3D Temperature Field Research Articles

Efficient online prediction and correction of 3D combustion temperature field in coal-fired boilers using GDNN

Predicting furnace temperature distribution is vital for coal-fired boiler safety. Existing methods, including finite element calculations and three-dimensional (3D) reconstruction still face limitations. A 3D combustion temperature field prediction method, GDNN, which utilizes offline-computed computational fluid dynamics (CFD) simulation results for online reconstructions of entire boiler, was proposed. GDNN method leverages the knowledge of the temperature field acquired by the base neural network model and Gaussian processes. Furthermore, a temperature field correction method is introduced, which employs intermediate variables of the GDNN model and measured values from temperature sensors to establish a correction model for the entire predicted temperature field. We compared GDNN's effectiveness with four well-established algorithms: Extreme Learning Machine (ELM), Least Absolute Shrinkage and Selection Operator (LASSO), Deep Neural Network(DNN), and Radial Basis Function (RBF) network, by also substituting these algorithms as the base model in our proposed method. The experimental results demonstrate that the proposed prediction method exhibits the highest performance, and the correction method effectively improves the overall results. The optimal parameters for predicting and correcting 3D furnace temperature field results were determined through experimental comparison, and the proposed method was applied to a 350 MW boiler, achieving an error of 2.41%, proving its real-world effectiveness.

Fast reconstruction of indoor temperature field for large-space building based on limited sensors: An experimental study

Accurately knowing the spatiotemporal distribution of indoor environmental parameters is important for indoor thermal comfort adjusting and building energy saving. Due to the significant distributed parameter characteristics of the building thermal environment, this task is difficult, especially for large-space buildings with the features of multi-input and multi-output. For a real large-space building, the reduced-dimensional model of indoor temperature field is rebuilt based on proper orthogonal decomposition (POD) and an offline-online strategy. POD technology is used to extract environmental features from steady / dynamic experimental data. Based on the solved POD modes, the optimal layout of temperature sensors is obtained by a heuristic reasoning method. By combining linear stochastic estimation (LSE) method together with the selected sensors, an offline-online strategy is proposed for rapid reconstruction and prediction of the indoor temperature fields. Digital interpolation is applied for soft sensing outside the original sampling locations. An experimental study for a large-space cafeteria with sixteen indoor air conditioning units is conducted. A total of 72 sensors are placed to collect indoor 3D temperature field data offline. Results show that using only six sensors of them, the large-space indoor temperature field could be accurately estimated online with the steady-state error of 0.35 (average RMSE) and dynamic-state errors of 0.0525 / 0.0540 (heating / cooling, average RMSE).

Arctic stratosphere changes in the 21st century in the Earth system model SOCOLv4

Two ensemble simulations of a new Earth system model (ESM) SOCOLv4 (SOlar Climate Ozone Links, version 4) for the period from 2015 to 2099 under moderate (SSP2-4.5) and severe (SSP5-8.5) scenarios of greenhouse gas (GHG) emission growth were analyzed to investigate changes in key dynamical processes relevant for Arctic stratospheric ozone. The model shows a 5–10 K cooling and 5%–20% humidity increase in the Arctic lower–upper stratosphere in March (when the most considerable ozone depletion may occur) between 2080–2099 and 2015–2034. The minimal temperature in the lower polar stratosphere in March, which defines the strength of ozone depletion, appears when the zonal mean meridional heat flux in the lower stratosphere in the preceding January–February is the lowest. In the late 21st century, the strengthening of the zonal mean meridional heat flux with a maximum of up to 20 K m/s (∼25%) in the upper stratosphere close to 70°N in January–February is obtained in the moderate scenario of GHG emission, while only a slight increase in this parameter over 50 N–60 N with the maximum up to 5 K m/s in the upper stratosphere and a decrease with the comparable values over the high latitudes is revealed in the severe GHG emission scenario. Although the model simulations confirm the expected ozone layer recovery, particularly total ozone minimum values inside the Arctic polar cap in March throughout the 21st century are characterized by a positive trend in both scenarios, the large-scale negative ozone anomalies in March up to −80 DU–100 DU, comparable to the second lowest ones observed in March 2011 but weaker than record values in March 2020, are possible in the Arctic until the late 21st century. The volume of low stratospheric air with temperatures below the solid nitric acid trihydrate polar stratospheric cloud (PSC NAT) formation threshold is reconstructed from 3D potential vorticity and temperature fields inside the stratospheric polar vortex. A significant positive trend is shown in this parameter in March in the SSP5-8.5 scenario. Furthermore, according to the model data, an increase in the polar vortex isolation throughout the 21st century indicates its possible strengthening in the lower stratosphere. Positive trends of the surface area density (SAD) of PSC NAT particles in March in the lower Arctic stratosphere over the period of 2015–2099 are significant in the severe GHG emission scenario. The polar vortex longitudinal shift toward northern Eurasia is expected in the lower stratosphere in the late 21st century in both scenarios. The statistically significant long-term stratospheric sulfuric acid aerosol trend in March is expected only in the SSP5.8-5 scenario, most probably due to cooler stratosphere and stronger Brewer–Dobson circulation intensification. Both scenarios predict an increase in the residual meridional circulation (RMC) in March by the end of the 21st century. In some regions of the stratosphere, the RMC enhancement under the severe GHG scenario can exceed 20%.

MATHEMATICAL MODEL FOR RISK ASSESSMENT OF THERMAL DAMAGE IN CASE OF FIRE AT THE FACILITIES OF THE FUEL AND ENERGY COMPLEX

Problem statement. The task of prediction for thermal air pollution and assessing the risk of thermal damage to people during a fire at an industrial site is considered. The task is to calculate 3D temperature fields during a fire and, based on this, to assess the risk of thermal damage to people. The purpose of the article. Development of a 3D numerical model for calculating thermal air pollution and assessing the risk of thermal damage to workers at an industrial site in the case of a fire. Methodology. A three-dimensional energy equation was used to model the process of thermal air pollution at an industrial site in the case of a fire. A three-dimensional equation for the velocity potential is used to calculate the air flow velocity field at the industrial site. For the numerical integration of the equation for the velocity potential, the splitting method is used. For the numerical integration of the three-dimensional energy equation, it is split at the differential level into two equations. The first equation describes the spread of temperature due to the movement of air masses. The second equation describes the temperature distribution due to thermal conductivity. For the numerical integration of the first equation, a variable-triangular difference splitting scheme is used. An explicit difference scheme is used for the numerical integration of the second equation. Scientific novelty. A 3D numerical model was created, which allows to quickly calculate the dynamics of the formation of thermal air pollution areas at the industrial site and, based on this information, to predict the risk of thermal damage to people in the work zones at the industrial site. The model is based on the numerical integration of the aerodynamic and heat transfer equations. The model allows to quickly calculate the dynamics of the thermal zones’ formation at the industrial site in the case of a fire. Practical value. The developed model makes it possible to predict the dynamics of changes in temperature fields in the air that occur during a fire at an industrial site. The numerical model can be used to determine zones of intense thermal pollution and assess the risk of thermal damage to workers. Conclusions. On the basis of the developed 3D numerical model, a code was created for conducting a computational experiment. The developed code allows to quickly calculate the dynamics of the formation of thermal air pollution areas at an industrial site during a fire. Based on the received information, the risk of thermal damage to workers is assessed. The results of the computational experiment are presented.

Online temperature-monitoring technology for grain storage: a three-dimensional visualization method based on an adaptive neighborhood clustering algorithm.

Post-harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three-dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA-based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile. The experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA-based visualization method for grain pile temperatures has a shorter rendering time and better visual effects. This research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

Optimizing the trench shaped film cooling design

In this numerical study an optimized trench film cooling design is determined using a Bayesian algorithm and neural network trained RANS model. Three objective functions were considered, the area-averaged film cooling efficiency, spatial standard deviation of film cooling efficiency and hot gas ingestion into the trench. Nine geometrical design parameters were varied to allow for a 3D trench shape and to find an optimal trench design based on the initial parametrization of the trench. Jet-engine like inflow boundary conditions with respect to turbulence intensity and length scales were applied. The investigated momentum ratios (I) were 1 and 8 at a main flow Reynolds number (ReD) of 2500. For each design the steady state Reynolds Averaged Navier-Stokes (RANS) equations were solved using the commercial Computational Fluid Dynamics (CFD) code Ansys Fluent V2022 R1. The turbulence model coefficients of the generalized k−ω (GEKO) model were tuned to approximate the time-averaged 3D temperature field from a predictive Large Eddy Simulation (LES) and trained by a neural network to improve the prediction capability. The tuned GEKO model shows improved agreement with experimental data of a literature case compared to the standard GEKO model. With this tuned RANS model optimized trench designs are found and validated by additional LES’s. The optimized designs include angled side walls to improve former trench designs, particularly in mitigating hot gas entrainment into the trench, which could be omitted almost entirely.

Real-time prediction and adaptive adjustment of continuous casting based on deep learning

Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process.

3D thermal mapping of smoldering coal gangue pile fires using airborne thermal infrared data

Smoldering coal gangue pile fires pollute the air, soil and groundwater severely. Physical and chemical explosions between water and fiery coal gangue may occur, resulting in a large number of casualties. Recognizing and delineating the scope of coal gangue pile fires is a precondition and top priority for firefighting efforts. The present study employs a 3D thermal imaging methodology based on Unmanned Aerial Vehicle (UAV) remote sensing data. Thermal infrared oblique photogrammetry is used to collect overlap-high thermal infrared images of smoldering fires. A batch preprocessing software is developed to standardize the temperature range, temperature distribution mode and color scale of all thermal infrared images, improving feature point identification accuracy. The elaborate delineation and accurate identification of 3D temperature field are realized by feature point matching and texture mapping. The method was used to build the 3D temperature model and visualize the scope of the coal gangue pile fires in the 1360 bench, viewing bench and belt corridor of the Anjialing Open-pit Mine in China. The 3D thermal models accurately characterize the distribution of the smoldering fires, providing firm support and guidance for the firefighting efforts.

Preliminary Study on Reconstruction of Building Thermal Field Based on Iterative Algorithm Acoustic CT

Real-time acquisition and visualization of temperature anomalies in building spaces and 3D temperature field data during fires are of vital importance for fire danger warnings, early rescue operations, evacuation commands, and subsequent fire accident investigations. Taking into account the non-contact, global (planar and spatial), and high efficiency advantages of acoustic CT temperature measurement technology, this study involved the conducting of exploratory preliminary research in order to provide new ideas for the real-time global perception of information on building fires. The detailed research objective was as follows: obtain the temperature data at any time of a fire based on Fire Dynamics Simulator (FDS) and fit them to form the base temperature distribution diagram at that time. The large ill-conditioned matrix equation of acoustic flight under the scheme of multi-grid division was then constructed. The discrete temperature data of each grid in the building space was obtained by solving the matrix equation based on algebraic reconstruction algorithm (ART) and joint algebraic reconstruction algorithm (SART). The three-dimensional temperature field reconstruction of building space was realized by the interpolation of discrete temperature data. The reconstruction effect of each scheme was evaluated through the error analysis between the reconstruction data and the basic data. The results show that the real-time reconstruction of a 3D temperature field of a building thermal field can be realized based on acoustic CT temperature measurement technology, and the reconstruction algorithm and grid division scheme have a significant control effect on the reconstruction effect.

Data recovery of 2D lifetime-based phosphor thermometry using deep neural networks

Thermographic phosphor (TP) thermometry has been widely used as one of the newly developed non-contact surface temperature measurement methods. However, temperature information is frequently lost locally because chemical bonding coatings are easily damaged during the measurement. This limits its application, such as in the case of jet impact, high-speed motion, high vibration, etc. We proposed to use of deep neural networks (DNNs) as a tool for recovering lost data. In this study, we captured the dynamic two-dimensional (2D) temperature field of jet impingement cooling a high temperature plate by TP thermometry. Different parts of the temperature information, including jet impact area and non-impact area, were then removed to assume the coating was damaged. A prediction model was established by the DNNs using the 2D space and time coordinates as the input dataset and using the temperature information corresponding to the coordinates as the output dataset. The removed temperature information was then recovered using the developed regression model and compared with the raw temperature field to evaluate predicted results. The results showed that the removed data can be successfully recovered using the established DNN prediction model, in which the predicted accuracy was greater than 94.94%. While in the central and the boundary area of the jet, the model has a relatively poor performance, which is mainly due to the large temperature gradients. These results indicate that the established DNN model can be used to recover the lost temperature information but it is limited for applications in regions with drastic temperature changes.

A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition

A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition

Refined 3D Solar Temperature Field and Effect Simulation of Ultra-High Steel Bridge Pylon

In order to accurately calculate the sunshine-induced temperature effect of an ultra-high bridge pylon, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine shadow recognition method and pylon-height-related convection modification method. A suspension bridge H-shaped pylon with a height of 280 m was taken as an example, and the temperature field and corresponding thermal stress and deformation were calculated under typical meteorological conditions in spring, summer, autumn, and winter. The results show that the maximum temperature differences between the outer surfaces of the pylon can reach 19 °C and 16 °C for north–south walls and east–west walls, respectively, and exceed the recommended value of ±5 °C in the Chinese Specification. The maximum displacements can reach 370 mm and 110 mm at the top of the bridge pylon in the longitudinal and transverse directions of the bridge, respectively. After the modification of the convective coefficient of the outer surfaces with different wind speeds at different pylon heights, the temperature gradually decreased from the top to the bottom of the pylon, with a temperature difference of 8 °C. The significant influence of the sunshine shadow was shown on the temperature field and temperature effect of the bridge pylon. By considering the shadow effect, the maximum temperature difference can reach 12 °C between adjacent sunlit and shaded areas and can reach 14 °C between two pylon columns. A significant mutation of thermal stress existed in the shaded area, and the maximum stress could be reduced by 13 MPa compared with the adjacent sunlit area. Obvious asynchronous deformation was shown between two pylon columns, and the maximum asynchronous displacement at the top of the pylon can reach 18 mm and 45 mm in the longitudinal and transverse directions, respectively.

Design analysis of a printed circuit heat exchanger for HTGR using a 3D finite elements model

The intermediate heat exchanger (IHX) is a critical component in most of the High Temperature Gas-cooled Reactor plant layouts. The printed circuit heat exchanger (PCHE) has been studied as an advantageous candidate for this application last years. The extreme operating conditions of the IHX and the higher structural integrity of the PCHE over other heat exchanger technologies have motivated these studies. In this work, the structural assessment of a zigzag channel PCHE under IHX conditions was carried out by means of computational fluid dynamics (CFD) and Finite Element Analysis (FEA). The ASME Boiling and Pressure Vessel Code, Sections III and VIII, were applied to evaluate the safety operation of the PCHE. The temperature field in steady state was obtained using a 3D CFD model. Then it was used as thermal load to calculate thermal stress using a FEA model. The influence of the zigzag angle and the bend radius on the thermal and mechanical stresses was analyzed. Thus, the values studied for the zigzag angle were 0° (straight), 15°, 30 and 45°, while for the bend radius were 0.5 mm, 2 mm, 4 mm and 6 mm. The importance of the 3D temperature field to calculate thermal stress in a zigzag channel was evidenced due to the appearance of local temperature differences in the channel cross-sections. The change in flow direction created areas of higher local temperatures where the flow lines change their directions impacting the channel wall. A significant thermal stress concentration was found in these areas, and this effect intensified with increasing zigzag angle. The bend radius was decisive to reduce the thermal stress concentration when the zigzag angle was higher than 15°.

Industrial Boiler Intelligent Monitoring System Based on LabVIEW

As a kind of traditional heating and power equipment, the industrial boiler is more and more applied in industrial production and life. However, due to the uneven heating of the boiler, the process of using the boiler would result in boiler leakage accidents and cause burns to workers. Given the above situation, the intelligent control system of the industrial boiler is studied in this paper to achieve safe and stable operation of the boiler. This design uses the distributed thermocouple temperature measurement method to design the boiler safety warning system based on the thermocouple sensor collaborative monitoring. Multiple sensors are used to cooperatively monitor and collect boiler operating signals. COMSOL model is used to realize 3D temperature field simulation and reconstruction inside the furnace lining, and the results are presented as early warning results through LabVIEW upper computer interface, which solves the shortcomings of low acquisition accuracy, narrow measurement accuracy, and single measurement method of the traditional contact measurement method. The practice shows that the system has the advantages of low cost, convenient installation and maintenance, easy expansion, high reliability, and strong anti-interference.

Application of 4D two-colour LIF to explore the temperature field of laterally confined turbulent Rayleigh–Bénard convection

Recent studies show the significant effect of the third dimension and flow unsteadiness of laterally confined Rayleigh–Bénard convection (RBC). However, there are limited studies investigating 4D flow properties. The application of 4D two-colour laser-induced fluorescence to explore a laterally confined RBC at high Rayleigh number of $$Ra=9.9\times {10}^{7}$$ and Prandtl number of $$Pr=6.1$$ is investigated using a scanning laser system. A two-colour, two dyes approach was employed to resolve the laser sheet intensity variations due to refractive index variations caused mainly by the generation of thermal plumes. Two temperature-sensitive fluorescent dyes with opposite sensitivities were used to enhance the overall temperature sensitivity to 7.3%/°C. Details of the experimental procedure and optical system employed to reach such a high sensitivity are demonstrated. From the whole field temperature distribution, the dimensionless heat transfer coefficient, the Nusselt number, and its evolution were calculated for both hot and cold boundaries. Temperature field and Nusselt number obtained from 3D and 2D fields are reported to compare the results for these two scenarios. Thermal plumes were found to have a conical shape in the laterally confined RBC compared to the conventional mushroom shape. From visualization of the time-resolved 3D temperature field and 2D distribution of the Nusselt number, it was also found that only by volumetric measurement, temporal and spatial variations of the temperature and heat transfer can be evaluated. This shows that to evaluate the classic and ultimate theories, volumetric measurement is required for a coherent understanding of the physics.

Three-dimensional temperature field inversion calculation based on an artificial intelligence algorithm

An increasing number of practical problems in heat transfer area are attributed to inverse heat transfer problems (IHTPs). One of the typical applications of the inverse problem is the prediction of the temperature field of an object from discrete temperature measurements of the surface. The study combines machine learning algorithms with numerical heat transfer methods to inversely predict the heat transfer boundary conditions of a hexahedral geometry from a finite number of discrete temperature measurements on its surface and then calculate the overall temperature field distribution. For the implementation of the numerical heat transfer forward problem, we first complete the coding of the finite element program to generate a training dataset for the inverse calculation by batch inputting the predefined boundary conditions. The inverse problem is modelled by constructing a neural network (NN) approach. The model is trained by calling data from the above dataset. The discrete temperature data are brought into the trained neural network for temperature field prediction. The results are tested for accuracy and generalisability. Finally, by comparing different hyperparameters and different training methods, a method of improving the efficiency and accuracy of the reconstruction results is proposed. The inversion calculation error is finally controlled to less than 0.1. In addition, the model implements the validation of commercial software simulation data and application to aero-engine turbine blades and vapour chambers. The approach can be extended as a generalised 3D temperature field reconstruction method.

Multidecadal and climatological surface current simulations for the southwestern Indian Ocean at 1∕50° resolution

Abstract. The Western INDian Ocean Simulation (WINDS) is a regional configuration of the Coastal and Regional Ocean Community Model (CROCO) for the southwestern Indian Ocean. WINDS has a horizontal resolution of 1/50∘ (∼2 km) and spans a latitudinal range of 23.5∘ S–0∘ N and a longitudinal range from the East African coast to 77.5∘ E. We ran two experiments using the WINDS configuration: WINDS-M, a full 28-year multidecadal run (1993–2020); and WINDS-C, a 10-year climatological control run with monthly climatological forcing. WINDS was primarily run for buoyant Lagrangian particle tracking applications, and horizontal surface velocities are output at a temporal resolution of 30 min. Other surface fields are output daily, and the full 3D temperature, salinity, and velocity fields are output every 5 d. We demonstrate that WINDS successfully manages to reproduce surface temperature, salinity, currents, and tides in the southwestern Indian Ocean, and it is therefore appropriate for use in regional marine dispersal studies for buoyant particles or other applications using high-resolution surface ocean properties.

Data-driven model reduction for fast temperature prediction in a multi-variable data center

With the rapid development of digital economy, the number of data centers and their capacity have been increasing sharply, and data center energy consumption becomes a whole-society concern. Computational fluid dynamics (CFD) is currently widely used to obtain the thermal fields inside air-cooled data centers to enable design improvements and optimize the airflow organization. However, a CFD simulation needs a lot of time which can not be accepted for real-time operation. In the present study, a design tool called pairwise independent combinatorial testing (PICT) is applied to optimize the simulation conditions and to maximize the amount of useful information obtained with the minimum number of numerical tests. Based on the snapshots, the proper orthogonal decomposition(POD) method combined with the multivariate adaptive regression splines (MARS) method, is proposed and used in a real row-level data center of 199 independent variables. Under design conditions, POD-MARS predictions are in good agreement with CFD simulations with the average mean relative error for 20 tested cases being ∼0.01%. For another 20 randomized cases under off-design conditions, the average mean relative error is 6.45%, the corresponding mean absolute error is 1.89 °C and on average there is 92.36% area of the total three-dimensional temperature field where the relative error doesn't exceed 15%. The POD-MARS computation takes only 30s to obtain a 3D temperature field for the same test case which is ∼240 times faster than CFD simulation on the same desktop computer.

Three-dimensional temperature field measurement method based on light field colorimetric thermometry

This paper proposes a novel method for three-dimensional (3D) temperature measurement using light field colorimetric thermometry, aiming to overcome the challenges associated with the intricate system structure and the limited availability of 3D information in traditional radiation temperature measurement methods. Firstly, the correlation between corresponding image points and the positions of 3D object in the light field image system is analyzed using the ray tracing method. The 3D position acquisition model and the light field colorimetric thermometry model are established, enabling simultaneous acquisition of the spatial coordinates and radiometric information of the 3D object. Then, the light field camera radiation calibration experiment was conducted, and the 3D temperature field will be obtained by employing colorimetric thermometry for each corresponding image point of the same object point. Finally, the experiment employed a light field camera for temperature measurement and reconstruction of candle flames. The accuracy of the temperature measurement is 3.31%, thus confirming the feasibility and effectiveness of the proposed method.

Flame and emission characteristics from NH3/CH4 combustion under ultrasonic excitation

As a high energy density hydrogen-rich carrier, ammonia (NH3) is a highly promising carbon-free fuel. The large-scale industrial application of NH3 is limited by its low reactivity and high NOx emission. In this work, the flame and emission characteristics of ammonia/methane (NH3/CH4) non-premixed combustion were investigated under ultrasonic excitation. An experimental system was designed and built, including non-premixed combustion system, loading ultrasonic system, deflectionmography temperature measurement system and flue gas measurement system. Combustion and measurement experiments at different ultrasonic frequencies and NH3/CH4 blending ratios were carried out. Flame images and flue gas species concentrations under ultrasonic excitation were acquired. The 3-D temperature field was reconstructed. The influence of ultrasonic excitation at different frequencies on flame characteristics, flame temperature field and emission characteristics of the combustion process was analysed. The mechanism of NH3/CH4 combustion enhancement and emission reduction was revealed when the flame was ex-cited by ultrasonic waves. Results showed that part of the hydrocarbon fuels was replaced by NH3 to reduce CO2 emission. The height and color of the NH3/CH4 flame were changed and the high temperature area of the flame gradually expanded as ultrasonic acted on the flame. As ultrasonic frequency increased, the emission concentrations of unburned CH4, unburned NH3, and NO decreased significantly. The flame was ex-cited by ultrasonic waves, which reduced its local equivalent ratio, improved combustion efficiency and suppressed NOx generation.