One of the tasks performed routinely by the Electromagnetics and Plasma Physics Analysis Department at Sandia National Laboratories is analyzing the effects of direct-strike lightning on Faraday cages that protect sensitive items. The Faraday cages analyzed thus far have many features in common. This report is an attempt to collect equations and other information that have been routinely used in the past in order to facilitate future analysis.
The design of experiments (DOEx) approach was used to characterize the Precision Laser Beam Welding Process with respect to four processing factors: Angle of Attack, Volts, Pulse Length, and Focus. The experiment was performed with Lap Joints, Nickel-Wire Joints, and Kovar-Wire Joints. The laser welding process and these types of welds are used in the manufacture of MC4368A Neutron Generators. For each weld type an individual optimal condition and operating window was identified. The widths of the operating windows that were identified by experimentation indicate that the laser weld process is very robust. This is highly desirable because it means that the quality of the resulting welds is not sensitive to the exact values of the processing factors within the operating windows. Statistical process control techniques can be used to ensure that the processing factors stay well within the operating window.
An optical sensor system has been developed for the autonomous monitoring of NO{sub 2} evolution in energetic material aging studies. The system is minimally invasive, requiring only the presence of a small sensor film within the aging chamber. The sensor material is a perylene/PMMA film that is excited by a blue LED light source and the fluorescence detected with a CCD spectrometer. Detection of NO{sub 2} gas is done remotely through the glass window of the aging chamber. Irreversible reaction of NO{sub 2} with perylene, producing the non-fluorescent nitroperylene, provides the optical sensing scheme. The rate of fluorescence intensity loss over time can be modeled using a numerical solution to the coupled diffusion and a nonlinear chemical reaction problem to evaluate NO{sub 2} concentration levels. The light source, spectrometer, spectral acquisition, and data processing were controlled through a Labivew program run by a laptop PC. Due to the long times involved with materials aging studies the system was designed to turn on, warm up, acquire data, power itself off, then recycle at a specific time interval. This allowed the monitoring of aging HE material over the period of several weeks with minimal power consumption and stable LED light output. Despite inherent problems with gas leakage of the aging chamber they were able to test the sensor system in the field under an accelerated aging study of rocket propellant. They found that the propellant evolved NO{sub 2} at a rate that yielded a concentration of between 10 and 100 ppm. The sensor system further revealed that the propellant, over an aging period of 25 days, evolves NO{sub 2} with cyclic behavior between active and dormant periods.
Dynamic thermography is a promising technology for inspecting metallic and composite structures used in high-consequence industries. However, the reliability and inspection sensitivity of this technology has historically been limited by the need for extensive operator experience and the use of human judgment and visual acuity to detect flaws in the large volume of infrared image data collected. To overcome these limitations new automated data analysis algorithms and software is needed. The primary objectives of this research effort were to develop a data processing methodology that is tied to the underlying physics, which reduces or removes the data interpretation requirements, and which eliminates the need to look at significant numbers of data frames to determine if a flaw is present. Considering the strengths and weakness of previous research efforts, this research elected to couple both the temporal and spatial attributes of the surface temperature. Of the possible algorithms investigated, the best performing was a radiance weighted root mean square Laplacian metric that included a multiplicative surface effect correction factor and a novel spatio-temporal parametric model for data smoothing. This metric demonstrated the potential for detecting flaws smaller than 0.075 inch in inspection areas on the order of one square foot. Included in this report is the development of a thermal imaging model, a weighted least squares thermal data smoothing algorithm, simulation and experimental flaw detection results, and an overview of the ATAC (Automated Thermal Analysis Code) software that was developed to analyze thermal inspection data.
This report documents how active structural control was used to significantly enhance the metal removal rate of a milling machine. An active structural control system integrates actuators, sensors, a control law and a processor into a structure for the purpose of improving the dynamic characteristics of the structure. Sensors measure motion, and the control law, implemented in the processor, relates this motion to actuator forces. Closed-loop dynamics can be enhanced by proper control law design. Actuators and sensors were imbedded within a milling machine for the purpose of modifying dynamics in such a way that mechanical energy, produced during cutting, was absorbed. This limited the on-set of instabilities and allowed for greater depths of cut. Up to an order of magnitude improvement in metal removal rate was achieved using this system. Although demonstrations were very successful, the development of an industrial prototype awaits improvements in the technology. In particular, simpler system designs that assure controllability and observability and control algorithms that allow for adaptability need to be developed.
Many problems in aeronautics can be described in terms of nonlinear systems of equations. Carleman developed a technique to linearize such equations that could lead to analytical solutions of nonlinear problems. Nonlinear problems are difficult to solve in closed form and therefore the construction of such solutions is usually nontrivial. This research will apply the Carleman linearization technique to three model problems: a two-degree-of-freedom (2DOF) ballistic trajectory, Blasius' boundary layer, and Van der Pol's equation and evaluate how well the technique can adequately approximate the solutions of these ordinary differential equations.
This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerable preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.
A new scheme to simulate elastic collisions in particle simulation codes is presented. The new scheme aims at simulating the collisions in the highly collisional regime, in which particle simulation techniques typically become computationally expensive. The new scheme is based on the concept of a grid-based collision field. According to this scheme, the particles perform a single collision with the background grid during a time step. The properties of the background field are calculated from the moments of the distribution function accumulated on the grid. The collision operator is based on the Langevin equation. Based on comparisons with other methods, it is found that the Langevin method overestimates the collision frequency for dilute gases.
A pointing control system is developed and tested for a flying gimbaled telescope. The two-axis pointing system is capable of sub-microradian pointing stability and high accuracy in the presence of large host vehicle jitter. The telescope also has high agility - it is capable of a 50° retarget (in both axes simultaneously) in less than 2 s. To achieve the design specifications, high-accuracy, high-resolution, two-speed resolvers were used, resulting in gimbal-angle measurements stable to 1.5 μrad. In addition, on-axis inertial angle displacement sensors were mounted on the telescope to provide host-vehicle jitter cancellation. The inertial angle sensors are accurate to about 100 nrad, but do not measure low-frequency displacements below 2 Hz. The gimbal command signal includes host-vehicle attitude information, which is band-limited. This provides jitter data below 20 Hz, but includes a variable latency between 15 and 25 ms. One of the most challenging aspects of this design was to combine the inertial-angle-sensor data with the less perfect information in the command signal to achieve maximum jitter reduction. The optimum blending of these two signals, along with the feedback compensation were designed using Quantitative Feedback Theory.
This report describes the underlying principles and goals of the Sandia ASCI Verification and Validation Program Validation Metrics Project. It also gives a technical description of two case studies, one in structural dynamics and the other in thermomechanics, that serve to focus the technical work of the project in Fiscal Year 2001.
This report documents an exploratory FY 00 LDRD project that sought to demonstrate the first steps toward a realistic computational representation of the variability encountered in individual human behavior. Realism, as conceptualized in this project, required that the human representation address the underlying psychological, cultural, physiological, and environmental stressors. The present report outlines the researchers' approach to representing cognitive, cultural, and physiological variability of an individual in an ambiguous situation while faced with a high-consequence decision that would greatly impact subsequent events. The present project was framed around a sensor-shooter scenario as a soldier interacts with an unexpected target (two young Iraqi girls). A software model of the ''Sensor Shooter'' scenario from Desert Storm was developed in which the framework consisted of a computational instantiation of Recognition Primed Decision Making in the context of a Naturalistic Decision Making model [1]. Recognition Primed Decision Making was augmented with an underlying foundation based on our current understanding of human neurophysiology and its relationship to human cognitive processes. While the Gulf War scenario that constitutes the framework for the Sensor Shooter prototype is highly specific, the human decision architecture and the subsequent simulation are applicable to other problems similar in concept, intensity, and degree of uncertainty. The goal was to provide initial steps toward a computational representation of human variability in cultural, cognitive, and physiological state in order to attain a better understanding of the full depth of human decision-making processes in the context of ambiguity, novelty, and heightened arousal.
Virtual Private Networking is a new communications technology that promises lower cost, more secure wide area communications by leveraging public networks such as the Internet. Sandia National Laboratories has embraced the technology for interconnecting remote sites to Sandia's corporate network, and for enabling remote access users for both dial-up and broadband access.
Public concern regarding the effects of noise generated by the detonation of excess and obsolete explosive munitions at U.S. Army demolition ranges is a continuing issue for the Army's demilitarization and disposal groups. Recent concerns of citizens living near the McAlester Army Ammunition Plant (MCAAP) in Oklahoma have lead the U.S. Army Defense Ammunition Center (DAC) to conduct a demonstration and evaluation of noise abatement techniques that could be applied to the MCAAP demolition range. With the support of the DAC, MCAAP, and Sandia National Laboratories (SNL), three types of noise abatement techniques were applied: aqueous foams, overburden (using combinations of sand beds and dirt coverings), and rubber or steel blast mats. Eight test configurations were studied and twenty-four experiments were conducted on the MCAAP demolition range in July of 2000. Instrumentation and data acquisition systems were fielded for the collection of near-field blast pressures, far-field acoustic pressures, plant boundary seismic signals, and demolition range meteorological conditions. The resulting data has been analyzed and reported, and a ranking of each technique's effects has been provided to the DAC.
A simplified and bounding methodology for analyzing the pressure buildup and hydrogen concentration within an unvented 2R container was developed (the 2R is a sealed container within a 6M package). The specific case studied was the gas buildup due to alpha radiolysis of water moisture sorbed on small quantities (less than 20 Ci per package) of plutonium oxide. Analytical solutions for gas pressure buildup and hydrogen concentration within the unvented 2R container were developed. Key results indicated that internal pressure buildup would not be significant for a wide range of conditions. Hydrogen concentrations should also be minimal but are difficult to quantify due to a large variation/uncertainty in model parameters. Additional assurance of non-flammability can be obtained by the use of an inert backfill gas in the 2R container.
The efficiency of the design-to-analysis process for translating solid-model-based design data to computational analysis model data plays a central role in the application of computational analysis to engineering design and certification. A review of the literature from within Sandia as well as from industry shows that the design-to-analysis process involves a number of complex organizational and technological issues. This study focuses on the design-to-analysis process from a business process standpoint and is intended to generate discussion regarding this important issue. Observations obtained from Sandia staff member and management interviews suggest that the current Sandia design-to-analysis process is not mature and that this cross-organizational issue requires committed high-level ownership. A key recommendation of the study is that additional resources should be provided to the computer aided design organizations to support design-to-analysis. A robust community of practice is also needed to continuously improve the design-to-analysis process and to provide a corporate perspective.
A Synthetic Aperture Radar (SAR) image is a two-dimensional projection of the radar reflectivity from a 3-dimensional object or scene. Stereoscopic SAR employs two SAR images from distinct flight paths that can be processed together to extract information of the third collapsed dimension (typically height) with some degree of accuracy. However, more than two SAR images of the same scene can similarly be processed to further improve height accuracy, and hence 3-dimensional position accuracy. This report shows how.