Owen J. Eslinger

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization

In this paper we describe some algorithms for noisy optimization in the context of problems from the gas transmission industry. The algorithms are implicit filtering, DIRECT, and a new hybrid of these methods, which uses DIRECT to find an intitial iterate for implicit filtering. We report on numerical results that illustrate the performance of the methods.

Omicron: Rapid Mesh Generation on HPC Platforms for the Study of Near Surface Phenomena with Remote Sensing

A technique is presented for rapidly producing unstructured finite element meshes in support of large-scale remote sensing simulations. These tetrahedral meshes typically have more than one million elements and more than 250 thousand nodes, and allow for arbitrary placement of objects into the domain. Open-source mesh generation packages are used in conjunction with a tetrahedra element smoothing operation to achieve the desired final meshes. Meshes can be reproduced in less than 30 minutes on a CrayXT3 architecture.

Computational Model Builder (CMB): A Cross-Platform Suite of Tools for Model Creation and Setup

The US Army Engineer Research and Development Center researches, develops, supports, and maintains many discipline-specific numerical models. Because of the increasing domain size and resolution needs of the modeling applications, it is no longer feasible to execute the pre- and post-processing of the models, such as mesh generation, boundary condition assignment, and visualization, on a single-processor desktop computer. Thus, a cross-platform suite of tools, the Computational Model Builder (CMB), is being developed to allow modelers to create scenes, generate solids from the scenes, and assign boundary conditions and materials on the solids. This greatly reduces the memory footprint of the problem from that required for manipulation of a full three-dimensional mesh, allowing pre- and postprocessing to be performed easily on a desktop. The tagged geometry can then be sent to HPC machines for mesh generation. This paper will discuss the requirements and design decisions for the CMB framework.

Integrated High-Fidelity Geoscience Simulations for Enhanced Terrain-Related Target Detection

A loosely coupled suite of physics-based geotechnical, geospatial, hydrogeologic, and thermal simulations are integrated into a virtual testing facility aimed at resolving terrain-related warfighter problems. This article describes the key aspects of the research conducted by the US Institute for Maneuverability and Terrain Physics Simulation and provides two examples of the virtual testing facility applied to subsurface threat detection.

Modeling Subsurface Phenomena for Tetrahedral Meshes

SceneGen is a set of tools for creating a subsurface mesh with very little human interaction. The tool set created allows the user to incorporate structured lattice data, typically generated from geo-statistical packages, and make a smoothed boundary representation (b-rep) for each of its material regions. Tools have also been created to take ground surface height-field data and create b-reps for Boolean combination with the geostatistical information. Constructive solid geometry (CSG) techniques are being developed to add subsurface items such as rocks and debris and incorporate them into the model as well. This subsurface system of material boundary regions is then capable of being meshed for finite element simulations. In this way, multiple unstructured tetrahedral meshes may be simultaneously generated on high performance computers

Examining the sensitivity of simulated surface temperatures due to meteorological conditions

The U.S. Army Engineer Research and Development Center (ERDC) has developed a suite of models that replicate the signicant geo-physical processes which aect the thermal signatures sensed by infrared imaging systems. This suite of models also includes an electro-optical/infrared (EO/IR) sensor model that produces synthetic thermal imagery. The EO/IR sensor model can be adapted to replicate the performance of other infrared sensor systems as well. It is well known that eld-collected IR imagery can be in uenced by the micro-topographic features of a particular location. As a result, the performance of automated target recognition algorithms and decisions based on their results can also be aected. Other signicant contributors to false alarms and issues with probabilities-of- detection include the relative locations of vegetation and local changes in soil types or properties. For example, a change in the retention of soil moisture alone is known to contribute to false alarms due to changes in radiative and thermal properties of wet versus dry soil. Many aspects of eld data collection eorts (weather, soil uniformity, etc.) cannot be controlled nor changed after the fact. Within a computational framework, however, plant and object locations, as well as weather patterns can, all be changed. In this work, the sensitivity of simulated IR imagery will be examined as it relates to initial states and boundary forcing terms due to weather conditions. Dierent approaches to these inputs will be examined using the computational testbed developed at the ERDC.

Overview of computational testbed for evaluating electro-optical/infrared sensor systems

The U.S. Army Engineer Research and Development Center (ERDC) developed a near-surface computational testbed (CTB) for modeling geo-environments. This modeling capability is used to predict and improve the performance of current and future-force sensor systems for surface and near-surface threat detection for a wide range of geoenvironments. The CTB is a suite of integrated models and tools used to approximately replicate geo-physical processes such as radiometry, meteorology, moisture transport, and thermal transport that influence the resultant signatures of both natural and man-made materials, as perceived by the sensors. The CTB is designed within a High Performance Computing (HPC) framework to accommodate the size and complexity of the virtual environments required for analyzing and quantifying sensor performance. Specifically, as a rule-of-thumb, the size of the scene should encompass an area that is at a minimum, the size of the spatial coverage of the sensor. This HPC capability allows the CTB to replicate geophysical processes and subsurface heterogeneity with high levels of realism and to provide new insight into identifying the geophysical processes and environmental factors that significantly affect the signatures sensed by multispectral imaging, near-infrared, mid-wave infrared, long-wave infrared, and ground penetrating radar sensors. Additionally, this effort is helping to quantify the performance and optimal time-of-use for sensors to detect threats within highly heterogeneous geo-environments by reducing false alarms from automated target recognition algorithms.

Signature Evaluation for Thermal Infrared Countermine and IED Detection Systems: Large-Area Simulations in an Operational Computing Environment

The countermine test bed (CTB) and accompanying tools provide a means to optimize thermal infrared sensor systems and automated target recognition algorithms. The CTB has been validated through a series of studies conducted since 2006. During that time, the capability of the CTB has been vastly expanded, particularly in regards to the size of the domain that can be modeled. The CTB consists of four independent models that are coupled through file transfers. Optimization of the system involves a scheduling problem whereby the processors are assigned to individual sub-models in accordance with their run times. The ground model and the ray caster dominate computations, with the other sub-models operating virtually as background processes.

Apparent soil thermal diffusivity determinatior method for use in thermal modeling

The purpose of this study is to develop a stable apparent soil thermal diffusivity determination method for long- term scenarios that can be applied to thermal modeling. The apparent soil thermal diffusivities for three different data collections were calculated using two different analytical methods known as the amplitude and logarithmic methods. The three data collections were controlled and in-situ measurements gathered from a dirt-filled bucket and two separate arid, desert regions, respectively. Results for the data collected from the dirt- filled bucket show analytical stability for depths of 2 cm - 16 cm, while results for the first arid, desert region show an apparent possibility for analytical soil layer change detection. Results for the second arid, desert region showed relative analytical stability with some larger variations occurring during seasonal changes. As a whole, the results strongly suggest that a stable apparent soil thermal diffusivity determination method has been developed that can be used to study long-term scenarios and aid in the thermal modeling of regions similar to those in this study.

A rapid meshing technique for simulations of near- surface phenomena involving remote sensing technology Large-scale meshing for remote sensing simulations

A technique is presented for rapidly producing finite element meshes in support of large-scale remote sensing simulations. These unstructured tetrahedral meshes typically have more than one million elements and more than 250 thousand nodes, and allow for arbitrary placement of objects into the scene. They can be reproduced in less than 30 minutes on a Cray XT3 architecture. Open-source mesh generation packages are used in conjunction with a tetrahedra element smoothing operation to achieve the desired final meshes. The resulting generated meshes used by a suite of thermal models and corresponding meteorological inputs provide the spatial distribution of near-surface thermal exitance.