Smooth Field Research Articles

Effects of Coolant and Working Temperature on the Cavitation in an Aeronautic Cooling Pump with High Rotation Speed

The centrifugal pump with high rotation speed is the key component in the cooling system of an aircraft. Because of the high rotation speed, the impeller inlet is very prone to cavitation. Two impellers with different types of blades (cylindrical and splitter) are designed, and the numerical models of the pumps are built. The authenticity of the numerical models is validated with the corresponding experiments in terms of both the hydraulic and cavitation characteristics. Then, the effects of different coolants and working temperatures on the hydraulic and cavitation performances of the prototype models are studied based on the numerical simulations. The results show that the head and efficiency of the pump for conveying water are higher than those for conveying ethylene glycol (EG) aqueous solution and propylene glycol (PG) aqueous solution (EGaq and PGaq are defined to represent the EG aqueous solution and the PG aqueous solution, respectively). The hydraulic performance in the EGaq is slightly better than that in the PGaq. The cavitation performance of water is far less than that of the EGaq and PGaq under high working temperature. The volume of cavitation in EGaq is smaller than that in PGaq, and the volume of cavitation in the splitter blades is slightly smaller than that in the cylindrical blades. It is suggested that EGaq be used as the first option. The splitter blades can improve the cavitation performance somehow while the improvement by using the splitter blades is very limited at high rotation speeds, and the design of the short blades should be careful in order to obtain a smooth internal flow field.

Stand-Alone Multi-Attention Fusion Network for Double-Identity Fingerprint Detection

In this work, we propose SAMAF, a novel stand-alone multi-attention fusion network to detect double-identity fingerprint images. The double-identity fingerprint is a newly developed potential threat where a fake, synthetic fingerprint is created by carefully aligning ridge-lines of two genuine fingerprints belonging to the criminal and innocuous accomplice. These fake fingerprints are visually indistinguishable due to smooth orientation fields at intersection regions and thus simultaneously exploited by both entities to evade the security system. The proposed SAMAF network stems from the novel multi-instance attention module to aggregate local and global discriminative contexts. On other hand, the multi-attention fusion module is designed to consolidate attentive features across multiple scales and focus on important regions. Experimental results and ablation studies on multiple fingerprint-based presentation attack datasets show the superiority of our proposed network as compared to state-of-the-art methodologies.

Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds

This paper presents a closed-form approach to constrain a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. We show that the obstacle avoidance problem can be inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume and obstacles, and will asymptotically converge towards an attractor. We further create smooth motion fields around obstacles with edges (e.g. tables). Both obstacles and enclosures may be time-varying, i.e. moving, expanding and shrinking. The technique enables a robot to navigate within an enclosed corridor while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment, and also tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in presence of a diverse crowd with a non-uniform motion pattern.

Analytic investigation of the compatibility condition and the initial evolution of a smooth velocity field for the Navier–Stokes equation in a channel configuration

A partial differential equation has usually a regular solution at the initial time if the initial condition is smooth in space, fulfills the governing equations and is compatible with the boundary condition. In the case of the Navier–Stokes equation, the initial velocity field must also be divergence–free. It is common belief that the initial condition is compatible with the boundary condition if the initial condition fulfills the boundary condition but this is not sufficient. Such a field does not necessarily fulfill the full compatibility condition of the Navier–Stokes equation. If the condition is violated, the solution is not regular at the initial time (). This issue has been known for a while but not in the full breadth of the engineering fluid dynamics community. In this paper, a practical calculation method is presented for checking the compatibility condition. Furthermore, a smooth initial condition is presented in a periodic channel flow that violates the compatibility condition and has therefore no smooth solution at the initial instant. The calculations were performed in an analytical framework. The results for a channel configuration show that in the absence of wall–normal velocity the condition is always fulfilled and the problem has a regular solution. If the wall–normal velocity component is non-zero, the condition is usually not fulfilled but exceptional cases, fulfilling the compatibility condition can be generated with optimization methods. The generation procedure of such a field is useful to provide correct initial conditions for such time-dependent numerical flow simulations, where the very first instances are important from an engineering point of view. The presented methods can provide insight about the applicability of the chosen initial conditions.

DIC-Net: Upgrade the performance of traditional DIC with Hermite dataset and convolution neural network

Digital image correlation (DIC) is a non-contact optical method that tracks the speckle pattern on specimen surface to calculate the displacement and strain by image correlation algorithm. Although the traditional DIC method can conveniently measure surface deformation, it still has many limitations: (1) the accuracy of displacement and strain calculation needs to be improved in the case of high deformation gradient; (2) under match or over-match can hardly be avoided when the filters are used to reconstruct smooth displacement or strain field, and (3) boundary effect remains unresolved in computing the deformation near the boundary of region of interest or the discontinuous area (e.g. area near crack tip or crack face). Recently, the deep learning based DIC (Deep-DIC) has revealed its attractive ability in handling above issues in traditional DIC, and impressive results have been achieved. The mean value of the absolute error (MAE) on the test set has been optimized to 0.0361 pixels using existing Deep-DIC approaches, which are accompanied by a real-time measurement speed. The network structure and training dataset are two key factors for the deep learning method. However, the current working networks have been modified from other image tasks and cannot fully adapt to the demands of the DIC tasks, and the dataset they generated still has evident flaws, limiting the method's accuracy and generalization performance which is utilized to assess performance on samples outside the training set. In this paper, we firstly proposed a new Hermite dataset that is created by using the high-order Hermite element to take account more complex deformation, then a new network architecture designed for the DIC task has been developed to extract richer deformation features. A test set of 3216 examples containing six different modes of displacement is used to compare the performance of our network with others. The proposed DIC-Net-d achieves the lowest MAE in the test set. Meanwhile, in the Star5 image sets from DIC-Challenge, the proposed DIC-Net-d achieves a spatial resolution of 17.25 pixels and a noise level of 0.0136 which outperforms existing traditional and non-traditional methods. Finally, the strain network trained by our Hermite dataset is also successful in predicting the strain field of Star6 in the DIC challenge. The experiment results show the superiority of the proposed Hermite dataset and new network with respect to other Deep-DIC methods.

Fast Multipole Boundary Element Method (FMM/BEM) for the solution of the Navier-Stokes in primitive variables based on the Burton and Miller formulation in two-dimensions

In the present work the fast multipole boundary element method (FMM/BEM) is presented for the solution of incompressible Navier-Stokes equations in 2 dimensions. This is a velocity-pressure primitive variable formulation abbreviated as uv-p. The Stokes-let fundamental solution is used as a weight function in the momentum equation eliminating the pressure from free variable. This way, the LBB conditions are bypassed removing any restriction in the type of grid, placement of nodes, or interpolation. Most of the numerical methods cannot use constant elements for all fields to solve the Navier-Stokes. BEM is the only known so far semi-analytical method capable of such an achievement. The continuity is enforced with the correction of velocity, using a scalar potential, the gradient of which is computed directly with hypersingular equations. The stress hypersingular equation is added to the boundary nodes rendering the well known Burton and Miller formulation and recommendation about the coupling constant is given. Accurate results for cavity Re = 1000 with constant elements and Re=10000 with linear quadrilaterals are obtained and presented. The reached degrees of freedom (dof) are beyond 1 million. Two more problems with mixed BCs are solved. The flow past cylinder is a kind of problem in which both singular and hypersingular equations must be used to ensure convergence. The main advantage of the method is the direct computation of the wall normal stresses or tractions, which is of cornerstone importance in many realistic applications. The tractions are obtained directly from the solution of the system of equations and can be integrated on the surface of a bluff body, to obtain forces and drag and lift coefficients. The pressure is computed in a post-processing phase with hypersingular equation derived in the framework of the present method. Smooth pressure field is obtained in all cases, even when the problem is solved with unstructured grids and constant elements. The method permits the use of non-conforming grids, and it can easily be combined with other numerical methods to obtain wall tractions and stresses via hypersingular equations. Moments for pressure and convective terms are derived.

Two-phase fully-coupled smoothed particle hydrodynamics (SPH) model for unsaturated soils and its application to rainfall-induced slope collapse

In this paper, a new fully-coupled Smoothed Particle Hydrodynamics (SPH) formulation for unsaturated soils is developed to study the influence of rainfall infiltration on slope stability. The single-layer two-phase formulation is investigated in SPH for the first time to simulate the response of unsaturated soils. The use of a single set of particles improves the computational efficiency and facilitates the implementation of infiltration boundary conditions. The Drucker–Prager strain-softening model, with the use of Bishop's effective stress, is adopted as the soil’s constitutive model. New extensions of a first-order consistent wall boundary treatment are proposed for the coupled hydro-mechanical problem to enforce non-slip/free-slip conditions for the soil phase and water phase. A novel stress diffusion algorithm for general application is introduced to smooth out the numerical noise in the stress field under large deformation. The accuracy of the formulated SPH model is examined with available analytical solutions and experimental data. The proposed numerical scheme is finally applied to the simulation of rainfall-induced slope collapse of an unsaturated slope with two different bedrock geometries. Results demonstrate that the geometry of the bedrock is shown to play an important role in the failure initiation and propagation of the collapse. It is found that the proposed model allows the investigation of both triggering and post-failure mechanism, providing a smooth stress field even at large deformations.

Some consequences of the distributional stress equilibrium condition

We derive two consequences of the distributional form of the stress equilibrium condition while incorporating piecewise smooth stress and body force fields with singular concentrations on an interface. First, we obtain the local equilibrium conditions in the bulk and at the interface, the latter inclusive of interfacial stress and stress dipole fields. Second, we obtain the necessary and the sufficient conditions on the divergence-free non-smooth stress field for there to exist a stress function field such that the stress equilibrium is trivially satisfied. In doing so, we allow the domain to be non-contractible with mutually disjoint connected boundary components. Both derivations illustrate the utility of the theory of distributions in dealing with singular stress fields.

An Analytic and Numerical Analysis of Weighted Singular Cauchy Integrals with Exponential Weights on ℝ

This article concerns an analytic and numerical analysis of a class of weighted singular Cauchy integrals with exponential weights with finite moments and with smooth external fields with varying smooth convex rate of increase for large argument. Our analysis relies in part on weighted polynomial interpolation at the zeros of orthonormal polynomials with respect to w 2. We also study bounds for the first derivatives of a class of functions of the second kind for w 2.

Galaxy and halo angular clustering in ΛCDM and modified gravity cosmologies

Using a suite of $N$-body simulations, we study the angular clustering of galaxies, halos, and dark matter in $\mathrm{\ensuremath{\Lambda}}$ cold dark matter and modified gravity (MG) scenarios. We consider two general categories of such MG models, one is the $f(R)$ gravity, and the other is the normal branch of the Dvali-Gabadadze-Porrati brane world. To measure angular clustering we construct a set of observer-frame light cones and resulting mock sky catalogs. We focus on the area-averaged angular correlation functions ${W}_{J}$, and the associated reduced cumulants ${S}_{J}\ensuremath{\equiv}{W}_{J}/{W}_{2}^{(J\ensuremath{-}1)}$, and robustly measure them up to the ninth order using counts in cells. We find that $0.15<z<0.3$ is the optimal redshift range to maximize the MG signal in our light cones. Analyzing various scales for the two types of statistics, we identify up to 20% relative departures in MG measurements from general relativity (GR), with varying signal significance. For the case of halos and galaxies, we find that third-order statistics offer the most sensitive probe of the different structure formation scenarios, with both ${W}_{3}$ and the reduced skewness ${S}_{3}$ reaching from $2\ensuremath{\sigma}$ to $4\ensuremath{\sigma}$ significance at angular scales $\ensuremath{\theta}\ensuremath{\sim}0.13\ifmmode^\circ\else\textdegree\fi{}$. The MG clustering of the smooth dark matter field is characterized by even stronger deviations ($\ensuremath{\gtrsim}5\ensuremath{\sigma}$) from GR, albeit at a bit smaller scales of $\ensuremath{\theta}\ensuremath{\sim}0.08\ifmmode^\circ\else\textdegree\fi{}$, where baryonic physics is already important. Finally, we stress that our mock halo and galaxy catalogs are characterized by rather low surface number densities when compared to existing and forthcoming state-of-the-art photometric surveys. This opens up exciting potential for testing GR and MG using angular clustering in future applications, with even higher precision and significance than reported here.

Optimizing creation operators for charmonium spectroscopy on the lattice

Smearing the bare quantum fields in lattice calculations before applying composite hadron creation operators has a long record of substantially improving overlaps onto low-lying energy eigenstates. A technique called distillation which defines smearing for quark fields as a low-rank linear projection operator into a small vector space of smooth gauge-covariant fields has proven to be both effective and versatile in hadron spectroscopy calculations albeit with significant computational cost . In this paper, more general operators in this space of smooth fields are introduced and optimised, which enhances the performance of the method when tested on systems of heavy quark-anti-quark pairs close to the charmonium energy scale.

Singular Points and Singular Curves in von Kármán Elastic Surfaces

Mechanical fields over thin elastic surfaces can develop singularities at isolated points and curves in response to constrained deformations (e.g., crumpling and folding of paper), singular body forces and couples, distributions of isolated defects (e.g., dislocations and disclinations), and singular metric anomaly fields (e.g., growth and thermal strains). With such concerns as our motivation, we model thin elastic surfaces as von Kármán plates and generalize the classical von Kármán equations, which are restricted to smooth fields, to fields which are piecewise smooth, and can possibly concentrate at singular curves, in addition to being singular at isolated points. The inhomogeneous sources to the von Kármán equations, given in terms of plastic strains, defect induced incompatibility, and body forces, are likewise allowed to be singular at isolated points and curves in the domain. The generalized framework is used to discuss the singular nature of deformation and stress arising due to conical deformations, folds, and folds terminating at a singular point.

Asymptotic topology of excursion and nodal sets of Gaussian random fields

Abstract Let M be a compact smooth manifold of dimension n with or without boundary, or an affine polytope, and let f : M → ℝ {f:M\to\mathbb{R}} be a smooth Gaussian random field. It is very natural to suppose that for a large positive real u, the random excursion set { f ≥ u } {\{f\geq u\}} is mostly composed of a union of disjoint topological n-balls. Using the constructive part of (stratified) Morse theory, we prove that in average, this intuition is true, and provide for large u the asymptotic of the expected number of such balls, and so of connected components of { f ≥ u } {\{f\geq u\}} . We similarly show that in average, the high nodal sets { f = u } {\{f=u\}} are mostly composed of spheres, with the same asymptotic than the one for excursion set. A quantitative refinement of these results using the average of the Euler characteristic proved in former works by Adler and Taylor provides a striking asymptotic of the constant defined by Nazarov and Sodin, again for large u. This new Morse theoretical approach of random topology also applies to spherical spin glasses with large dimension.

F2F-NN: A Field-to-Field Wind Speed Retrieval Method of Microwave Radiometer Data Based on Deep Learning

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously.

Invariant Gaussian fields on homogeneous spaces: explicit constructions and mean nodal volume

We review and study some of the properties of smooth Gaussian random fields defined on a homogeneous space, under the assumption that the probability distribution is invariant under the isometry group of the space. We first give an exposition, building on early results of Yaglom, of the way in which representation theory and the associated special functions make it possible to give completely explicit descriptions of these fields in many cases of interest. We then turn to the expected size of the zero-set: extending two-dimensional results from Optics and Neuroscience, we show that every invariant field comes with a natural unit of volume (defined in terms of the geometrical redundancies in the field) with respect to which the average size of the zero-set is given by a universal constant depending only on the dimension of the source and target spaces, and not on the precise symmetry exhibited by the field.

Numerical approximation of partial differential equations by a variable projection method with artificial neural networks

We present a method for solving linear and nonlinear partial differential equations (PDE) based on the variable projection framework and artificial neural networks. For linear PDEs, enforcing the boundary/initial value problem on the collocation points gives rise to a separable nonlinear least squares problem about the network coefficients. We reformulate this problem by the variable projection approach to eliminate the linear output-layer coefficients, leading to a reduced problem about the hidden-layer coefficients only. The reduced problem is solved first by the nonlinear least squares method to determine the hidden-layer coefficients, and then the output-layer coefficients are computed by the linear least squares method. For nonlinear PDEs, enforcing the boundary/initial value problem on the collocation points gives rise to a nonlinear least squares problem that is not separable, which precludes the variable projection strategy for such problems. To enable the variable projection approach for nonlinear PDEs, we first linearize the problem with a Newton iteration, using a particular linearization formulated in terms of the updated approximation field. The linearized system is solved by the variable projection framework together with artificial neural networks. Upon convergence of the Newton iteration, the neural-network coefficients provide the representation of the solution field to the original nonlinear problem. We present ample numerical examples to demonstrate the performance of the method developed herein. For smooth field solutions, the errors of the current method decrease exponentially as the number of collocation points or the number of output-layer coefficients increases. We compare extensively the current method with the extreme learning machine (ELM) method from a previous work. Under identical conditions and configurations, the current method exhibits an accuracy significantly superior to the ELM method.

The halo of M 105 and its group environment as traced by planetary nebula populations

Context. M 105 (NGC 3379) is an early-type galaxy in the nearby Leo I group, the closest galaxy group to contain all galaxy types and therefore an excellent environment to explore the low-mass end of intra-group light (IGL) assembly. Aims. We present a new and extended kinematic survey of planetary nebulae (PNe) in M 105 and the surrounding 30′×30′ in the Leo I group with the Planetary Nebula Spectrograph (PN.S) to investigate kinematically distinct populations of PNe in the halo and the surrounding IGL. Methods. We use PNe as kinematic tracers of the diffuse stellar light in the halo and IGL, and employ photo-kinematic Gaussian mixture models to (i) separate contributions from the companion galaxy NGC 3384, and (ii) associate PNe with structurally defined halo and IGL components around M 105. Results. We present a catalogue of 314 PNe in the surveyed area and firmly associate 93 of these with the companion galaxy NGC 3384 and 169 with M 105. The PNe in M 105 are further associated with its halo (138) and the surrounding exponential envelope (31). We also construct smooth velocity and velocity dispersion fields and calculate projected rotation, velocity dispersion, and λR profiles for the different components. PNe associated with the halo exhibit declining velocity dispersion and rotation profiles as a function of radius, while the velocity dispersion and rotation of the exponential envelope increase notably at large radii. The rotation axes of these different components are strongly misaligned. Conclusions. Based on the kinematic profiles, we identify three regimes with distinct kinematics that are also linked to distinct stellar population properties: (i) the rotating core at the centre of the galaxy (within 1Reff) formed in situ and is dominated by metal-rich ([M/H] ≈ 0) stars that also likely formed in situ, (ii) the halo from 1 to 7.5Reff consisting of a mixture of intermediate-metallicity and metal-rich stars ([M/H] > −1), either formed in situ or was brought in via major mergers, and (iii) the exponential envelope reaching beyond our farthest data point at 16Reff, predominately composed of metal-poor ([M/H] < −1) stars. The high velocity dispersion and moderate rotation of the latter are consistent with those measured for the dwarf satellite galaxies in the Leo I group, indicating that this exponential envelope traces the transition to the IGL.

Hybrid Feedback for Global Tracking on Matrix Lie Groups $SO(3)$ and $SE(3)$

We introduce a new hybrid control strategy, which is conceptually different from the commonly used synergistic hybrid approaches, to efficiently deal with the problem of the undesired equilibria that precludes smooth vectors fields on <inline-formula><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> from achieving global stability. The key idea consists in constructing a suitable potential function on <inline-formula><tex-math notation="LaTeX">$SO(3)\times \mathbb {R}$</tex-math></inline-formula> involving an auxiliary scalar variable, with flow and jump dynamics, which keeps the state away from the undesired critical points while, at the same time, guarantees a decrease of the potential function over the flows and jumps. Based on this new hybrid mechanism, a hybrid feedback control scheme for the attitude tracking problem on <inline-formula><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula>, endowed with global asymptotic stability and semiglobal exponential stability guarantees, is proposed. This control scheme is further improved through a smoothing mechanism that removes the discontinuities in the input torque. The third hybrid control scheme, proposed in this article, removes the requirement of the angular velocity measurements, while preserving the strong stability guarantees of the first hybrid control scheme. This approach has also been applied to the tracking problem on <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> to illustrate its advantages with respect to the existing synergistic hybrid approaches. Finally, some simulation results are presented to illustrate the performance of the proposed hybrid controllers.

Integrated Geophysical and Geospatial Approaches for Delineating Groundwater Potential Zones in Karachi, Pakistan

Availability of subsurface fresh water in coastal cities of the world is a growing problem due to sea level intrusion and less seepage. We have utilized an integrated data analysis in which conventional geophysical methods was used to collect primary data for the groundwater resources in Karachi and geospatial approaches were used to model the hydrogeological parameters. It was aimed to investigate geological/hydrogeological conditions of any aquifer system in the study area. The geophysical survey was conducted to acquire electrical resistivity data in the outskirts of Karachi. The geophysical survey was carried out at twenty-one stations by adopting vertical electrical sounding technique with schlumberger configuration. The field data was processed in an iterative process to improve the signal to noise ratio and obtain smooth field data curves for delineation of the aquifer. We have interpreted field data to model the geological information and determine the hydrogeophysical parameters of respective layers. These parameters including the number of layers, aquifer resistivity, aquifer lithology, aquifer thickness and depth to the aquifer, are determined at each field station. The acquired dataset of hydrogeophysical parameters was used to build a geospatial database. The multi-criteria analysis and decision-making process were utilized in GIS based program to model spatial distribution models of hydrogeophysical parameters. The results identified aquifer system in the depth ranging from 53.3 meter to 143.9 meter. The aquifer in the area are mostly sandstone having sufficient thickness which varies from northeast to south and southwest due to undulating topography of the area. The maximum potential of the groundwater is identified in the south which is suitable for water exploration because of low resistivity zone, high aquifer thickness, and flow of drainage network.

Interpolation of electrophysiology parameters using OpenEP: technology development and clinical application

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background Interpolation of data is common during clinical electrophysiology procedures. Applications include local activation mapping, voltage mapping and novel techniques including Sparkle and Coherence mapping. Nevertheless, underlying interpolation algorithms are proprietary and therefore challenging to reproduce. Importantly, direct comparison of electroanatomic datasets between system vendors is therefore not possible. Purpose We sought to (1) develop an open-source architecture for interpolation within the Open Electrophysiology Framework for Research (OpenEP; https://openep.io); (2) to provide three interpolation methods within this architecture and (3) to evaluate their performance against clinical data. Method The software architecture is shown in Figure 1A. The currently available methods are Radial Basis [1], Scattered Interpolant [2] and Local Smoothing [3]. Default parameters for each method are shown in Figure 1B. The performance of each method was assessed using clinical left atrial mapping data, using the default options for each scheme. Following interpolation, a sample of 1000 activation/voltage points per mesh was used for analysis. For each interpolation method, correlation with clinical data was assessed using the intra-class correlation coefficient, whilst agreement was assessed using Bland Altman limits of agreement. Results For activation mapping, radial basis interpolation resulted in a smoother field than local smoothing, whilst scattered interpolation required more filtering of extreme values. Correlations between interpolated and original activation times were excellent for all interpolation schemes (radial basis R=0.91, p&amp;lt;0.0001; local smoothing R=0.95, p&amp;lt;0.0001; scattered interpolant R=0.92, p&amp;lt;0.0001). Local smoothing resulted in the narrowest 95 percent limits of agreement (-19 to +20ms), compared to radial basis (-24 to +28ms) and scattered interpolation (-22 to +25ms). For voltage mapping, the interpolation schemes resulted in similar appearances of low voltage areas, however correlations with clinical data were weaker than for activation mapping (radial basis R=0.84, p&amp;lt;0.0001; local smoothing R=0.82, p&amp;lt;0.0001; scattered interpolant R=0.79, p&amp;lt;0.0001). The 95 percent limits of agreement were wide as a proportion of the mean data values, ranging from 83% (-0.8 to +0.66mV) for local smoothing to 97% (-0.78 to +0.63mV) for radial basis interpolation. Conclusion An extensible architecture is provided for data interpolation in OpenEP together with three interpolation methods. The methods performed wellfor local activation time interpolation but variation compared to clinical data was greater for voltage mapping. This new architecture will permit the optimisation of interpolation methods against "gold standard" simulation or histological data and facilitate comparison of datasets between system vendors.