Field Reconstruction Research Articles

Optimization Design of Laser Arrays Based on Absorption Spectroscopy Imaging for Detecting Temperature and Concentration Fields.

Detecting temperature and concentration fields within engine combustors holds paramount significance in enhancing combustion efficiency and ensuring operational safety. Within the realm of engine combustors, the laminar absorption spectroscopy technique has garnered considerable attention. Particularly crucial is the optimization of the optical path configuration to enhance the efficacy of reconstruction. This study presents a flame parameter field reconstruction model founded on laminar absorption spectroscopy. Furthermore, an optimization approach for refining the optical path configuration is delineated. In addressing non-axisymmetric flames, the simulated annealing algorithm (SA) and Harris's Hawk algorithm (HHO) are employed to optimize the optical path layout across varying beam quantities. The findings underscore a marked reduction in imaging errors with the optimized optical path configuration compared to conventional setups, thereby elevating detection precision. Notably, the HHO algorithm demonstrates superior performance over the SA algorithm in terms of optimization outcomes and computational efficiency. Compared with the parallel optical path, the optimized optical path of the HHO algorithm reduces the temperature field error by 25.5% and the concentration field error by 26.5%.

Sparsity-driven loudspeaker gain optimization for sound field reconstruction with spherical microphone array

The paper presents a sparsity-driven method utilizing loudspeakers to reconstruct spatial sound fields using measurements obtained from a spherical microphone array (SMA). Employing spherical harmonics decomposition (SHD), the SMA recordings are characterized in the spherical harmonics domain. The gains for the loudspeakers are determined through an optimization problem, equating spherical harmonics pressure coefficients from primary and secondary sources. Furthermore, the sparsity within the loudspeaker feeds is redefined as a constrained sparse optimization problem, integrating linearity and orthogonality constraints. This method effectively reduces the required loudspeakers while maintaining sound field quality. The Bregman iteration method is applied to solve the constrained optimization problem. Rigorous evaluation based on reconstructed sound fields and objective measures highlights significant enhancements compared to least square and compressed sensing methods.

Half-Space Sound Field Reconstruction Based on the Combination of the Helmholtz Equation Least-Squares Method and Equivalent Source Method.

In practical conditions, near-field acoustic holography (NAH) requires the measurement environment to be a free sound field. If vibrating objects are located above the reflective ground, the sound field becomes non-free in the presence of a reflecting surface, and conventional NAH may not identify the sound source. In this work, two types of half-space NAH techniques based on the Helmholtz equation least-squares (HELS) method are developed to reconstruct the sound field above a reflecting plane. The techniques are devised by introducing the concept of equivalent source in HELS-method-based NAH. Two equivalent sources are tested. In one technique, spherical waves are used as the equivalent source, and the sound reflected from the reflecting surface is regarded as a linear superposition of orthogonal spherical wave functions of different orders located below the reflecting surface. In the other technique, some monopoles are considered equivalent sources, and the reflected sound is considered a series of sounds generated by simple sources distributed under the reflecting surface. The sound field is reconstructed by matching the pressure measured on the holographic surface with the orthogonal spherical wave source in the vibrating object and replacing the reflected sound with an equivalent source. Therefore, neither technique is related to the surface impedance of the reflected plane. Compared with the HELS method, both methods show higher reconstruction accuracy for a half-space sound field and are expected to broaden the application range of HELS-method-based NAH techniques.

A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Floating nuclear power plants (FNPPs) have the potential to supply abundant electricity, hot water, and freshwater resources for offshore islands and offshore oil and gas extraction platforms. However, the consequences of a nuclear reactor accident can be extremely serious. In addition, there may be many uncertain factors in the actual operation of FNPPs. These factors lead to higher structural safety requirements. So， developing more scientific structural monitoring methods is an effective way to enhance the structural safety of FNPP. Traditional hull monitoring schemes can only track bending and torsional forces on specific cross-sections, failing to identify the overall stress field of the entire vessel structure. The paper proposes a reconstruction method for the stress field, bending moment distribution, and wave parameters of FNPP using regular wave identification. Initially, irregular waves are decomposed into a linear combination of multiple regular waves with different amplitudes and frequencies. Subsequently, the strains induced by these regular waves on the FNPP are utilized as basic functions to establish a mathematical model for reconstructing the stress field. The stress field, bending moment distribution, and wave parameters of the FNPP can be reconstructed by identifying the combination coefficients of the regular wave loads. The ill-posedness of the mathematical model for stress field reconstruction has the potential to significantly impact the accuracy of the stress field reconstruction, which was addressed by optimizing the sensor installation position in this study. Meanwhile, the optimization of the sensor installation position greatly reduces the dimensionality of mathematical models. Finally, a numerical study, i.e. FNPP being designed by China, was used to validate the accuracy of the proposed stress field reconstruction algorithm. The reconstructed stress field of the FNPP closely matched the original stress distribution under various irregular wave conditions.

Sound field reconstruction using improved ℓ1-norm and the Cauchy penalty method

Sound field reconstruction using improved ℓ1-norm and the Cauchy penalty method

Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings

AbstractAccurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high‐rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real‐world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real‐world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high‐rise buildings, paving the way for advancements in digital twins, real‐time monitoring, and risk assessment of wind loads.

On the Parameter Calibration Method for Dual-Spectral Triocular Camera

The dual-spectrum triocular camera system composed of a binocular visible light camera and mid-infrared camera is used for high-precision thermal fault detection and thermal field reconstruction of industrial equipment. The realization of its function depends on the high-precision camera parameter calibration. The difficulty lies in how to realize the infrared camera calibration quickly and improve the parameter accuracy of multi-lens camera. In this paper, according to the characteristics of the dual-spectral triocular camera system, the theoretical model is constructed, and the circular asymmetric calibration plate under infrared supplementary light is selected as the calibration object through experiments. A method for combining infrared image adaptive histogram enhancement and binarization processing based on the Sauvola algorithm is proposed to effectively calibrate the infrared camera. A global parameter optimization method based on traditional passive vision binocular stereo calibration is proposed. The optimization of experimental parameters finds the reprojection error value is 0.16, which meets the demand of high-precision calibration and solves the problem of difficult-to-calibrate weak image texture of infrared camera and the calibration accuracy of the camera under different imaging capabilities.

Enhancing Accuracy In 3D Defocusing Particle Tracking Using 3D-Printed Ramp

Three-dimensional defocusing particle tracking velocimetry (3D DPTV) is an emerging tool for investigating fluid motion within microfluidic devices, where observation is typically limited to a single direction. This technique enables the reconstruction of 3D flow fields by analyzing the defocused particle images. The accuracy of 3D DPTV significantly relies on the collection of a proper calibration image stack containing particle images with precisely known defocus distances. One major challenge is to insert a microscale calibration target inside the microfluidic devices due to the small length scales of microfluidic devices. While the alternative approaches such as using tilted glass slides or conducting z-scans of particles fixed at the bottom wall, face several limitations. Here, we present the application of a two-photon polymerization (2PP) based method to manufacture a microscale reference ramp as the calibration target. We demonstrate the fabrication of a precise reference ramp structure that spans the desired calibration range to enhance accuracy in 3D defocusing particle tracking. We experimentally and computationally compare the result with the commonly employed methods to obtain the calibration image stack. The performance of the 2PP-based calibration ramps is compared with the z-scanning method using the General Particle tracking (GPTV). The results demonstrate that the z-scanning method underestimates the defocused distances compared to the ground truth provided by the ramp approach, with the error increasing as the defocused distance grows. The integration of microscale calibration targets using 2PP fabrication method offers a significant advancement in the reliability and accuracy of 3D DPTV measurements in microfluidic systems. By enabling precise, device-specific calibration, this approach can enhance the fidelity of flow field reconstructions, benefiting a wide range of microfluidic applications that rely on detailed velocity field information.

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

Revisit Liu & Katz (2006) And Zigunov & Charonko (2024): On The Equivalency Of Omni-Directional Integration And Pressure Poisson Equation

In this paper we demonstrate the equivalency of Omni-Directional Integration (ODI) and the Pressure Poisson Equation (PPE) for pressure field reconstruction from corrupted image velocimetry data. Over the years, it has been long debated which of the two families of methods is better for pressure reconstruction, direct pressure gradient integration (particularly ODI) versus PPE. Some have claimed that ODI is fundamentally different, and far more accurate than PPE; while other studies observed similar reconstruction accuracy between ODI and PPE (McClure & Yarusevych, 2017). This debate has been filled with confusion and conflicting results until a recent breakthrough by Zigunov & Charonko (2023, 2024) while trying to improve the computational efficiency of ODI. In a series of works, Zigunov & Charonko (2023, 2024) reformulated the iterative integration process of ODI into a system of linear equations resembling the discretized PPE, alluding to a deep connection between ODI and PPE. With careful numerical treatment, we show that ODI can be viewed as pursuing the minimal norm solution to a Poisson equation with pure Neumann boundary conditions. We provide a detailed and physical explanation for why some have reported poor robustness of the PPE, highlighting critical nuances in its numerical implementation, and explain why the ODI is more robust to random noise in the data. We hope to put an end to the PPE versus ODI debate and clear up the confusion surrounding how these and when these methods perform well. With these new comprehensions, we can leverage the established regularization techniques and efficient numerical algorithms of elliptic equations to improve PPE/ODI-based pressure field reconstruction.

A Particle Based Approach For Improved Resolution PIV And TOMO-PIV Based On The Coherent Point Drift And The Affine Least-Squares Transformation

Particle Image Velocimetry (PIV) is a widely used method for flow diagnostics, but there is still potential for improvement, particularly in terms of velocity gradient estimation and computational cost when considering three-dimensional problems. This paper presents a framework that combines a Particle Tracking Velocity (PTV) approach with local gradient-based Eulerian reconstruction to improve PIV performance. The approach uses the Coherent Point Drift (CPD) method for particle pairing and introduces the Affine Least-Squares Transformation (ALST) for local deformation gradient estimation. The CPD method consists of pairing particles whose positions at two successive instants have been obtained from the images. The ALST estimates local deformation gradients, allowing the Eularian reconstruction of the velocity field. The effectiveness of the proposed method compared to traditional PIV algorithms is demonstrated by synthetic test cases in both 2D and 3D configurations. In 2D cases, the CPD+ALST approach outperforms standard PIV methods, especially in capturing local velocity gradients. In 3D cases, comparisons with TOMO-PIV show the improved performance of CPD+ALST in the context of locally large velocity gradients. However, the linear nature of ALST makes it less efficient than quadratic binning techniques. The study demonstrates the potential of CPD+ALST to improve velocity field reconstruction in complex flows.

A Self‐Supervised Framework for Refined Reconstruction of Geophysical Fields via Domain Adaptation

AbstractReconstructing fine‐grained, detailed spatial structures from time‐evolving coarse‐scale geophysical fields has been a long‐standing challenge. Current deep learning approaches addressing this issue generally require massive fine‐scale fields as supervision, which is often unavailable due to limitations in existing observational systems and the scarcity of widespread high‐precision sensors. Here, we present AdaptDeep, a self‐supervised framework for refined reconstruction of geophysical fields via domain adaptation from the coarse‐scale source domain to the fine‐scale target domain. This method incorporates two pretext tasks, cropped field reconstruction and temporal augmentation‐assisted contrastive learning, to leverage spatial and temporal correlations in the target domain. A global propagation structure is proposed in the feature extraction network to leverage bidirectional information for enhanced long‐range dependencies and robustness against estimation errors. In experiments, AdaptDeep correctly identifies local, fine structures and significantly recovers 81.2% detailed information in sea surface temperature fields.

A generative adversarial network based on an efficient transformer for high-fidelity flow field reconstruction

The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal complexity of flows and the lack of standardized benchmark datasets. In this study, we generate a low- and high-fidelity dataset containing 25 600 snapshots of four representative flow dynamics simulations using eight different numerical-precision and grid-resolution configurations. Using this dataset, we develop a physics-guided transformer-based generative adversarial network (PgTransGAN) for concurrently handling numerical-precision and grid-resolution enhancement. PgTransGAN leverages a dual-discriminator-based generative adversarial network for capturing continuous spatial and temporal dynamics of flows and applies a soft-constraint approach to enforce physical consistency in the reconstructed data using gradient information. An efficient transformer model is also developed to obtain the long-term temporal dependencies and further alleviate storage constraints. We compare the performance of PgTransGAN against standard linear interpolation and solutions based solely on convolutional neural networks or generative adversarial networks, and demonstrate that our method achieves better reconstruction quality at the data, image, and physics levels with an upscaling factor of 4 or even 8 in each grid dimension.

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of prior information about block partitions, resulting in poor accuracy in sound field reconstruction. In this paper, a pattern-coupled Bayesian compressive sensing method is proposed for sparse reconstruction of sound fields. The proposed method establishes a hierarchical Gaussian-Gamma probability model with a pattern-coupled prior based on the equivalent source method, transforming the sound field reconstruction problem into recovering the sparse coefficient vector of the equivalent source strengths within the compressive sensing framework. A set of hyperparameters is introduced to control the sparsity of each element in the sparse coefficient vector of the equivalent source strengths, where the sparsity of each element is determined by both its own hyperparameters and those of its immediate neighbors. This approach enables the promotion of block sparse solutions and achieves better performance in solving for the sparse coefficient vector of the equivalent source strengths without prior information of block partitions. The effectiveness and superiority of the proposed method in reconstructing sound fields are verified by simulations and experiments.

Super-resolution reconstruction of flow fields coupled with feature recognition

Super-resolution reconstruction of flow fields coupled with feature recognition

An improved deep learning model for sparse reconstruction of cavitation flow fields

An improved deep learning model for sparse reconstruction of cavitation flow fields

Peculiar Velocity Reconstruction from Simulations and Observations Using Deep Learning Algorithms

In this paper, we introduce a U-Net model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the U-Net model with simulation data under more realistic conditions, including the redshift space distortion effect and halo mass threshold. Our results show that the U-Net model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient, and 27% in average coherence. The deep learning algorithm exhibits exceptional capacities to capture velocity features in nonlinear regions and substantially improve reconstruction precision in boundary regions. We then apply the U-Net model trained under Sloan Digital Sky Survey (SDSS) observational conditions to the SDSS Data Release 7 data for observational 3D peculiar velocity reconstructions.

Real-time vacuum plume flow field reconstruction during lunar landings based on deep learning

In space missions, the vacuum plume generated by rocket engines can negatively impact spacecraft. Therefore, researching the vacuum plume is crucial to guarantee the regular operation of spacecraft. The conventional numerical simulation methodology, the direct simulation Monte Carlo (DSMC) method, is time-consuming and lacks real-time calculation capabilities. Recently, deep learning (DL) methods have emerged in the field of fluid dynamics. In this study, a DL model trained by a convolutional neural network with multiple decoders is introduced to predict the vacuum plume flow field during lunar landings. The network processes shape topology information and boundary conditions as inputs, yielding flow field data including velocity and pressure fields as outputs. Meanwhile, the flow field prediction results under different conditions and training methods are discussed. The results show that the predicted flow field under different lunar surface conditions is in accord with the DSMC results. The maximum mean and standard deviation errors of the data distribution of each flow field do not exceed 9.72% and 9.07%, respectively. Different training methods with flat and inclined lunar surfaces also have an impact on the prediction results. Compared with the DSMC method, the DL method exhibits higher efficiency with a speedup of about four orders of magnitude, indicating that the DL-based flow field reconstruction method has strong application prospects in the real-time computation of vacuum plume flow fields.

Flow field reconstruction from sparse sensor measurements with physics-informed neural networks

In the realm of experimental fluid mechanics, accurately reconstructing high-resolution flow fields is notably challenging due to often sparse and incomplete data across time and space domains. This is exacerbated by the limitations of current experimental tools and methods, which leave critical areas without measurable data. This research suggests a feasible solution to this problem by employing an inverse physics-informed neural network (PINN) to merge available sparse data with physical laws. The method's efficacy is demonstrated using flow around a cylinder as a case study, with three distinct training sets. One was the sparse velocity data from a domain, and the other two datasets were limited velocity data obtained from the domain boundaries and sensors around the cylinder wall. The coefficient of determination (R2) coefficient and mean squared error (RMSE) metrics, indicative of model performance, have been determined for the velocity components of all models. For the 28 sensors model, the R2 value stands at 0.996 with an associated RMSE of 0.0251 for the u component, while for the v component, the R2 value registers at 0.969, accompanied by an RMSE of 0.0169. The outcomes indicate that the method can successfully recreate the actual velocity field with considerable precision with more than 28 sensors around the cylinder, highlighting PINN's potential as an effective data assimilation technique for experimental fluid mechanics.

Proton deflectometry analysis in magnetized plasmas: Magnetic field reconstruction in one dimension.

Proton deflectometry is used in magnetized high-energy-density plasmas to observe electromagnetic fields. We describe a reconstruction algorithm to recover the electromagnetic fields from proton fluence data in 1-D. The algorithm is verified against analytic solutions and applied to example data. Next, we study the role of source fluence uncertainty for 1-D reconstructions. We show that reconstruction boundary conditions can be used to constrain the source fluence profile and use this to develop a reconstruction using a specified pair of boundary conditions on the magnetic field. From these considerations, we experimentally demonstrate a hybrid mesh-fluence reconstruction technique where fields are reconstructed from fluence data in an interior region with boundary conditions supplied by direct mesh measurements at the boundary.