U.S. Air Force Research Laboratory Munitions Directorate

We report the results of a mechanistic study of energy localization in aHMX (High Melting point eXplosive octahydro-1,3,5,7-tetranitro-1,2,3,5-tetrazocine)/Estane PBX system during dynamic loading. The focus is on the thermal-mechanical response over the strain rate range of 104 – 105 s−1 under different confinement conditions. A recently developed cohesive finite element method is used to track and analyze the contributions to heating from different constituents, interfaces, deformation and fracture mechanisms, and internal friction. In particular, energy dissipations due to viscoelastic deformation, grain fracture, interfacial debonding, and friction along crack faces are quantified as functions of time and overall deformation. The materials analyzed have HMX volume fractions between 0.69 and 0.82. Calculations show that variation in strain rate can significantly affect the spatial distribution but not the overall number of hot spots. Higher confining stresses lead to more intense heating in the binder and more uniform distribution of hot spots. The evolution of hot spots is quantified as a function of loading condition, deformation and microstructural attributes. The microstructure-response relations obtained can be used to assess the initiation sensitivity of energetic composites.

A criterion for the ignition of granular explosives (GXs) and polymer-bonded explosives (PBXs) under shock and non-shock loading is developed. The formulation is based on integration of a quantification of the distributions of the sizes and locations of hotspots in loading events using a cohesive finite element method (CFEM) developed recently and the characterization by Tarver et al. [C. M. Tarver et al., "Critical conditions for impact- and shock-induced hot spots in solid explosives," J. Phys. Chem. 100, 5794–5799 (1996)] of the critical size-temperature threshold of hotspots required for chemical ignition of solid explosives. The criterion, along with the CFEM capability to quantify the thermal-mechanical behavior of GXs and PBXs, allows the critical impact velocity for ignition, time to ignition, and critical input energy at ignition to be determined as functions of material composition, microstructure, and loading conditions. The applicability of the relation between the critical input energy (E) and impact velocity of James [H. R. James, "An extension to the critical energy criterion used to predict shock initiation thresholds," Propellants, Explos., Pyrotech. 21, 8–13 (1996)] for shock loading is examined, leading to a modified interpretation, which is sensitive to microstructure and loading condition. As an application, numerical studies are undertaken to evaluate the ignition threshold of granular high melting point eXplosive, octahydro-1,3,5,7-tetranitro-1,2,3,5-tetrazocine (HMX) and HMX/Estane PBX under loading with impact velocities up to 350 ms−1 and strain rates up to 105 s−1. Results show that, for the GX, the time to criticality (tc) is strongly influenced by initial porosity, but is insensitive to grain size. Analyses also lead to a quantification of the differences between the responses of the GXs and PBXs in terms of critical impact velocity for ignition, time to ignition, and critical input energy at ignition. Since the framework permits explicit tracking of the influences of microstructure, loading, and mechanical constraints, the calculations also show the effects of stress wave reflection and confinement condition on the ignition behaviors of GXs and PBXs.

Abstract The deterministic problem for finding an aircraft's optimal risk trajectory in a threat environment has been formulated. The threat is associated with the risk of aircraft detection by radars or similar sensors. The model considers an aircraft's trajectory in three‐dimensional (3‐D) space and represents the aircraft by a symmetrical ellipsoid with the axis of symmetry directing the trajectory. Analytical and discrete optimization approaches for routing an aircraft with variable radar cross‐section (RCS) subject to a constraint on the trajectory length have been developed. Through techniques of Calculus of Variations, the analytical approach reduces the original risk optimization problem to a vectorial nonlinear differential equation. In the case of a single detecting installation, a solution to this equation is expressed by a quadrature. A network optimization approach reduces the original problem to the Constrained Shortest Path Problem (CSPP) for a 3‐D network. The CSPP has been solved for various ellipsoid shapes and different length constraints in cases with several radars. The impact of ellipsoid shape on the geometry of an optimal trajectory as well as the impact of variable RCS on the performance of a network optimization algorithm have been analyzed and illustrated by several numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006

A smooth finite-time distributed control architecture is introduced and analyzed for the cooperative engagement problem. Using a time transformation method as well as Lyapunov stability theory, it is shown that the proposed architecture guarantees finite-time cooperative engagement in that the difference between the positions of each agent and a time-varying target, where this difference represents a dynamic equilibrium point, vanishes in a-priori given, user-defined finite time. In addition, this finite-time convergence is achieved without dependence on the initial conditions of agents and in the presence of unknown but bounded velocity of the target. Specifically, we first time transformed the proposed smooth finite-time distributed control architecture into an infinite-time (that is, stretched) interval. This time transformation method is then allowed to utilize tools from standard Lyapunov stability theory in which we analyze convergence properties of this architecture and boundedness of local control signals of each agent in this infinite-time interval. While this note focuses on a particular problem in the context of multiagent systems, the proposed time transformation method and the analysis procedure can be used for many other problems, where a-priori given, user-defined finite-time convergence is necessary with smooth control laws.

Random variations in constituent properties, constituent distribution, microstructural morphology, and loading cause the ignition of explosives to be inherently stochastic. An approach is developed to computationally predict and quantify the stochasticity of the ignition process in polymer-bonded explosives (PBXs) under impact loading. The method, the computational equivalent of carrying out multiple experiments under the same conditions, involves subjecting sets of statistically similar microstructure samples to identical overall loading and characterizing the statistical distribution of the ignition response of the samples. Specific quantities predicted based on basic material properties and microstructure attributes include the critical time to ignition at given load intensity and the critical impact velocity below which no ignition occurs. The analyses carried out focus on the influence of random microstructure geometry variations on the critical time to ignition at given load intensity and the critical impact velocity below which no ignition occurs. Results show that the probability distribution of the time to criticality (tc) follows the Weibull distribution. This probability distribution is quantified as a function of microstructural attributes including grain volume fraction, grain size, specific binder-grain interface area, and the stochastic variations of these attributes. The relations reveal that the specific binder-grain interface area and its stochastic variation have the most influence on the critical time to ignition and the critical impact velocity below which no ignition is observed. For a PBX with 95% octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine content, the computationally predicted minimum impact velocity for ignition is in the range of 54–63 ms−1 depending on microstructure. This range is comparable to values measured experimentally for PBX9501 (53 ms−1 by Chidester et al., “Low amplitude impact testing and analysis of pristine and aged solid high explosives,” in Eleventh (International) Symposium on Detonation, ONR (1998), 33300. 60–84 ms−1 by Gruau et al., “Ignition of a confined high explosive under low velocity impact,” Int. J. Impact Eng. 36, 537–550 (2009)).

The demand for additively manufactured polymer composites with increased specific properties and functional microstructure has drastically increased over the past decade. The ability to manufacture complex designs that can maximize strength while reducing weight in an automated fashion has made 3D-printed composites a popular research target in the field of engineering. However, a significant amount of understanding and basic research is still necessary to decode the fundamental process mechanisms of combining enhanced functionality and additively manufactured composites. In this review, external field-assisted additive manufacturing techniques for polymer composites are discussed with respect to (1) self-assembly into complex microstructures, (2) control of fiber orientation for improved interlayer mechanical properties, and (3) incorporation of multi-functionalities such as electrical conductivity, self-healing, sensing, and other functional capabilities. A comparison between reinforcement shapes and the type of external field used to achieve mechanical property improvements in printed composites is addressed. Research has shown the use of such materials in the production of parts exhibiting high strength-to-weight ratio for use in aerospace and automotive fields, sensors for monitoring stress and conducting electricity, and the production of flexible batteries.

The Air Force Research Laboratory Munitions Directorate (AFRL/MN) is presently involved in many aspects of micro aerial vehicle (MAV) research. Among these are: advanced modeling and simulation models for MAVs, aero-structural interaction, advanced guidance techniques, hardware-in-the-loop simulations, and vehicle integration. In order to optimize collaboration within AFRL and also with outside research organizations, it was decided that a common MAV configuration be designed that would serve as a reference for current and future research. This paper describes a generic micro air vehicle that will serve as a "baseline" configuration. The MAV design incorporates a circular fuselage, a thin cambered wing, and a conventional tail. The MAV has a wingspan of 24 inches and a fuselage length of 17 inches. This paper will also detail the rational behind the design as well as provide initial aerodynamic properties and flight performance characteristics of the AFRL Generic MAV, herein called "GENMAV."

Carbon fiber-reinforced epoxy composites (CFEC) were fabricated infusing 0–0.40 wt% XD-grade carbon nanotube (XD-CNT) using the compression molding process under 16 kips. XD-CNTs were infused into Epon 862 resin using a mechanical stirrer followed by a high intensity ultrasonic liquid processor. The mixture was then placed in a three-roll milling processor for three successive cycles at 140 rpm. Epikure W curing agent was added to the modified resin and mixed using a high-speed mechanical stirrer. Flexural and tensile properties obtained from the flexural and tensile tests were higher in all nanophased composites compared to those of the conventional one. However, samples with 0.3 wt% CNT loading demonstrated the maximum improvement by 27% and 14% in flexural strength and modulus and 19% and 10% in tensile strength and modulus, respectively. Fracture morphology studied by both scanning electron microscopy and optical microscopy revealed better interfacial bonding in the CNT-loaded CFEC.

We report polarized infrared absorption spectra of water isotopologues isolated in solid parahydrogen (pH2) which reveal the crystal field induced splittings of the 1 01<--0 00 R(0) lines in the nu1 HDO, nu3 D2O, nu3 HDO, and nu3 H2O fundamental bands. For annealed pH2 solids, these spectra also reveal a strong alignment of the hexagonal-close-packed crystallites' c axes with the deposition substrate surface normal. This alignment effect explains our failure to detect the parallel-polarized components of these R(0) lines in spectra of pH2 solids produced on a transparent deposition substrate [M. E. Fajardo et al., J. Mol. Struct. 695, 111 (2004)]. This lesson applies more generally to comparison of solid pH2 spectra obtained in different laboratories. The spectra are consistent with water monomers existing in solid pH2 as very slightly hindered rotors. The individual components of the R(0) absorption lines show a Lorentzian lineshape, with vibrational depopulation the most important source of line broadening.

Accounting for the combined effect of multiple sources of stochasticity in material attributes, we develop an approach that computationally predicts the probability of ignition of polymer-bonded explosives (PBXs) under impact loading. The probabilistic nature of the specific ignition processes is assumed to arise from two sources of stochasticity. The first source involves random variations in material microstructural morphology; the second source involves random fluctuations in grain-binder interfacial bonding strength. The effect of the first source of stochasticity is analyzed with multiple sets of statistically similar microstructures and constant interfacial bonding strength. Subsequently, each of the microstructures in the multiple sets is assigned multiple instantiations of randomly varying grain-binder interfacial strengths to analyze the effect of the second source of stochasticity. Critical hotspot size-temperature states reaching the threshold for ignition are calculated through finite element simulations that explicitly account for microstructure and bulk and interfacial dissipation to quantify the time to criticality (tc) of individual samples, allowing the probability distribution of the time to criticality that results from each source of stochastic variation for a material to be analyzed. Two probability superposition models are considered to combine the effects of the multiple sources of stochasticity. The first is a parallel and series combination model, and the second is a nested probability function model. Results show that the nested Weibull distribution provides an accurate description of the combined ignition probability. The approach developed here represents a general framework for analyzing the stochasticity in the material behavior that arises out of multiple types of uncertainty associated with the structure, design, synthesis and processing of materials.

Abstract We consider the problem of detecting a collection of critical node structures of a graph whose deletion results in the maximum deterioration of the graph's connectivity. The proposed approach is aimed to generalize other existing models whose scope is restricted to removing individual and unrelated nodes. We consider two common metrics to quantify the connectivity of the residual graph: the total number of connected node pairs and the size of the largest connected component. We first discuss the computational complexity of the problem and then introduce a general mixed‐integer linear formulation, which depending on the kind of node structures, may have an exponentially large number of variables and constraints. To solve this potentially large model, we develop a branch‐price‐and‐cut framework, along with some valid inequalities and preprocessing algorithms to strengthen the formulation and reduce the overall execution time. We use the proposed approach to solve the problem for the cases, where the node structures form cliques or stars and provide further directions on how to extend the framework for detecting other kinds of critical structures as well. Finally, we test the quality of our approach by solving a collection of real‐life and randomly generated instances with various configurations, analyze the benefits of our model, and propose further enhancements.

Microscopic defects such as voids and cracks in an energetic material significantly influence its shock sensitivity. So far, there is a lack of systematic and quantitative study of the effects of cracks both experimentally and computationally, although significant work has been done on voids. We present an approach for quantifying the effects of intragranular and interfacial cracks in polymer-bonded explosives (PBXs) via mesoscale simulations that explicitly account for such defects. Using this approach, the ignition thresholds corresponding to any given level of ignition probability and, conversely, the ignition probability corresponding to any loading condition (i.e., ignition probability maps) are predicted for PBX 9404 containing different levels of initial grain cracking or interfacial debonding. James relations are utilized to express the predicted thresholds and ignition probabilities. It is found that defects lower the ignition thresholds and cause the material to be more sensitive. This effect of defects on shock sensitivity diminishes as the shock load intensity increases. Furthermore, the sensitivity differences are rooted in energy dissipation and the consequent hotspot development. The spatial preference in hotspot distribution is studied and quantified using a parameter called the defect preference ratio (rpref). Analyses reveal that defects play an important role in the development of hotspots and thus have a strong influence on the ignition thresholds. The findings are in qualitative agreement with reported trends in experiments.

Neuromorphic architectures have been introduced as platforms for energy-efficient spiking neural network execution. The massive parallelism offered by these architectures has also triggered interest from nonmachine learning application domains. In order to lift the barriers to entry for hardware designers and application developers, we present RANC: a reconfigurable architecture for neuromorphic computing, an opensource highly flexible ecosystem that enables rapid experimentation with neuromorphic architectures in both software via C++ simulation and hardware via FPGA emulation. We present the utility of the RANC ecosystem by showing its ability to recreate behavior of IBM’s TrueNorth and validate with a direct comparison to IBM’s Compass simulation environment and published literature. RANC allows optimizing architectures based on application insights as well as prototyping future neuromorphic architectures that can support new classes of applications entirely. We demonstrate the highly parameterized and configurable nature of RANC by studying the impact of architectural changes on improving application mapping efficiency with quantitative analysis based on Alveo U250 FPGA. We present post routing resource usage and throughput analysis across implementations of synthetic aperture radar classification and vector matrix multiplication applications, and demonstrate a neuromorphic architecture that scales to emulating 259K distinct neurons and 73.3M distinct synapses.

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

Accurate simulations of the shock response of heterogeneous energetic (HE) materials require closure models, which account for energy localization in the micro-structure. In a multi-scale framework, closure is provided by reaction rate models that account for ignition and growth of hotspots, allowing for prediction of the overall macro-scale sensitivity of a HE material. In the present meso-informed ignition and growth (MES-IG) model, the reaction rate is expressed as a function of shock pressure and morphology of the void field in a pressed energetic material. In MES-IG, the void morphology is quantified in terms of a limited number of parameters: viz., overall porosity, void size, and shape (aspect ratio and orientation). In this paper, we quantify the effects of arbitrary variations in void shapes on meso-scale energy deposition rates. A collection of voids of arbitrary shapes is extracted from scanning electron microscope (SEM) images of real, pressed HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) samples and classified into groups based on their similarity in shapes. Direct numerical simulations (DNS) are performed on the highly contorted “real” void shapes, and the calculated hotspot ignition and growth rates are compared with values predicted by the MES-IG. It is found that while the parameterization of complex void morphologies in terms of orientation and aspect ratio gives fairly good agreement between DNS and MES-IG reaction rates, the intricate details of highly complex void shapes impact hotspot characteristics to a significant extent. This work suggests possible improvements for the prediction of reaction rate in the energetic microstructure by adopting a more detailed description of shapes.

Helicoidal composites have been found in shrimp club, lobster claw, beetle cuticle, crab shell, scorpion pincer, and fish scale as a natural material. The helicoidal composite possesses excellent impact resistance and extraordinary damage tolerance due to its hierarchical structure and the unique helicoidal arrangement of its reinforcement fibres. Its structure and performance have been studied through various characterisation and mechanical testing methods. Based on the structure-property relationship of the natural helicoidal composite, researchers have been able to mimic the unique fibre arrangement and develop bio-inspired helicoidal composites with enhanced impact performance. Various helicoidal composites comprising of synthetic fibrous materials such as carbon fibre (CF), glass fibre (GF), and aramid fibre, and matrix materials such as thermoset and thermoplastic polymers have been developed through biomimicry. The failure mechanisms of the bio-inspired helicoidal composites have been studied and the advantages of arranging the fibre reinforcement into helicoidal architectures have been elucidated over conventional composite constructions such as quasi-isotropic (QI) and cross-ply layups. This review systematically elaborates the recent progress of the research work on both natural and bio-inspired helicoidal composites. It sheds light on the distinctive construction of the natural helicoidal composites found in different animals such as shrimps, lobsters, crabs, beetles, scorpions, and fish, and their energy absorption mechanisms. Different manufacturing methods for developing bio-inspired helicoidal composites are discussed and various reinforcements and matrix materials used in the composites are described. The processing-structure-property interrelationship of the bio-inspired helicoidal composites is summarised. This review will contribute to the advancement of the knowledge of the natural helicoidal composite and potentially help researchers to develop highly efficient bio-inspired damage tolerant helicoidal composites.

Current distributed control methods have a lack of information exchange infrastructure to enable spatially evolving multiagent formations. Specifically, these methods are designed based on information exchange rules represented by a network having a single layer, where they lead to multiagent formations with fixed, nonevolving spatial properties. For situations where capable agents have to control the resulting formation through these methods, they can often do so if such agents have global information exchange ability. Yet, global information exchange is not practical for cases that have large numbers of agents and low-bandwidth peer-to-peer communications. Motivated from this standpoint, the contribution of this paper is to show how information exchange rules, which are represented by a network having multiple layers (multiplex information networks), can be designed for enabling spatially evolving multiagent formations. In particular, we first consider the formation assignment problem and then the formation tracking problem and introduce new distributed control architectures that allow capable agents to spatially alter the size and the orientation of the resulting formation without requiring global information exchange ability. In addition, tools and methods from differential potential fields are further utilized in order to generalize the proposed distribute control architecture for the formation tracking problem to allow for connectivity maintenance and collision avoidance needed in real-world applications. Stability of the proposed architectures is theoretically analyzed and their efficacy is illustrated on numerical examples and on multiagent formation experiments.

We experimentally verify a new class of coherent absorbers based on guided-mode resonance effects in periodic thin films. We design a silicon-based resonant absorber that is fabricated and tested near the 1300-nm wavelength. Implementing phase control, the device can, in principle, be switched between full absorption and full scattering. The first experimental prototype presented herein shows ~78% absorption in the in-phase state. Nearly total scattering is realized in the out-of-phase state. The experimental results agree reasonably well with theory.

The characterization of a new wind tunnel at the University of Florida’s Research and Engineering Education Facility is presented. The tunnel is specifically designed to operate in the low Reynolds number regime in which Micro Air Vehicles operate. The wind tunnel is driven by a 60 inch, 50 hp axial blower controlled by a variable frequency drive. The tunnel entrance consists of a flow conditioning section and a 8:1 area contraction ratio that results in a 42” square entrance to the open jet test section. The enclosure surrounding the test section has a volume of nearly 2000 ft with an axial length of 10 ft. The free stream velocities in the tunnel range from nominally 0 to 22 m/s by altering the frequency on the variable frequency drive. Flow uniformity studies for free stream velocities of 2 and 15 m/s were conducted demonstrating a potential core throughout the test section of at least 60% of the 1.14 m contraction exit. Experiments using hot wire anemometry were also performed and the turbulence intensities were found to be less 0.22% for free stream velocities greater than 1m/s.

This paper presents the development of an adaptive controller for a hypersonic air-breathing missile with terminal constraints. The controller is designed to regulate the longitudinal dynamics of a hypersonic vehicle model via a backstepping approach. The backstepping approach is used to compensate for uncertainties in the dynamics that do not satisfy the matching condition while ensuring asymptotic tracking of a desired velocity profile and asymptotic regulation of the vehicle position, angle of attack, body angle, and angular rates. A Lyapunov-based stability analysis is used to prove the asymptotic regulation of the controlled states. Simulation results are presented to verify the performance of the controller.