A method for detecting the explosive capacity of energetic materials based on small amounts of gaseous products.

Abstract

This invention belongs to the field of quantitative detection of explosive capacity of energetic materials, and relates to a method for detecting the explosive capacity of energetic materials based on small amounts of explosive gas products. Specifically, it relates to a novel method for obtaining the explosive capacity of energetic materials based on small amounts of explosive gas products and utilizing time-correlated laser-induced breakdown spectroscopy combined with a high-speed schlieren imaging system. The method obtains the kinetic process of small amounts of explosive gas products and the plasma spectrum of the gas products at specific times through time-correlated laser-induced breakdown spectroscopy combined with a high-speed schlieren imaging system. Using principal component analysis-partial least squares (PCA-PLS) combined with a small-sample modeling algorithm, a high-precision quantitative analysis model of the gas product spectral data and the macroscopic explosive capacity of energetic materials is established. The method described in this invention provides a novel, rapid, and safe method for assessing the explosive capacity of explosives with small amounts of explosives.

Description

Technical Field

[0001] This invention belongs to the field of quantitative detection of explosive capacity of energetic materials, and relates to a new method for detecting explosive capacity of energetic materials based on a small amount of gaseous products. Specifically, it relates to a method for obtaining the explosive capacity of energetic materials based on a small amount of gaseous products and using time-correlated laser-induced breakdown spectroscopy combined with a high-speed schlieren system. Background Technology

[0002] The explosive capacity of explosives is one of the key parameters for measuring their energy release efficiency and explosive performance. Traditional explosive capacity testing methods typically require gram-level explosives and complex procedures involving specialized instruments under harsh conditions. Furthermore, for the testing of the explosive capacity of novel explosives, which are mainly synthesized in laboratories, production volumes are small and safety cannot be guaranteed. Achieving safe and rapid explosive capacity measurement based on traditional testing methods has always been a challenge. Developing new explosive capacity measurement methods based on advanced technologies, and establishing and improving diversified, safe, rapid, accurate, and quantitative analysis methods applicable to the explosive capacity of explosives, are crucial for the research and development of new explosives, the improvement of the performance of existing explosives, and safe production. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a novel method for obtaining the explosive capacity of energetic materials based on minute amounts of gaseous products, and a burst capacity testing system combining time-correlated laser-induced breakdown spectroscopy (TLS) with high-speed schlieren imaging. The plasma spectrum of minute amounts of gaseous products at a specific time is obtained by combining TLS with a high-speed schlieren imaging system. A high-precision quantitative analysis model of the gaseous product spectral data and the macroscopic burst capacity of the energetic material is established using principal component analysis-partial least squares (PCA-PLS) combined with a small-sample modeling algorithm.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for detecting the explosion capacity of energetic materials based on small amounts of gaseous products, the method mainly includes the following steps:

[0006] (1) Select t energetic material samples with known explosion capacity parameters as calibration samples. Place 10mg-15mg energetic material samples into a closed experimental chamber. To ensure the stability of the experimental environment, compressed air is introduced into the experimental chamber by air exhaust method. The chamber is kept at a pressure 10kPa higher than 1 standard atmosphere to avoid the entry of external environmental gases affecting the experiment.

[0007] (2) A pulsed laser focused by a plano-convex lens is applied to the surface of the energetic material sample as an excitation laser to induce the energetic material to produce gaseous products. The energy of the excitation laser is 430 mJ. The dynamic process of the gaseous products is obtained by a high-speed schlieren system to determine the spatial position and time of the gaseous products. Another pulsed laser focused by a plano-convex lens is used as a probe laser to obtain the plasma spectrum of the gaseous products. The energy of the probe laser is 110 mJ. Each sample is tested n times and the plasma spectrum is collected n times.

[0008] Optionally, t≥8; the time for introducing compressed air into the 300ml experimental chamber is ≥30s, and the flow rate is 5L / min.

[0009] Optionally, the spatial position of the gaseous product is 14 mm away from the sample surface, 210 μs after laser loading of the sample; n ≥ 3.

[0010] (3) Set the spectral intensity range to in σ represents the average spectral intensity, and σ represents the standard deviation of the spectral intensity. Each spectrum whose average intensity falls within a defined range is considered a valid spectrum. The sample size is expanded using a small-sample augmentation algorithm. This method treats each valid gaseous product spectrum as a sample and uses a window-shifting smoothing method to subtract the spectral background signal. The main operational steps are as follows:

[0011] Select window size: Determine an appropriate window size that will be used for smoothing; the size needs to be selected based on the characteristics of the spectral data.

[0012] Initialize window position: Place the window at the beginning of the spectral data sequence;

[0013] Calculate the minimum value within the window: Calculate the minimum value of all data points within the window;

[0014] Replace original data points: Replace the values ​​of the original data points in the window with the calculated minimum value;

[0015] Window sliding: Slide the window along the data sequence by one unit and recalculate the minimum value of all data points within the window;

[0016] Repeat the calculation and replacement: Continue sliding the window and repeat the above calculation and replacement steps until the entire spectral data sequence is covered;

[0017] Polynomial fitting: The minimum value obtained in each window is subjected to polynomial fitting to obtain the entire spectral background.

[0018] Finally, the characteristic spectral line of each channel is divided by the integrated intensity of the background signal of the corresponding channel, and the spectrum is normalized using the channel-specific normalization method.

[0019] (4) The sample dataset was randomly divided into training and test sets. Preprocessed and standardized gas product spectra were used as input data. A quantitative analysis model of explosion capacity was established using the PCA-PLS method. The number of principal components in the modeling process was determined through five-fold cross-validation, and the coefficient of determination R was used to determine the modeling parameters. 2 The maximum relative error (MRE), average relative error (ARE), and root mean square error (RMSE) were used to evaluate the quantitative analysis model of burst capacity.

[0020] (5) For energetic material samples with unknown explosion capacity parameters, the gas product spectra at multiple optimized spatial locations and times are obtained using a time-correlated laser-induced spectroscopy system. After the spectral screening and preprocessing in step (3), the spectra are input into the explosion capacity quantitative model to obtain the explosion capacity parameters of the unknown sample.

[0021] It should be noted that this invention acquires the spatial location and time of gaseous products using a high-speed schlieren imaging system, and obtains the plasma spectrum of gaseous products at a specific moment using time-correlated laser-induced breakdown spectroscopy. A high-precision quantitative analysis model of the gaseous product spectral data and the macroscopic burst capacity of energetic materials is established using principal component analysis-partial least squares (PCA-PLS) combined with a small-sample modeling algorithm.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] This invention utilizes laser-induced breakdown spectroscopy to obtain the plasma spectrum of gaseous products at a specific moment. The corresponding characteristic spectra in the plasma spectrum can reflect the amount of different components in the gaseous products, providing a theoretical basis for determining the explosive capacity of energetic materials. This invention also utilizes principal component analysis-partial least squares (PCA-PLS) to extract key information related to explosive capacity from complex spectral data, establishing a high-precision quantitative analysis model of explosive capacity and gaseous product spectra. Specifically, it provides a novel, rapid, and safe method for quantitative analysis of the explosive capacity of energetic materials based on minute amounts of gaseous products. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 This is a diagram of a time-correlated laser-induced breakdown spectrum combined with a high-speed schlieren imaging system.

[0026] Figure 2This is a schematic diagram of the connection of an adjustable ambient gas chamber.

[0027] Figure 3 Schlieren images of different energetic materials at 210 μs.

[0028] Figure 4 The images show the spectra of gaseous products from different energetic materials.

[0029] Figure 5 This is a quantitative model diagram of the explosion capacity. Detailed Implementation

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] The term "embodiment" used herein, as an example, is not necessarily to be construed as superior to or better than other embodiments. Performance testing in the embodiments of this application, unless otherwise specified, employs conventional testing methods in the art. It should be understood that the terminology used in this application is merely for describing particular implementations and is not intended to limit the scope of this disclosure.

[0032] Unless otherwise stated, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; other experimental methods and technical means not specifically mentioned herein refer to experimental methods and technical means commonly used by one of ordinary skill in the art.

[0033] In the description of this invention, it should be understood that the terms "middle", "upper", "lower", "rise", "fall", "vertical", "surface", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0034] To better illustrate the content of this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented even without certain specific details. In the embodiments, some methods, means, instruments, and devices well-known to those skilled in the art are not described in detail in order to highlight the main points of this application.

[0035] Without conflict, the technical features disclosed in the embodiments of this application can be combined arbitrarily, and the resulting technical solution belongs to the content disclosed in the embodiments of this application.

[0036] This invention discloses a method for obtaining the explosive capacity of energetic materials based on small amounts of gaseous products and by using time-correlated laser-induced breakdown spectroscopy combined with a high-speed schlieren system.

[0037] To better understand the present invention, the following embodiments are provided for further detailed description of the present invention, but they should not be construed as limiting the present invention. Any non-essential improvements and adjustments made by those skilled in the art based on the above-described invention are also considered to fall within the protection scope of the present invention.

[0038] This invention uses eight explosive samples with known explosive capacity parameters as calibration samples. The values ​​of each explosive capacity parameter have been obtained by traditional testing methods, and their explosive capacity test values ​​are shown in Table 1.

[0039] Table 1. Explosive volume and oxygen balance values ​​of single-element explosive samples.

[0040]

[0041] (1) Place 10mg-15mg of energetic material sample into a closed experimental chamber. To ensure the stability of the experimental environment, compressed air is introduced into the experimental chamber for 30s by air exhaust method, with a flow rate of 5L / min. The chamber is kept at a pressure 10kPa higher than 1 standard atmosphere to avoid the entry of external environmental gases affecting the experiment.

[0042] (2) The testing system used is as follows: Figure 1 As shown, a pulsed laser beam focused by a plano-convex lens is applied to the surface of an energetic material sample as the excitation laser, inducing the generation of gaseous products. The energy of the excitation laser is 430 mJ. A high-speed camera is set to 200,000 frames / second, and the kinetics of the gaseous products are acquired using a high-speed schlieren image system (Xinyu Zhang, An Li, Ruibin Liu, et al. "Volume of detonation determination based on gaseous products of energetic materials by time resolved LIPS combined with schlieren image," Opt. Express 32, 24877-24888 (2024)) to determine the spatial location and time of the gaseous products. Figure 3As shown, the gaseous product located 14 mm from the sample surface at 210 μs was selected as the representative gaseous product. A separate pulsed laser, focused by a plano-convex lens, was used as the probe laser to acquire the plasma spectrum of the gaseous product; the probe laser energy was 110 mJ. Each sample was tested three times, and three plasma spectra were collected.

[0043] (3) Set the spectral intensity range to in σ represents the average spectral intensity, and σ represents the standard deviation of the spectral intensity. Spectra with average intensities within a defined range are considered valid spectra. Figure 4 As shown. Then, the sample size was expanded using a small-sample augmentation algorithm. This method treats each valid gaseous product spectrum as a sample, resulting in 16 samples with true values ​​for the explosion capacity parameter. The spectral background signal was then subtracted using a window-shifting smoothing method. The main steps are as follows:

[0044] Select window size: Determine an appropriate window size that will be used for smoothing; the size needs to be selected based on the characteristics of the spectral data.

[0045] Initialize window position: Place the window at the beginning of the spectral data sequence;

[0046] Calculate the minimum value within the window: Calculate the minimum value of all data points within the window;

[0047] Replace original data points: Replace the values ​​of the original data points in the window with the calculated minimum value;

[0048] Window sliding: Slide the window along the data sequence by one unit and recalculate the minimum value of all data points within the window;

[0049] Repeat the calculation and replacement: Continue sliding the window and repeat the above calculation and replacement steps until the entire spectral data sequence is covered;

[0050] Polynomial fitting: The minimum value obtained in each window is subjected to polynomial fitting to obtain the entire spectral background.

[0051] Finally, the characteristic spectral line of each channel is divided by the integrated intensity of the background signal of the corresponding channel, and the spectrum is normalized using the channel-specific normalization method.

[0052] (4) The sample dataset is randomly divided into training and test sets. Preprocessed and standardized gas product spectra are used as input data, and a quantitative analysis model for explosion capacity is established using the PCA-PLS method, such as... Figure 5 As shown. Five-fold cross-validation was used to determine the number of principal components in the modeling process. The coefficient of determination R0 was used... 2The maximum relative error (MRE), mean relative error (ARE), and root mean square error (RMSE) were used to evaluate the quantitative analysis model of the burst capacity. The results are shown in Table 2. In Table 2, MREC, AREC, and RMSEC are the evaluation metrics for the training set, while MRET, ARET, and RMSET represent the evaluation metrics for the test set.

[0053] Table 2 Evaluation Indicators for Capacity Explosion Model

[0054]

[0055] (5) For energetic material samples with unknown explosion capacity parameters, gas product spectra at multiple optimized spatial locations and times were obtained using a time-correlated laser-induced spectroscopy system. After spectral screening and preprocessing in step (3), the spectra were input into the explosion capacity quantitative model to obtain the explosion capacity parameters of the unknown samples. The results of the two blind tests are shown in Table 3.

[0056] Table 3 Blind Test Data Results

[0057]

[0058] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for obtaining the explosive capacity of energetic materials based on a small amount of gaseous products, characterized in that, Includes the following steps: (1) Select t kinds of energetic material samples with known explosion capacity parameters as calibration samples. Put 10mg-15mg of energetic material samples into a closed experimental chamber. To ensure the stability of the experimental environment, compressed air is introduced into the experimental chamber and the chamber is kept at a pressure 10kPa higher than 1 standard atmosphere to avoid the entry of external environmental gases affecting the experiment. (2) A pulsed laser focused by a plano-convex lens is applied to the surface of the energetic material sample as an excitation laser to induce the generation of gaseous products. The energy of the excitation laser is 430 mJ. The dynamic process of the gaseous products is obtained by a high-speed schlieren system to determine the spatial position and time of the gaseous products. Another pulsed laser focused by a plano-convex lens is used as a probe laser to obtain the plasma spectrum of the gaseous products. The energy of the probe laser is 110 mJ. Each sample is tested n times and the plasma spectrum is collected n times. (3) Set the spectral intensity range to ,in Represents the average value of spectral intensity. The standard deviation of spectral intensity is used to represent the average intensity of each spectrum within the defined range, which is considered an effective spectrum. The sample size is expanded using a small sample augmentation algorithm, the spectral background signal is removed using a window translation smoothing method, and the spectrum is normalized using a channel-based normalization method. (4) The sample dataset is randomly divided into training set and test set. The preprocessed and standardized gas product spectra are used as input data. The explosion capacity quantitative analysis model is established by PCA-PLS method. The number of principal components in the modeling process is determined by five-fold cross-validation. The explosion capacity quantitative analysis model is evaluated by coefficient of determination R², maximum relative error MRE, mean relative error ARE and root mean square error RMSE.

2. The method for obtaining the explosive capacity of energetic materials based on a small amount of gaseous products according to claim 1, characterized in that, The gas chamber in step (1) is connected to a pressure sensor, a temperature and humidity sensor, a solenoid valve, a proportional valve, a digital flow meter, and can be connected to different gases.

3. The method for obtaining the explosive capacity of energetic materials based on a small amount of gaseous products according to claim 1, characterized in that, The testing system in step (2) mainly includes: two 1064nm Nd:YAG lamp-pumped Q-switched lasers, a photodetector, a 1064nm laser mirror, a fused silica plano-convex lens, a five-channel fiber optic spectrometer, a digital delay pulse generator, a three-dimensional electric displacement stage, and a high-speed schlieren imaging system consisting of a high-speed color camera, a halogen lamp, a planar mirror, a concave mirror, and a knife edge.

4. The method for obtaining the explosive capacity of energetic materials based on a small amount of gaseous products according to claim 1, characterized in that, In step (2), the dynamic process of the gas products is obtained through a high-speed schlieren system, and the spatial location and time of the gas products are determined.

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