previous design with 2% improvement.

Textile study courses – evaluation of student performance over one complete decade

The success of students and their performance are influenced by manifold parameters. The current study focusses especially on the correlation between study duration and the student performance. Investigated are the numbers of two textile related courses (bachelor and master) over the complete time frame of one decade. Data of more than 800 students are considered. Data evaluation is done with the final marks of the students and by using a calculated value – the student performance index PI. Especially discussed is the behavior of long-time students needing more than the double of the regular study duration. In this study only results of students are discussed which successfully finished the study course with a degree. Students leaving without a degree are not considered. For the bachelor course a correlation of their final grades with larger study duration can be determined. In contrast, for the master course nearly no influence of the study duration on the student performance is determined. A possible explanation for these different results can be discussed with the different reasons for longer study duration. For master course it is obviously the situation that the studying is combined with part-time or even full-time jobs. This combination of job and study course leads even for excellent students to prolonged study duration. With this background, for future developments a special designed part time master study course should be developed and offered for students who like the combination of a job carrier with gaining simultaneously a master degree.

Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

The present work advances the PC-transport approach in the context of Large Eddy Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems requires large kinetic mechanisms. However, realistic high-fidelity simulations of turbulent reacting flows still represent a big challenge on the current computational tools. Therefore, a parameterization of the thermo-chemical state-space using a reduced number of variables is needed. To this end, the potential offered by Principal Component Analysis (PCA) in identifying low-dimensional manifolds is very appealing. The present paper extends the PC-transport approach, coupled with Gaussian Process Regression (GPR), to a lifted methane/air flame in LES. Previous investigations by the authors showed the great potential of the PC-GPR model in the context of Sandia flames. This study investigated some key features of the model: the sensitivity to the training data set and the scaling methods . To this end, two different canonical reactors were used: unsteady counter-flow laminar flames (CFLF) and unsteady perfectly stirred reactor (PSR). Moreover, the authors proposes an approach to address the issue of data density inherent to large numerical data sets, by means of a kernel density weighting of the data set before applying PCA. Finally, a subgrid scale (SGS) closure model was coupled to the PC-transport approach to treat complex turbulence/chemistry interactions.

Imaging mass‐wasting sliding surfaces within complex glacial deposits along coastal cliffs using geophysics

AbstractThis study presents a multidisciplinary survey combining geological fieldwork and geophysical data to better constrain the parameters influencing the morphology and behaviour of a retreating coastal cliff. Erosion rates are spatially highly variable and hard to predict because of the manifold parameters acting on them. Among these parameters, rock resistance exerts a paramount influence on cliff retreat. Characterizing the rock resistance distribution along a coastal region requires the mapping of several key subsurface properties including the bulk lithology, faulting, fracturing, or weathering. This is a difficult and expensive task because of the high spatial variability of these factors linked to the spatial complexity of the geology. Geophysical methods can be used to tackle this challenge by quickly providing the 3D visualization and distribution of these parameters within the subsurface. A fast‐eroding portion of the Norfolk coast (UK) at West Runton is investigated using a multidisciplinary approach, combining ground‐penetrating radar, electrical resistivity tomography (ERT), cone penetration tests, and outcrop studies. The results allowed us to build a 3D geological and geophysical model of a highly complex area of glacial geology. It forms part of a relict glaciotectonic thrust‐tip moraine and sand basin sequence. The surfaces interpreted on radar data are associated with strong resistivity contrasts on the ERT data. These contrasts have been attributed to petrophysical variations between the lithological units. The base of the sand basin is marked by a low‐permeability clay bed. Its low shear strength is likely to be more susceptible to failure, hereby accelerating the erosion rate of an already fast‐eroding sand basin. The resulting model can be used as input for locally constraining the ground parameters in coastal recession and erosion models.

Global optimization of second-order sliding mode controller parameters using a new sliding surface: An experimental verification to process control system

The chattering effect, robustness and stability are the major issues in a typical Sliding Mode Control (SMC). The selection of sliding manifold parameters are complicated, time-consuming and tedious. The parameter convergence is also a challenging work. Hence, the paper accords a global optimization of Second-Order Sliding Mode Controller (SOSMC) parameters with new sliding surface using the Jaya algorithm for non-linear uncertain process tank systems. The controller (SOSMC) role is to direct the sliding surface and it’s first order time derivative to zero in a finite time.The Jaya algorithm uses ’optimal features’ by updating the solutions in populations. The effectiveness of proposed strategy is evaluated for five objective functions and compared with Artificial Bee Colony (ABC) based SOSMC, classical SOSMC, typical SMC, Non-dominated Sorting Genetic Algorithm based First-order SMC (NSGA-II FOSMC) and Proportional+Integral+Derivative (PID) controller which is validated through real-time experimentation. The asymptotic stability is guaranteed through direct Lyapunov candidate function.The statistical significance of algorithms has been verified by using Friedman, Dunn, critical time efficiency and p-value tests. The Jaya algorithm shows a better performance over reported methods concerning process speed, settling time, rise time, reaching time, overshoot, response time and parameter convergence as explored from simulation, statistical, and experimental results. Furthermore, the elevated dead-time and oscillatory processes have been presented to show the efficacy of proposed strategy.

Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data

Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some prespecified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss using the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.

A model of the basic components in the seismic Vrancea zone

Seismic processes occur and develop temporally and spatially under the action of internal determinism of global tectonics. Uncertainties related to interlacement of internal physical fields of the Earth and gravity forces of celestial bodies and their effect on global tectonics introduce an element of randomness to models of seismicity. Seismic processes are complicated and multiform because their formation is conditioned by complex variegated geological-geophysical processes occurred in the interior of the Earth and characterized by a set of manifold parameters and the results of their observations are presented as multivariate accidental values. While studying such multivariate processes the question arises: if we can set aside a part of parameters or substitute them by minor number of some of their functions and preserve at the same time all the information? For solving this problem there is a factor analysis based on finding minimal number of factors forming the maximum share of data dispersion. In the studies of the complicated nature of seismicity the factor analysis forwards a deeper understanding of the essence of seismic processes because interrelation of seismic parameters must be conditioned by parameter relations which revealing is a task of factor analysis. The modern models of seismicity and the theories explaining appearance of the earthquakes are based on indirect data, mainly on seismic observations. The basic mission of geophysical studies is solving of inverse problem, i. e. determination of the structure of the medium according to observations of physical fields characteristics. Special features of seismic actions of each earthquake is determined by such of its characteristics as tectonics, the depth of a focus, mechanism, geometry of the focus, direction and the running of the process of rocks rupture and other parameters. The pattern of a microseismic field is a reflection of all these factors effect and of local geological features on manifestations of seismic effect in the sites of day surface.

Uap: reproducible and robust HTS data analysis

BackgroundA lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis.ResultsHere we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap.Conclusionsuap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

Walking With Confidence: Safety Regulation for Full Order Biped Models

Safety guarantees are valuable in the control of walking robots, as falling can be both dangerous and costly. Unfortunately, set-based tools for generating safety guarantees (such as sums-of-squares optimization) are typically restricted to simplified, low-dimensional models of walking robots. For more complex models, methods based on hybrid zero dynamics can ensure the local stability of a pre-specified limit cycle, but provide limited guarantees. This letter combines the benefits of both approaches by using sums-of-squares optimization on a hybrid zero dynamics manifold to generate a guaranteed safe set for a ten-dimensional walking robot model. Along with this set, this letter describes how to generate a controller that maintains safety by modifying the manifold parameters when on the edge of the safe set. The proposed approach, which is applied to a bipedal RABBIT model, provides a roadmap for applying sums-of-squares techniques to high-dimensional systems. This opens the door for a broad set of tools that can generate flexible and safe controllers for complex walking robot models.