Artificial intelligence and machine learning algorithms for object detection in the infrared require an extensive amount of high-resolution object-tagged thermal infrared images. Often, acquiring real imagery of sufficient size and range of environmental conditions is difficult due to the cost and time. To address this need, the current study has developed a novel computational framework, i.e. the Sensor Engine, that generates target-tagged synthetic infrared imagery of large complex natural environments. This computational framework, coupled with high-fidelity soil and vegetation thermal physics and geometry models, generates synthetic, high-definition infrared images tailored for High-Performance Computing (HPC) systems. A unique plugin mechanism used to load and unload configured infrared sensors at run time in addition to allowing the framework to effectively work with different sensors in parallel is also discussed. The sensor model within the Sensor Engine communicates with another computational framework to acquire radiative energy for each sensor pixel detector as well as material, distance, source location, and incident angle. To demonstrate the modus operandi of this computational framework, an evaluation and discussion of runtime message passing and test cases are provided.
For realistic synthetic imagery, radiative transfer methods coupled with large mesh geometry provide the most scientifically accurate way to model a scene. Radiative models typically use ray-tracing techniques to determine where radiative energy is coming from or moving to. This work presents an approach to making a ray query Geometry Engine that actively stores large-scale, terabyte-sized geometry in out-of-core memory on parallel general purpose processors. Procedures for geometry distribution, structures for efficient ray-tracing, and the ray query API are discussed. Geometry distribution uses Morton codes with parallel sorting routines to create geometry scene-chunks that are distributed among processing nodes. Each scene chunk is then broken down using a bounding volume hierarchy (BVH) using axis-aligned bounding boxes (AABB). The BVH allows for efficient ray tracing of the geometry. The ray query engine API allows client-side programs, such as sensor models and radiative transfer models, which exist on the same high performance computer to efficiently identify intersected geometry given directed rays and collect individual geometry elements. The geometry has key values that can uniquely identify data from solver programs. Scalability, partition timing, and ray timing results are presented.
Many bio-structures, such as the paddlefish rostrum, owe their remarkable resistance to permanent deformation to an optimized arrangement of hard and soft materials. This study utilizes the unique characteristics seen in biological systems to determine the optimized composition of hard and soft materials to develop an enhanced damping mechanism for dynamic load resistance. This work develops novel 3D printed prototypes inspired by the material composition of the paddlefish rostrum. The design-test-build cycle of the prototypes will consist of numerical analyses to inform the experimental boundary conditions and multi-material configuration. In biological systems, the boundary conditions determine an optimized material configuration. This study consists of quasi-static flexure experiments under different load and displacement boundary conditions to determine the optimized configuration for the given boundary condition. This investigation compares the prototypes' deformation, load transfer distribution, shear capacity, and the optimized material configuration per specific load and displacement boundary conditions against other samples with single material properties. When compared to isotropic materials currently in use, bio-inspired, multi-material structures demonstrate an enhanced stress to deformation performance . The study also determined the best material layup for the 3D printed prototype for each of the load and displacement boundary conditions.
Lightweight structures are in demand in many application areas since they provide an optimum use of available resources. For example, in the transportation and aviation industries, lightweight structures increase the energy efficiency. However, lightweight structures need to have adequate strength for their safe usage. This work investigates the performance of novel proof-of-concept structural systems developed based on the rostrum (snout) of the paddlefish. Numerical experiments are conducted on the conceptual prototypes of bioinspired, energy- dissipative mechanical system models with different lattice patterns that mimic those found in the rostrum of the paddlefish. The performance of the models is quantified in terms of deformations and maximum principal stresses experienced by the model under a blast load using fixed plate and cantilever beam boundary conditions. The bioinspired models showed identical trends of stress and deformation. However, the heterogeneous bioinspired structures showed a decrease of 30% in deformation and experienced lower stresses as compared to a structure with identical geometry and homogeneous material properties.