Allen R. Curran

Ground target infrared signature modeling with the multiservice electro-optic signature (MuSES) code

With an increased reliance on modeling and simulation in the defense community a requirement has developed for improved ground target infrared signature prediction capabilities. Predictive ground target infrared signature modeling has traditionally been done using the Physically Reasonable Infrared Signature Model (PRISM). The PRISM code has been used extensively in support of signature management for vehicle designers as well as other applications. The intended replacement for PRISM, the Multi-Service Electro-optic Signature (MuSES) code, has recently been developed and offers increased capabilities and ease of use. Until recently, IR/thermal signature analysis suffered from a disparity between the geometry required to predict signatures and the geometry used to design vehicles. The solution to the IR geometry problem was the development of MuSES, which uses meshed CAD geometry. MuSES is a rapid prototyping thermal design tool and an infrared signature prediction tool. To restore modularity lost over ten years of PRISM evolution, a new object-oriented thermal solver was created. The solver incorporates numerous advanced features including a net enclosure method for radiation, CFD interface, restart/seed capability, batch mode, and alternate solution strategies (such as the partial direct solution method). The MuSES interface is optimized for engineers/analysts who need to incorporate signature management treatments or heat management solutions into vehicle designs. Topics covered by this paper include a detailed description of the MuSES code and its capabilities, as well as multiple examples of model creation. The geometry modeling paradigm for the MuSES code represents a radical shift in how a vehicle model is created for the purpose of infrared signature modeling. The model creation examples are presented to demonstrate the tools and techniques used as well as to convey lessons learned to potential users in proper geometry modeling and meshing techniques.

Development of a signature supercode

This paper reviews current and future signature modeling activities at KRC and TACOM. PRISM (Physically Reasonable Infrared Signature Model) and its associated modeling tools are discussed along with the implementation of the physical principles that will evolve into the SuperCode. By continuing the current efforts with PRISM and then forming a SuperCode Research Consortium to implement additional advanced features, a universal code will be available to the modeling community.

A thermo-physiological description of a 50th percentile Western female.

Thermophysiological models are used to predict thermal sensation, thermal comfort and human effectiveness for a wide range of environmental conditions. Typically, such models are based on the anatomy and physiological responses of an adult male. The objective of this study was to develop an adult female model and test it against experimental results from the literature.

Simulation-based sensor modeling and at-range target detection characterization with MuSES

Accurate infrared signature prediction of targets, such as humans or ground vehicles, depends primarily on the realistic prediction of physical temperatures. Thermal model development typically requires a geometric description of the target (i.e., a 3D surface mesh) along with material properties for characterizing the thermal response to simulated weather conditions. Once an accurate thermal solution has been obtained, signature predictions for an EO/IR spectral waveband can be generated. The image rendering algorithm should consider the radiative emissions, diffuse/specular reflections, and atmospheric effects to depict how an object in a natural scene would be perceived by an EO/IR sensor. The EO/IR rendering process within MuSES, developed by ThermoAnalytics, can be used to create a synthetic radiance image that predicts the energy detected by a specific sensor just prior to passing through its optics. For additional realism, blurring due to lens diffraction and noise due to variations in photon detection can also be included, via specification of sensor characteristics. Additionally, probability of detection can be obtained via the Targeting Task Performance (TTP) metric, making it possible to predict a target’s at-range detectability to a particular threat sensor. In this paper, we will investigate the at-range contrast and detectability of some example targets and examine the effect of various techniques such as sub-pixel sampling and target pixel thresholding.

Thermoregulation in Humans: Results From an Anatomically-Based, High-Resolution Voxelized Model

The ability to predict local surface and internal temperatures in humans subjected to various environmental and direct thermal loads has applications which include assessment of human exposure to radio frequency radiation (RFR) from mobile phones [1], medical imaging technologies [2] and mild-temperature hyperthermia (MTH) treatment for some cancers [3].Copyright