Field Structure Research Articles

Simulating Compressive Stream Interaction Regions during Parker Solar Probe’s First Perihelion Using Stream-aligned Magnetohydrodynamics

We used the stream-aligned magnetohydrodynamics (SA-MHD) model to simulate Carrington rotation 2210, which contains Parker Solar Probe’s (PSP) first perihelion at 36.5 R ⊙ on 2018 November 6, to provide context to the in situ PSP observations by FIELDS and SWEAP. The SA-MHD model aligns the magnetic field with the velocity vector at each point, thereby allowing for clear connectivity between the spacecraft and the source regions on the Sun, without unphysical magnetic field structures. During this Carrington rotation, two stream interaction regions (SIRs) form, due to the deep solar minimum. We include the energy partitioning of the parallel and perpendicular ions and the isotropic electrons to investigate the temperature anisotropy through the compression regions to better understand the wave energy amplification and proton thermal energy partitioning in a global context. Overall, we found good agreement in all in situ plasma parameters between the SA-MHD results and the observations at PSP, STEREO-A, and Earth, including at PSP’s perihelion and through the compression region of the SIRs. In the typical solar wind, the parallel proton temperature is preferentially heated, except in the SIR, where there is an enhancement in the perpendicular proton temperature. This is further showcased in the ion cyclotron relaxation time, which shows a distinct decrease through the SIR compression regions. This work demonstrates the success of the Alfvén wave turbulence theory for predicting interplanetary magnetic turbulence levels, while self-consistently reproducing solar wind speeds, densities, and overall temperatures, including at small heliocentric distances and through SIR compression regions.

Hydrodynamic analysis of the upright swimming of seahorse

The seahorse is the only creature in the ocean that can maintain an upright posture while swimming. This paper mainly discusses the hydrodynamic characteristics and the flow field structure of the seahorse when it swims upright. Using a three-dimensional seahorse model, numerical simulations of self-propelled swimming are conducted by establishing the kinematic equations of its dorsal fin. The focus is on elucidating the effects of the undulation frequency and the inclination angle on swimming performance. The results indicate that a higher undulation frequency of the dorsal fin leads to better acceleration performance, or in other words, greater hydrodynamic forces. The inclination angle of the seahorse's body also directly affects its hydrodynamics and the flow field structure. Unlike other fish that swim horizontally, the seahorse generates forward and upward thrust as the flow field simultaneously spreads backward and downward. Since the upright posture makes the forward thrust much smaller than the upward one, the seahorse has low efficiency in forward propulsion when swimming upright. As the inclination angle decreases, the forward thrust gradually increases and exceeds the upward force, which allows for a rapid improvement in the swimming velocity. The simulation findings of this study are consistent with previous experimental observations.

Filamentary Network and Magnetic Field Structures Revealed with BISTRO in the High-mass Star-forming Region NGC 2264: Global Properties and Local Magnetogravitational Configurations

We report 850 μm continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations large program on the James Clerk Maxwell Telescope. These data reveal a well-structured nonuniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30° from north to east. Field strength estimates and a virial analysis of the major clumps indicate that NGC 2264C is globally dominated by gravity, while in 2264D, magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and longitudinal collapsing, driven by the region's global gravity.

Partial Sequence Labeling With Structured Gaussian Processes.

Existing partial sequence labeling models mainly focus on a max-margin framework that fails to provide an uncertainty estimation of the prediction. Furthermore, the unique ground-truth disambiguation strategy employed by these models may include wrong label information for parameter learning. In this article, we propose structured Gaussian processes for partial sequence labeling (SGPPSL), which encodes uncertainty in the prediction and does not need extra effort for model selection and hyperparameter learning. The model employs factor-as-piece approximation that divides the linear-chain graph structure into the set of pieces, which preserves the basic Markov random field structure and effectively avoids handling a large number of candidate output sequences generated by partially annotated data. Then, confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning. Based on the derived lower bound of the variational lower bound of the proposed model, variational parameters and confidence measures are estimated in the framework of alternating optimization. Moreover, a weighted Viterbi algorithm is proposed to incorporate confidence measures to sequence prediction, which considers label ambiguity arose from multiple annotations in the training data and thus helps improve the performance. SGPPSL is evaluated on several sequence labeling tasks and the experimental results show the effectiveness of the proposed model.

First Insights into the Applicability and Importance of Different 3D Magnetic Field Extrapolation Approaches for Studying the Preeruptive Conditions of Solar Active Regions

The three-dimensional (3D) coronal magnetic field has not yet been directly observed. However, for a better understanding and prediction of magnetically driven solar eruptions, 3D models of solar active regions are required. This work aims to provide insight into the significance of different extrapolation models for analyzing the preeruptive conditions of active regions with morphological parameters in 3D. Here, we employed potential field (PF), linear force-free field (LFFF), and nonlinear force-free field (NLFFF) models and a neural network-based method integrating observational data and NLFFF physics (NF2). The 3D coronal magnetic field structure of a “flaring” (AR11166) and “flare-quiet” (AR12645) active region, in terms of their flare productivity, is constructed via the four extrapolation methods. To analyze the evolution of the field, six prediction parameters were employed throughout, from the photosphere up to the base of the lower corona. First, we find that the evolution of the adopted morphological parameters exhibits similarity across the investigated time period when considering the four types of extrapolations. Second, all the parameters exhibited preeruptive conditions not only at the photosphere but also at higher altitudes in the case of active region (AR) 11166, while three out of the six proxies also exhibited preeruptive conditions in the case of AR12645. We conclude that: (i) the combined application of several different precursor parameters is important in the lower solar atmosphere to improve eruption predictions, and (ii) to gain a quick yet reliable insight into the preflare evolution of active regions in 3D, the PF and LFFF are acceptable; however, the NF2 method is likely the more suitable option.

Density distributions, magnetic field structures, and fragmentation in high-mass star formation

Context. The fragmentation of high-mass star-forming regions depends on a variety of physical parameters, including density, the magnetic field, and turbulent gas properties. Aims. We evaluate the importance of the density and magnetic field structures in relation to the fragmentation properties during high-mass star formation. Methods. Observing the large parsec-scale Stokes I millimeter dust continuum emission with the IRAM 30 m telescope and the intermediate-scale (<0.1 pc) polarized submillimeter dust emission with the Submillimeter Array toward a sample of 20 high-mass star-forming regions allows us to quantify the dependence of the fragmentation behavior of these regions on the density and magnetic field structures. Results. Based on the IRAM 30 m data, we infer density distributions n ∝ r−p of the regions with typical power-law slopes p around ~1.5. There is no obvious correlation between the power-law slopes of the density structures on larger clump scales (~1 pc) and the number of fragments on smaller core scales (<0.1 pc). Comparing the large-scale single-dish density profiles to those derived earlier from interferometric observations at smaller spatial scales, we find that the smaller-scale power-law slopes are steeper, typically around ~2.0. The flattening toward larger scales is consistent with the star-forming regions being embedded in larger cloud structures that do not decrease in density away from a particular core. The magnetic fields of several regions appear to be aligned with filamentary structures that lead toward the densest central cores. Furthermore, we find different polarization structures; some regions exhibit central polarization holes, whereas other regions show polarized emission also toward the central peak positions. Nevertheless, the polarized intensities are inversely related to the Stokes I intensities, following roughly a power-law slope of ∝ SI−0.62. We estimate magnetic field strengths between ~0.2 and ~4.5 mG, and we find no clear correlation between magnetic field strength and the fragmentation level of the regions. A comparison of the turbulent to magnetic energies shows that they are of roughly equal importance in this sample. The mass-to-flux ratios range between ~2 and ~7, consistent with collapsing star-forming regions. Conclusions. Finding no clear correlations between the present-day large-scale density structure, the magnetic field strength, and the smaller-scale fragmentation properties of the regions, indicates that the fragmentation of high-mass star-forming regions may not be affected strongly by the initial density profiles and magnetic field properties. However, considering the limited evolutionary range and spatial scales of the presented CORE analysis, future research directions should include density structure analysis of younger regions that better resemble the initial conditions, as well as connecting the observed intermediate-scale magnetic field structure with the larger-scale magnetic fields of the parental molecular clouds.

Magnetic Fields in the Central Molecular Zone Influenced by Feedback and Weakly Correlated with Star Formation

Magnetic fields of molecular clouds in the central molecular zone (CMZ) have been relatively under-observed at sub-parsec resolution. Here, we report JCMT/POL2 observations of polarized dust emission in the CMZ, which reveal magnetic field structures in dense gas at ∼0.5 pc resolution. The 11 molecular clouds in our sample include two in the western part of the CMZ (Sgr C and a farside cloud candidate), four around the Galactic longitude 0 (the 50 km s−1 cloud, CO 0.02−0.02, the Stone, and the Sticks and Straw among the Three Little Pigs), and five along the Dust Ridge (G0.253+0.016, clouds b, c, d, and e/f), for each of which we estimate the magnetic field strength using the angular dispersion function method. The morphologies of magnetic fields in the clouds suggest potential imprints of feedback from expanding H ii regions and young massive star clusters. A moderate correlation between the total viral parameter versus the star formation rate (SFR) and the dense gas fraction of the clouds is found. A weak correlation between the mass-to-flux ratio and the SFR, and a weak anticorrelation between the magnetic field and the dense gas fraction are also found. Comparisons between magnetic fields and other dynamic components in clouds suggest a more dominant role of self-gravity and turbulence in determining the dynamical states of the clouds and affecting star formation at the studied scales.

The Energy-dependent Gamma-Ray Light Curves and Spectra of the Vela Pulsar in the Dissipative Magnetospheres

We study the pulsar energy-dependent γ-ray light curves and spectra from curvature radiation in the dissipative magnetospheres. The dissipative magnetospheres with the combined force-free (FFE) and Aristotelian are computed by a pseudo-spectral method with a high-resolution simulation in the rotating coordinate system, which produces a near-FFE field structure with the dissipative region only near the equatorial current sheet outside the light cylinder. We use the test-particle trajectory method to compute the energy-dependent γ-ray light curves and phase-average and phase-resolved spectra by including both the accelerating electric field and radiation reaction. The predicted energy-dependent γ-ray light curves and spectra are then compared with those of the Vela pulsar observed by Fermi. Our results can generally reproduce the observed trends of the energy-dependent γ-ray light curves and spectra for the Vela pulsar.

The internal Faraday screen of Sagittarius A*

We report on 85−101 GHz light curves of the Galactic Center supermassive black hole, Sagittarius A* (Sgr A*), observed in April 2017 with the Atacama Large Millimeter/submillimeter Array (ALMA). This study of high-cadence full-Stokes data provides new measurements of the fractional linear polarization at a 1−2% level resolved in 4 s time segments, and stringent upper limits on the fractional circular polarization at 0.3%. We compare these findings to ALMA light curves of Sgr A* at 212−230 GHz observed three days later, characterizing a steep depolarization of the source at frequencies below about 150 GHz. We obtain time-dependent rotation measure (RM) values, with the mean RM at 85−101 GHz being a factor of two lower than that at 212−230 GHz. Together with the rapid temporal variability of the RM and its different statistical characteristics in both frequency bands, these results indicate that the Faraday screen in Sgr A* is largely internal, with about half of the Faraday rotation taking place inside the inner 10 gravitational radii, contrary to the common external Faraday screen assumption. We then demonstrate how this observation can be reconciled with theoretical models of radiatively inefficient accretion flows for a reasonable set of physical parameters. Comparisons with numerical general relativistic magnetohydrodynamic simulations suggest that the innermost part of the accretion flow in Sgr A* is much less variable than these models predict; in particular, the observed magnetic field structure appears to be coherent and persistent.

Study on microchannel cavitation phenomena based on experiment and simulation

Cavitation phenomenon is a common fluid mechanics phenomenon. Its special flow limiting structure generates abundant microchannel cavitation in the microchannel, and the existence of these cavitation flow field with complex flow field structure characteristics is formed in the microchannel. In order to better understand the cavitation phenomenon in the microchannel, this paper takes the microchannel as the research object, adopts the method of comparison between experimental research and numerical simulation, changes the cavitation number by adjusting parameters, and analyses the cavitation phenomenon of the microchannel under different working conditions. From the experimental point of view, a micro-channel with rectangular current limiting structure was designed. In order to observe and analyse the microchannel hydraulic cavitation phenomenon, the hydraulic cavitation micro-channel experiment platform was designed and built, which could analyse the cavitation flow pattern of the microchannel. The computational fluid dynamics simulation software Fluent was used to simulate and study the microchannel cavitation phenomenon characteristics by changing the boundary conditions. It is found that the cavitation phenomenon becomes more obvious as the number of cavitations decreases. Finally, the simulation results are compared with the experimental results, and the simulated cavitation flow pattern is in good agreement with the experimental results.

Influence of the suspension gap on the wake characteristics of a 600 km/h superconducting maglev train

Although the suspension gap between a maglev train and its track is narrow, it has a significant impact on the aerodynamic performance of the train. This study investigated the effect of the suspension gap on the wake flow of a superconducting maglev train traveling at a speed of 600 km/h by comparing the aerodynamics and flow field structure of a tail car (TC) with suspension gaps of 8, 10, 12, and 14 cm. The results showed that with increasing suspension gap, the vortex structures in the wake region move closer to the TC, the airflow from the suspension gap inhibits the high-pressure region of the wake, and the pressure and velocity fluctuations significantly increase near the nose tip of the TC. Moreover, the suspension gap was found to have a significant impact on lift and almost no effect on drag. Increasing the suspension gap leads to a balance in the lift between the head car (HC) and the TC; as the gap continues to increase, the lift ratio of the HC and TC rapidly decreases.

Effects of increasing drip irrigation at different maize growth stages on soil microorganisms.

To investigate the effects of different drip irrigation periods on soil microbial communities and functions. Increasing drip irrigation amount at the seedling (S), jointing (J), bell (B), tasseling (T) and grain filling (G) stages of maize were studied using no increase in irrigation amount as control (CK). Principal component analysis was conducted to comprehensively evaluate soil microbial quality following the different drip irrigation treatments. In addition, the characteristics of the community structure and the potential functional composition of soil bacteria and fungi were comparatively analyzed by combining amplicon sequencing and functional prediction methods. The results indicated that MBNT15 was the most important genus for the classification of soil bacterial samples, Saitozyma was the most important genus for the classification of soil fungal samples, and fungi were more important than bacteria for the classification of soil microbial samples. Compared with fungal communities, bacterial communities exhibited high levels of functional diversity. The proportion of metabolism was the highest in the prediction of bacterial primary functions, and carbohydrate metabolism and amino acid metabolism were important functions in the prediction of bacterial secondary functions. BugBase phenotype prediction results showed that soil bacteria under B treatment had a higher number of aerobic bacteria and greater resistance to disease and stress. The J treatment had the highest number of bacteria with biofilm forms, and the J, S, and G treatments contained more potentially pathogenic bacteria but fewer stress-tolerant bacteria compared with the CK treatment. The number of Saprotroph was the largest and the number of Symbiotroph was the least. The relative abundances of Saprotroph, Pathotroph and Symbiotroph were 68.60%~74.33%, 15.76%~20.60% and 9.16%~11.13%, respectively. The findings provide a reference for conserving water resources, improving maize yield, and predicting soil microbial metabolic potential and function by reflecting the richness of the soil microbial community structure in maize fields.

Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

We present a computational approach that incorporates a convolutional neural network (CNN) for detecting internal delamination in a layered 2D plane-strain anisotropic composite structure of transient elastodynamic fields. The two-dimensional spectral element method (SEM) is utilized to simulate the propagation of elastic waves in an orthotropic solid sandwiched by isotropic solids and their interaction with the internal delamination cavity. This work generates training data consisting of input-layer features (i.e., measured wave signals) and output-layer features (i.e., element types, such as void or regular, of all elements in a domain).To accelerate training data generation, we utilize explicit time integration (e.g., the Runge–Kutta scheme) coupled with an SEM wave solver. Applying the level-set method additionally avoids having to perform an expensive re-meshing process for every possible configuration of the delamination cavities during the data-generation phase. The CNN is trained to classify each element as a non-void or void element from the measured wave signals. Clusters of identified void elements reconstruct targeted cavities. Once our neural network is trained using synthetic data, we analyze how effectively the CNN performs on synthetic measurement data. To this end, we use blind test data from a third-party simulator that explicitly models the traction-free boundary of cavities for anisotropic materials without the application of the level-set method.Our numerical examples show that our approach can effectively detect the internal cavities in an anisotropic structure made of aluminum and carbon fiber-reinforced epoxy using the measured elastic waves without any prior information about the cavities’ locations, shapes, and numbers. The presented method can be extended into a more realistic 3D setting and utilized for the nondestructive test of various anisotropic composite structures.

The discourse of American and British political interviews

The aim is to conduct a comparative analysis of the political discourse of Great Britain and the United States, in particular the study of the speech style of the leaders, the context and the role of the media in the formation of public opinion. Methods. The study employs the method of analysis of lexical units, the linguistic method of field structures, and the method of stylistic analysis of markers. Standard methods of mathematical statistics were used, as well as Cronbach’s alpha to check the reliability of the research methods. Conclusions. The study reveals the importance of the media’s role in political discourse, especially in the dissemination of political interviews and their public perception. A comparison of UK and US political discourse reveals both similarities and differences in the communication of political leaders, and helps analyse each country’s response to common challenges and issues. Prospects. Further research could focus on gendered aspects of political speech, including the analysis of the gender’s role in shaping political discourse and identifying gender stereotypes.

Nonlinear modeling and parameter optimization of the pilot stage applied to the deflector-jet servovalve

Due to its good anti-pollution performance and high reliability, the deflector-jet servovalve is widely used in aerospace engineering. However, because the jet effects such as boundary expansion and adsorption are neglected, the model of the pilot stage applied to the deflector-jet servovalve is inaccurate, making it difficult to simulate the performance and optimize the parameters of the deflector-jet servovalve based on the model. Based on the structure of the jet flow field and the hydraulic resistance network bridge, an accurate nonlinear mathematical model of the pilot stage applied to the deflector-jet servovalve was developed, which considers the influence of the jet effect. Based on the new mathematical model, the structural parameters of the pilot stage were optimized with the maximum pressure, maximum flow, and maximum transmission power as the optimization objectives, respectively. Then the design criteria of the structural parameters were given based on the parameter optimization. Finally, the accuracy of the model was verified by three experiments. The comparison between experiments and this new model showed the maximum relative error of the flow between the model and experiment to be about 8.09%, which is more accurate than the traditional models.

Mapping the sustainability branding field: emerging trends and future directions

Purpose This study aims to offer the intellectual structure and dynamics of the sustainability branding field, involving the identification of influential authors and journals, current and emerging themes, theories, methods, contexts and future research directions. Design/methodology/approach The study conducted a bibliometric approach of 1,509 articles retrieved from Scopus to analyze the evolution of the knowledge of sustainability branding and suggest future research. The analysis used various methods such as performance analysis, keyword analysis, cluster analysis and bibliographic coupling. Findings The topics of corporate image, philanthropy and stakeholder pressures were core in the foundation phase. Then rose the topics of sustainable development goals and global supply chains. Green marketing and the new paradigms of circularity, ethical consumerism and hyperconnected societies emerged more recently. Six thematic clusters represent the field’s knowledge structure: (1) corporate branding and reputation, (2) sustainable business development, (3) sustainable branding and ethical consumption, (4) corporate social responsibility, (5) brand equity and green marketing and (6) sustainability branding in hospitality and tourism. Practical implications This paper provides readers with an overview of sustainability branding core themes, key contributions and challenges, which can be used as a toolkit for brand management studies and practice. Originality/value The study’s uniqueness lies in bibliometric analysis (combined with network analysis and science mapping techniques) of the sustainability branding field from the identification and evolution of the thematic clusters to propose future research directions.

Physically based simulation of focusing schlieren imaging for a hypersonic boundary layer flow.

Focusing schlieren systems are more advantageous than conventional schlieren systems in providing a schlieren image with certain spatial discrimination along the light path. The present work employed a hybrid of the optical-transfer matrix and ray-tracing method to faithfully replicate complete physical imaging processes throughout a focusing schlieren optic system. A direct numerical simulation of a hypersonic boundary layer flow was employed to synthesize focusing schlieren images. The influence of various configuration parameters on the properties of focusing schlieren image such as local schlieren structure, brightness, sensitivity, and depth of field were systematically explored. In addition, an approximation method was proposed as a simplified means to facilitate the simulation of a focusing schlieren image.

Machine learning approach for GNSS geodetic velocity estimation

This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the current Global Navigation Satellite System (GNSS) velocity field. To achieve this objective, the analysis utilized the most comprehensive velocity field available for Turkey, where 70% of the GNSS velocities was allocated for training the ML algorithms, while the remaining 30% was used for testing. Contrary to the previous research, the significance of considering the tectonic structure within the study area was emphasized at this point. To determine the tectonic structure of the horizontal velocity field in the region, a preliminary clustering procedure was conducted. Subsequently, distinct ML algorithms were trained using velocity fields associated with different tectonic plates. Moreover, to investigate the impact of the tectonic domain, the entire velocity field was also tested using ML algorithms without considering the tectonic structure. Four different ML algorithms, namely, Gradient Boosting Machines (GBM), LightGBM, Random Forest (RF), and eXtreme Gradient Boosting Machines (XGBoost), were employed to estimate the horizontal velocities (east and north components). The findings imply that incorporating the tectonic structure improved the performance of machine learning predictions, as indicated by the GBM algorithm's decreased root-mean-square error values. In addition, when the tectonic structure was taken into account, the accuracy assessment values for the RF and XGBoost algorithms in the east component decreased significantly. In terms of predicting GNSS velocities, the RF algorithm exhibited the lowest root-mean-square error values compared to other algorithms. The horizontal velocity differences between averages of the reference velocity field and the RF velocity estimates are maximum 0.4 mm/yr.

Optimizing nitrogen application position to change root distribution in soil and regulate maize growth and yield formation in a wide-narrow row cropping system: pot and field experiments.

The wide-and narrow-row cropping technology used for maize has the advantages of protecting cultivated soil and improving the population structure in maize fields. However, the relationship between nitrogen application position and root interactions has not been determined. Through pot and field experiments, we evaluated the effects of two nitrogen application positions ((narrow row nitrogen application (RC) and wide row nitrogen application (RN)) and two nitrogen application regimens ((high nitrogen(HN) and low nitrogen(LN)) on root growth and yield composition of wide-narrow row maize during the flowering and harvest stages. In field experiments, RC increased the biomass, length and surface area of competing roots (narrow-row roots, CR) at the flowering stage. The yield and agronomic efficiency of N(AEN) and partial factor productivity of N(PFPN) were increased by RN compared to RC under HN, However, the AEN under LN was significantly lower; There was no significant effect on maize growth and biomass allocation at the same level of application of N. At the flowering stage, the results of CR and non-competing roots (wide-row roots, NCR) was consistent under pot experiments and the field experiments, and the yield under RN was also higher than that under RC, although the difference was not significant. Furthermore, according to the principal component analysis and correlation analysis, the competing roots were the main factor influencing yield and AEN. In conclusion, our study showed that RN is a useful fertilization method to improve overall productivity. All in all, how roots coordinate neighbors and nitrogen spatial heterogeneity is a complex ecological process, and its trophic behavior deserves further study.

Research on Measurement Technology of Coefficient of Thermal Expansion of Honeycomb Structure

Honeycomb structure is widely used as the main sandwich layer of load-bearing structure in aviation, aerospace and other fields because of its light weight and high strength. Under some service conditions, the deformation of the honeycomb structure affected by high and low temperature will affect the overall mechanical performance of the structure. Therefore, accurate evaluation of the thermal expansion performance of the honeycomb structure becomes the key to structural design. However, the existing measurement methods of thermal expansion coefficient mainly have problems such as the hollow structure cannot be effectively contacted, the area of the grid optical target is limited and difficult to detect. In view of the above conditions, in this paper, a method for measuring the thermal expansion coefficient of honeycomb structure is proposed, and the measurement system is built, and the thermal expansion performance of honeycomb structure is effectively obtained by testing the honeycomb structure. At the same time, the measurement accuracy of the system is verified by using standard test pieces. The experimental results show that the method has high measurement accuracy.