The Experts below are selected from a list of 390 Experts worldwide ranked by ideXlab platform

Robert J Hammell - One of the best experts on this subject based on the ideXlab platform.

  • predicting Terminal Ballistics using an iterative application of an artificial neural network
    2017 Computing Conference, 2017
    Co-Authors: John R Auten, Robert J Hammell
    Abstract:

    The need to accurately predict the Terminal Ballistics of kinetic energy projectiles in Vulnerability/Lethality models is of paramount importance to the U.S. Department of Defense. In previous work, an artificial neural network was trained on a set of 1,877 data points to predict perforation, residual velocity, and residual mass of a kinetic energy projectile impacting a target consisting of a single homogeneous-monolithic-metallic element. The capability of that neural network was extended to handle targets consisting of multiple homogeneous-monolithic-metallic elements by iteratively applying the artificial neural network on each element in the target and using the predicted residual properties as inputs for the next element. The performance of that approach is analyzed against a phenomenological model that is currently used by the U.S. Department of Defense for modeling the Terminal Ballistics of kinetic energy projectiles. For completeness, the performance of the neural network against single element targets is also revisited since more data are now available.

  • predicting the Terminal Ballistics of kinetic energy projectiles using artificial neural networks
    Journal of Information Systems Applied Research, 2013
    Co-Authors: John R Auten, Robert J Hammell
    Abstract:

    The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and vulnerability/lethality modeling & simulation. The current modeling and simulation methods being utilized often require significant amounts of time and subject matter expertise. This means that quick results cannot be provided to address new threats encountered in theatre. Recently, there has been an increased focus on rapid results for modeling and simulation efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper proposes the application of artificial neural networks to the prediction of the Terminal Ballistics of kinetic energy projectiles. By shifting the computational complexity of the problem to the fitting (regression) phase of the algorithm, the speed of the algorithm during an analysis is improved when compared to other Terminal ballistic models for kinetic energy projectiles. An improvement in overall analysis time can also be realized by removing the need for input preparation by a subject matter expert prior to using the algorithm for an analysis.

Addis Kidane - One of the best experts on this subject based on the ideXlab platform.

  • rigorous model based uncertainty quantification with application to Terminal Ballistics part ii systems with uncontrollable inputs and large scatter
    Journal of The Mechanics and Physics of Solids, 2012
    Co-Authors: Addis Kidane, A Lashgari, Mike Mckerns, M Ortiz, Houman Owhadi, G Ravichandran, Mark A Stalzer, T J Sullivan
    Abstract:

    This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quantification (UQ) protocol based on concentration-of-measure inequalities. Specific aims are to establish the feasibility of the protocol and its basic properties, including the tightness of the predictions afforded by the protocol. The assessment is based on an application to Terminal Ballistics and a specific system configuration consisting of 6061-T6 aluminum plates struck by spherical S-2 tool steel projectiles at ballistic impact speeds. The system's inputs are the plate thickness and impact velocity and the perforation area is chosen as the sole performance measure of the system. The objective of the UQ analysis is to certify the lethality of the projectile, i.e., that the projectile perforates the plate with high probability over a prespecified range of impact velocities and plate thicknesses. The net outcome of the UQ analysis is an M/U ratio, or confidence factor, of 2.93, indicative of a small probability of no perforation of the plate over its entire operating range. The high-confidence (>99.9%) in the successful operation of the system afforded the analysis and the small number of tests (40) required for the determination of the modeling-error diameter, establishes the feasibility of the DoD UQ protocol as a rigorous yet practical approach for model-based certification of complex systems.

  • verification and validation of the optimal transportation meshfree otm simulation of Terminal Ballistics
    International Journal of Impact Engineering, 2012
    Co-Authors: Addis Kidane, G Ravichandran, M Ortiz
    Abstract:

    We evaluate the performance of the OptimalTransportationMeshfree (OTM) method of Li et al. [21], suitably extended to account for seizing contact and fracture, in applications involving TerminalBallistics. The evaluation takes the form of a conventional Verification and Validation (V&V) analysis. In support of the validation analysis, we have conducted tests concerned with the normal impact of Aluminum alloy 6061-T6 thin plates by S2 tool steel spherical projectile over a range of plate thicknesses of [0.8 mm, 1.6 mm] and a range of impact velocities of [100, 400]m/s. The tests were conducted at Caltech’s GALCIT gas-gun Plate-Impact Facility. We find excellent agreement between measured and computed perforation areas and a ballistic limits over the thickness and velocity ranges considered. Our verification analysis consists of model-on-model comparisons and an assessment of the convergence of the OTM method. Specifically, we find excellent agreement between the incident vs. residual velocities predicted by the OTM method and by the power-law relation of Recht and Ipson [36]. We also find robust linear convergence of the OTM method as measured in terms of residual velocity error vs. number of nodes.

T J Sullivan - One of the best experts on this subject based on the ideXlab platform.

  • rigorous model based uncertainty quantification with application to Terminal Ballistics part ii systems with uncontrollable inputs and large scatter
    Journal of The Mechanics and Physics of Solids, 2012
    Co-Authors: Addis Kidane, A Lashgari, Mike Mckerns, M Ortiz, Houman Owhadi, G Ravichandran, Mark A Stalzer, T J Sullivan
    Abstract:

    This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quantification (UQ) protocol based on concentration-of-measure inequalities. Specific aims are to establish the feasibility of the protocol and its basic properties, including the tightness of the predictions afforded by the protocol. The assessment is based on an application to Terminal Ballistics and a specific system configuration consisting of 6061-T6 aluminum plates struck by spherical S-2 tool steel projectiles at ballistic impact speeds. The system's inputs are the plate thickness and impact velocity and the perforation area is chosen as the sole performance measure of the system. The objective of the UQ analysis is to certify the lethality of the projectile, i.e., that the projectile perforates the plate with high probability over a prespecified range of impact velocities and plate thicknesses. The net outcome of the UQ analysis is an M/U ratio, or confidence factor, of 2.93, indicative of a small probability of no perforation of the plate over its entire operating range. The high-confidence (>99.9%) in the successful operation of the system afforded the analysis and the small number of tests (40) required for the determination of the modeling-error diameter, establishes the feasibility of the DoD UQ protocol as a rigorous yet practical approach for model-based certification of complex systems.

John R Auten - One of the best experts on this subject based on the ideXlab platform.

  • predicting Terminal Ballistics using an iterative application of an artificial neural network
    2017 Computing Conference, 2017
    Co-Authors: John R Auten, Robert J Hammell
    Abstract:

    The need to accurately predict the Terminal Ballistics of kinetic energy projectiles in Vulnerability/Lethality models is of paramount importance to the U.S. Department of Defense. In previous work, an artificial neural network was trained on a set of 1,877 data points to predict perforation, residual velocity, and residual mass of a kinetic energy projectile impacting a target consisting of a single homogeneous-monolithic-metallic element. The capability of that neural network was extended to handle targets consisting of multiple homogeneous-monolithic-metallic elements by iteratively applying the artificial neural network on each element in the target and using the predicted residual properties as inputs for the next element. The performance of that approach is analyzed against a phenomenological model that is currently used by the U.S. Department of Defense for modeling the Terminal Ballistics of kinetic energy projectiles. For completeness, the performance of the neural network against single element targets is also revisited since more data are now available.

  • predicting the Terminal Ballistics of kinetic energy projectiles using artificial neural networks
    Journal of Information Systems Applied Research, 2013
    Co-Authors: John R Auten, Robert J Hammell
    Abstract:

    The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and vulnerability/lethality modeling & simulation. The current modeling and simulation methods being utilized often require significant amounts of time and subject matter expertise. This means that quick results cannot be provided to address new threats encountered in theatre. Recently, there has been an increased focus on rapid results for modeling and simulation efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper proposes the application of artificial neural networks to the prediction of the Terminal Ballistics of kinetic energy projectiles. By shifting the computational complexity of the problem to the fitting (regression) phase of the algorithm, the speed of the algorithm during an analysis is improved when compared to other Terminal ballistic models for kinetic energy projectiles. An improvement in overall analysis time can also be realized by removing the need for input preparation by a subject matter expert prior to using the algorithm for an analysis.

M Ortiz - One of the best experts on this subject based on the ideXlab platform.

  • rigorous model based uncertainty quantification with application to Terminal Ballistics part ii systems with uncontrollable inputs and large scatter
    Journal of The Mechanics and Physics of Solids, 2012
    Co-Authors: Addis Kidane, A Lashgari, Mike Mckerns, M Ortiz, Houman Owhadi, G Ravichandran, Mark A Stalzer, T J Sullivan
    Abstract:

    This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quantification (UQ) protocol based on concentration-of-measure inequalities. Specific aims are to establish the feasibility of the protocol and its basic properties, including the tightness of the predictions afforded by the protocol. The assessment is based on an application to Terminal Ballistics and a specific system configuration consisting of 6061-T6 aluminum plates struck by spherical S-2 tool steel projectiles at ballistic impact speeds. The system's inputs are the plate thickness and impact velocity and the perforation area is chosen as the sole performance measure of the system. The objective of the UQ analysis is to certify the lethality of the projectile, i.e., that the projectile perforates the plate with high probability over a prespecified range of impact velocities and plate thicknesses. The net outcome of the UQ analysis is an M/U ratio, or confidence factor, of 2.93, indicative of a small probability of no perforation of the plate over its entire operating range. The high-confidence (>99.9%) in the successful operation of the system afforded the analysis and the small number of tests (40) required for the determination of the modeling-error diameter, establishes the feasibility of the DoD UQ protocol as a rigorous yet practical approach for model-based certification of complex systems.

  • verification and validation of the optimal transportation meshfree otm simulation of Terminal Ballistics
    International Journal of Impact Engineering, 2012
    Co-Authors: Addis Kidane, G Ravichandran, M Ortiz
    Abstract:

    We evaluate the performance of the OptimalTransportationMeshfree (OTM) method of Li et al. [21], suitably extended to account for seizing contact and fracture, in applications involving TerminalBallistics. The evaluation takes the form of a conventional Verification and Validation (V&V) analysis. In support of the validation analysis, we have conducted tests concerned with the normal impact of Aluminum alloy 6061-T6 thin plates by S2 tool steel spherical projectile over a range of plate thicknesses of [0.8 mm, 1.6 mm] and a range of impact velocities of [100, 400]m/s. The tests were conducted at Caltech’s GALCIT gas-gun Plate-Impact Facility. We find excellent agreement between measured and computed perforation areas and a ballistic limits over the thickness and velocity ranges considered. Our verification analysis consists of model-on-model comparisons and an assessment of the convergence of the OTM method. Specifically, we find excellent agreement between the incident vs. residual velocities predicted by the OTM method and by the power-law relation of Recht and Ipson [36]. We also find robust linear convergence of the OTM method as measured in terms of residual velocity error vs. number of nodes.