Influence of Al and Al_2O_3 Nanoparticles on the Thermal Decay of 1,3,5-Trinitro-1,3,5-triazinane (RDX): Reactive Molecular Dynamics Simulations

Metallic additives, Al nanoparticles in particular, have extensively been used in energetic materials (EMs), of which thermal decomposition is one of the most basic properties. Nevertheless, the underlying mechanism for the highly active Al nanoparticles and their oxidized counterparts, the Al_2O_3 nanoparticles, influencing the thermal decay of aluminized EMs has not fully been understood. Herein, we explore the influence of Al and Al2O3 nanoparticles on the thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane (RDX), one of the most common EMs, based on large-scale reactive force field molecular dynamics simulations within three heating schemes (constant-temperature, programmed, and adiabatic heating). The presence of Al nanoparticles significantly reduces the induction time and energy required to activate the RDX decay and greatly increases energy release. The fundamental reason for these results is that Al changes the primary decay pathway from the unimolecular N–NO_2 scission of RDX to bimolecular barrier-free or low-barrier Al-involved reactions and possesses a strong O-extraction capability and a moderate one to react with C/H/N. It is also responsible for the growth of the Al-containing clusters. In addition, Al_2O_3 nanoparticles can also demonstrate such catalysis capability but contribute less to the enhancement of energy release. Moreover, the detailed evolutions of key thermodynamic properties, intermediate and final gaseous products, and Al-containing products are also presented. Besides, under the programmed heating and adiabatic heating conditions, the catalysis of the Al and Al_2O_3 nanoparticles becomes more distinct. Therefore, many properties of aluminized EMs are expected to well be understood by our simulation results

Influence of Al and Al_2O_3 Nanoparticles on the Thermal Decay of 1,3,5-Trinitro-1,3,5-triazinane (RDX): Reactive Molecular Dynamics Simulations

Metallic additives, Al nanoparticles in particular, have extensively been used in energetic materials (EMs), of which thermal decomposition is one of the most basic properties. Nevertheless, the underlying mechanism for the highly active Al nanoparticles and their oxidized counterparts, the Al_2O_3 nanoparticles, influencing the thermal decay of aluminized EMs has not fully been understood. Herein, we explore the influence of Al and Al2O3 nanoparticles on the thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane (RDX), one of the most common EMs, based on large-scale reactive force field molecular dynamics simulations within three heating schemes (constant-temperature, programmed, and adiabatic heating). The presence of Al nanoparticles significantly reduces the induction time and energy required to activate the RDX decay and greatly increases energy release. The fundamental reason for these results is that Al changes the primary decay pathway from the unimolecular N–NO_2 scission of RDX to bimolecular barrier-free or low-barrier Al-involved reactions and possesses a strong O-extraction capability and a moderate one to react with C/H/N. It is also responsible for the growth of the Al-containing clusters. In addition, Al_2O_3 nanoparticles can also demonstrate such catalysis capability but contribute less to the enhancement of energy release. Moreover, the detailed evolutions of key thermodynamic properties, intermediate and final gaseous products, and Al-containing products are also presented. Besides, under the programmed heating and adiabatic heating conditions, the catalysis of the Al and Al_2O_3 nanoparticles becomes more distinct. Therefore, many properties of aluminized EMs are expected to well be understood by our simulation results

Laser-based defect characterization and removal process for manufacturing fused silica optic with high ultraviolet laser damage threshold

Residual processing defects during the contact processing processes greatly reduce the anti-ultraviolet (UV) laser damage performance of fused silica optics, which significantly limited development of high-energy laser systems. In this study, we demonstrate the manufacturing of fused silica optics with a high damage threshold using a CO2 laser process chain. Based on theoretical and experimental studies, the proposed uniform layer-by-layer laser ablation technique can be used to characterize the subsurface mechanical damage in three-dimensional full aperture. Longitudinal ablation resolutions ranging from nanometers to micrometers can be realized; the minimum longitudinal resolution is < 5 nm. This technique can also be used as a crack-free grinding tool to completely remove subsurface mechanical damage, and as a cleaning tool to effectively clean surface/subsurface contamination. Through effective control of defects in the entire chain, the laser-induced damage thresholds of samples fabricated by the CO2 laser process chain were 41% (0% probability) and 65.7% (100% probability) higher than those of samples fabricated using the conventional process chain. This laser-based defect characterization and removal process provides a new tool to guide optimization of the conventional finishing process and represents a new direction for fabrication of highly damage-resistant fused silica optics for high-energy laser applications

Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effectiveness of the proposed approach

Atomic insight into the thermobaric effect of aluminized explosives

The addition of active metallic particles to common explosives can greatly extend high-temperature and high-pressure time of explosion, as the so-called thermobaric effect. However, the atomic details for this effect remain unclear. This work presents an atomic insight into the thermobaric explosion including anaerobic and aerobic stages by molecular dynamics simulations with a thermobaric explosive (TBX) model consisting of Al@Al2O3 nanoparticle and RDX. It is found that the thermobaric effect is originated from the Al oxidation by the intermediates and products of the RDX decomposition in the first anaerobic stage with a lot of Al-O, Al-H, Al-C and Al-N bonds formed, and the further oxidation by O2 in the aerobic stage with many C, H and N atoms extruded from the Al clusters. Interestingly, in terms of the components and structures of the final products, as well as the total heat release, the whole thermobaric explosion of the RDX-Al system can be regarded as the sum of the RDX decomposition and the Al oxidation in O2. Thereby, the energy design of a TBX can be simplified by considering the sum of the decomposition heat of the energetic compound involved and the combustion heat of the active metal. Regarding RDX, it plays a role in forming a high temperature and high pressure environment to promote the dispersion and combustion of Al particles. This work presents the atomic details responsible for the thermobaric effect and it is expected to pave a way to understand this effect

Observer-Based Robust Fault Detection Filter Design and Optimization for Networked Control Systems

The problem of robust fault detection filter (FDF) design and optimization is investigated for a class of networked control systems (NCSs) with random delays. The NCSs are modeled as Markovian jump systems (MJSs) by assuming that the random delays obey a Markov chain. Based on the model, an observer-based residual generator is constructed and the corresponding fault detection problem is formulated as an H∞ filtering problem by which the error between the residual signal and the fault is made as small as possible. A sufficient condition for the existence of the desired FDF is derived in terms of linear matrix inequalities (LMIs). Furthermore, to improve the performance of the robust fault detection systems, a time domain optimization approach is proposed. The solution of the optimization problem is given in the form of Moore-Penrose inverse of matrix. A numerical example is provided to illustrate the effectiveness and potential of the proposed approach

Rapid fragmentation contributing to the low heat resistance of energetic materials

Heat resistance is a basic and crucial characteristic of energetic materials (EMs), and always accounted in development and application. Compared with the clear origin of the high heat resistance of EMs, the mechanism for the low heat resistance remains still unclear. This work reveals the mechanism by carrying reactive molecular dynamics simulations on heating six less thermally stable EMs of nitroforms and pentaerythritol tetranitrate (PETN), as well as a more thermally stable EM of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) for comparison. Unexceptionally, all the nitroforms and PETN heated feature fast NO2 partition and further decomposition throng the oxidation of NO2 to form small fragments and final small stable product molecules, with fast heat release; while, the intermolecular reactions and the further clustering govern the initial steps in decomposing TATB. Therein the reactants also exhibit a rapid consumption; however, this fast consumption with clustering does not result in the low heat resistance of TATB. That is, some general indicators representative of thermostability, such as bond dissociation energy and the reactant consumption rate, are insufficient to assess it practically. Thus, the rapid fragmentation originally contributes to the low heat resistance. These insights are expected to present an overall perspective of understanding the thermal stability mechanism of EMs, and set a theoretical base and pave a way for designing EMs with desired heat resistance

Study on Ecological Security Evaluation Index System of the Yellow River Basin

As an important energy base and ecological barrier in China, the ecological security of the Yellow River Basin has a very important strategic position in the pattern of high-quality development, but it is threatened by human activities and climate change. In order to evaluate the degree of ecological security in the Yellow River Basin and clarify the influencing factors of ecological security, this paper discusses the advantages and disadvantages of the existing ecological security assessment methods and models. On this basis, the system dynamics model is used to describe the process of ecological security threats, identify the key factors that threaten ecological security, and then sort out the ecological security assessment indicators. The ecological security assessment model composed of pressure(P), state(S), hidden dangers(D), response(R) and management (M) (PSDRM) indicators is constructed, and the ecological security assessment methods and processes are improved to provide theoretical guidance and technical support for improving the ecological security assessment of the basin

Unsupervised SAR Image Change Type Recognition Using Regionally Restricted PCA-Kmean and Lightweight MobileNet

Change detection using synthetic aperture radar (SAR) multi-temporal images only detects the change area and generates no information such as change type, which limits its development. This study proposed a new unsupervised application of SAR images that can recognize the change type of the area. First, a regionally restricted principal component analysis k-mean (RRPCA-Kmean) clustering algorithm, combining principal component analysis, k-mean clustering, and mathematical morphology composition, was designed to obtain pre-classification results in combination with change type vectors. Second, a lightweight MobileNet was designed based on the results of the first stage to perform the reclassification of the pre-classification results and obtain the change recognition results of the changed regions. The experimental results using SAR datasets with different resolutions show that the method can guarantee change recognition results with good change detection correctness