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At least 1,459 records · Page 81

Large-Area PDV for Characterizing Explosive Particle and Fragment Velocity Distributions, STL-023-17, Year 1 of 1

This presentation describes a project to develop a PDV system that can measure over an area of 80 cm2, as opposed to the traditional point-sampling PDV. Ultimately the large-area PDV could be used to characterize the morphological evolution of a high-explosive event over the complete event, from very early stages (source terms) to an equilibrium state.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Analysis of ignition and stabilization modes in diesel spray flames using large eddy simulations and chemical explosive mode analysis

In this work, spray flames under diesel engine conditions are numerically investigated to understand the ignition and stabilization characteristics, based on state-of-the-art large eddy simulations (LES) and chemical explosive mode analysis (CEMA). In particular, CEMA is employed to identify such critical flame features as auto-ignition, premixed flame fronts, cool and strongly burning flames, and local extinction. In particular, a recently developed CEMA-based criterion for demarcating local combustion modes, that is, auto-ignition, diffusion-assisted ignition, and extinction, is applied to gain in-depth insights into the interactions of local chemical and transport processes. A series of one-dimensional non-premixed flamelets with varying air temperatures and scalar dissipation rates are first analyzed using CEMA with a focus on the role of each local mode in the transient ignition process. CEMA-based diagnostics are then applied to diesel spray flames at different ambient temperatures. The structure of local combustion modes is identified, while the contribution from each local mode to overall flame burning is quantified at different stages of the flame developing process. Effects of the ambient temperature on local combustion mode distribution, and subsequently on the ignition and stabilization mechanisms, are further examined. Results show that auto-ignition is the main mechanism for flame initiation and stabilization, while the role of diffusion is substantially enhanced as ambient temperature decreases.

assisted-ignition↗

Defeat system for vehicle attached improvised explosive devices

An explosive device detection and expulsion system broadly comprising a detection subsystem for sensing an IED attached to a vehicle and an expulsion subsystem for expelling the IED from the vehicle. The detection subsystem includes a sensor for detecting an IED via an electromagnetic field change or capacitance change, vibrations, and/or heat, and a controller for activating the expulsion subsystem upon receiving a detection signal from the sensor. The expulsion subsystem includes a deployable panel configured to be attached to the vehicle and a deployable mechanism for shifting the deployable panel from an undeployed state to a deployed state so as to expel the IED from the vehicle.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

A low-permeability underground nuclear explosion containment regime: An example of gas transport by “invisible” fractures?

Understanding the nature of gas transport from an underground nuclear explosion (UNE) is required for evaluating the ability to detect and interpret either on-site or atmospheric signatures of noble gas radionuclides resulting from the event. We performed a pressure and chemical tracer monitoring experiment at the site of an underground nuclear test that occurred in a tunnel in Nevada to evaluate the possible modes of gas transport to the surface. The site represents a very well-contained, low gaspermeability end member for past UNEs at the Nevada National Security Site. However, there is very strong evidence that gases detected at the surface during a period of low atmospheric pressure resulted from fractures of extremely small aperture that are essentially invisible. Our analyses also suggest that gases would have easily migrated to the top of the high-permeability collapse zone following the detonation minimizing the final distance required for migration along these narrow fractures to the surface. This indicates that on-site detection of gases emanating from such low-permeability sites is feasible while standoff detection of atmospheric plumes may also be possible at local distances for sufficiently high fracture densities. Finally, our results show that gas leakage into the atmosphere also occurred directly from the tunnel portal and should be monitored in future tunnel gas sampling experiments for the purpose of better understanding relative contributions to detection of radioxenon releases via both fracture network and tunnel transport.

58 GEOSCIENCES↗

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗

Defeat system for vehicle attached improvised explosive devices

An explosive device detection and expulsion system broadly comprising a detection subsystem for sensing an IED attached to a vehicle and an expulsion subsystem for expelling the IED from the vehicle. The detection subsystem includes a sensor for detecting an IED via an electromagnetic field change or capacitance change, vibrations, and/or heat, and a controller for activating the expulsion subsystem upon receiving a detection signal from the sensor. The expulsion subsystem includes a deployable panel configured to be attached to the vehicle and a deployable mechanism for shifting the deployable panel from an undeployed state to a deployed state so as to expel the IED from the vehicle.

Hower, Brent↗

Methods and devices for preventing computationally explosive calculations in a computer for model parameters distributed on a hierarchy of geometric simplices

A computer-implemented method of preventing computationally explosive calculations. The method includes obtaining, by a processor of the computer, measured data of one of a physical process or a physical object; performing hierarchical numerical modeling of a physical process inclusive of an Earth model containing at least one of (a) infrastructure in the ground and (b) a formation feature in the ground, wherein predicted data is generated; comparing the measured data to the predicted data to calculate an estimated error; analyzing the estimated error via an inversion process to update the at least one of the Earth model and infrastructure model so as to reduce the estimated error and to determine a final composite Earth model of at least one of the infrastructure and the feature; and using the final composite Earth model to characterize at least one of the process and the physical object.

Source record↗

The 2022 Hunga eruption exercises the nuclear explosion monitoring community

The 2022 Hunga eruption was a global event recorded by all three Comprehensive Test Ban Treaty Organization (CTBTO) International Monitoring System (IMS) geophysical waveform sensor technologies: seismic, infrasound, and hydroacoustic. Large volcanic eruptions are rare and additionally present an opportunity to test seismoacoustic detection, location, and identification methods for nuclear explosion monitoring.

58 GEOSCIENCES↗

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXI

This volume contains 12 sessions, 28 papers and 26 presentations on the following topics: Photonic Integrated Circuits and Plasmonic Sensing; Advancs in CBE Signature Modeling and Sensor Algorithms I; Standoff Sensing of CBRNE Hazards I; Standoff Sensing of CBRNE Hazards II; Advances in CBE Signature Modeling and Sensor Algorithms II; Biological Hazard Sensing; Chemical Hazard Sensing; Joint Session with Conferences 11390 and 11416: Spectroscopy for CBRNE Detection and Warning; Radiological and Nuclear Sensing; Explosive Material Sensing; Poster Session.

47 OTHER INSTRUMENTATION↗

Improved Smartphone Network Deployment Methods for Explosion Collection

Improved Smartphone Network Deployment Methods for Explosion Collection Samuel Kei Takazawa1, Sarah Popenhagen1, Milton A. Garcés1, Luis Ocampo Giraldo2, Jay Hix2 1University of Hawai?i, 2Idaho National Laboratory Milton A. Garcés, milton@isla.hawaii.edu ETI – Consortium for Enabling Technologies and Innovation: Director, Anna Erickson, Ph.D.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

The Henkin-McGill critical temperature test for explosives revisited: An overview of historical and modern method development

Many overarching standards, regulations, or other requirements necessitate that the critical temperature (T crit ) of an energetic material be known or estimate prior to operations such as heating. However, they rarely, if ever, provide a specific method for doing so. While other methods exist to calculate T crit , such as the Frank-Kamenetskii equation, computer simulation, etc., these cannot be readily utilized for energetics that are physical mixtures (i.e. most pyrotechnics) or materials where detailed material properties required for such calculations are lacking. Here, in this study, a COTS (commercial off-the-shelf) SBAT (Simulated Bulk Auto-ignition Test) apparatus is modified to perform Henkin cookoff tests. This creates a simple, efficient, and cost-effective solution to estimate required T crit values for energetic materials. After a historical overview of the evolution of the Henkin test, several common energetic materials (PETN, RDX, etc.) for which T crit has been readily calculated, and historical data is available, were analyzed for comparison and verification purposes. This was followed by a variety of pyrotechnic mixtures and other materials to where calculation methods cannot be readily used. The modified apparatus, as well as an updated sample shell sealing method, produced results that aligned well with historical data. It also produced reasonable T crit estimates for those materials where the Frank-Kamenetskii equation cannot be applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗