Army 8. Small Business Innovation Research (sbir) Proposal Submission Instructions
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With the commercial partner, develop the required implementation plan to transition the technology and show the benefits of the higher fidelity Mg-based alloy in the specific application selected. REFERENCES: 1. P. Fu, L. Peng, H. Jiang, W. Ding, and C. Zhai, “Tensile properties of high strength cast Mg alloys at room temperature: A review,” China Foundry 11 (2014) 277-286. 2. M. Gupta and W.L.E. Wong, "Magnesium based nanocomposites: Lightweight materials of the future," Materials Characterization 105 (2015) 30-46. 3. V. Hammond, "Magnesium Nanocomposites: Current status and prospects for Army applications," US Army Laboratory Tech Report, ARL-TR-5728, September 2011. 4. A. Khandelwal, K. Mani, N. Srivastava, R. Gupta, and G.P. Chaudhari, "Mechanical behavior of AZ31/Al2O3 magnesium alloy nanocomposites prepared using ultrasound assisted stir casting," Composites B123 (2017) 64-73. 5. A. Luo, “Magnesium casting technology for structural applications,” Journal of Magnesium and Alloys 1 (2013) 2-22. 6. A. Luo, “Recent magnesium alloy development for elevated temperature applications,” International Materials Review 49 (2004) 13-30. 7. J.F. Nie, "Effects of precipitate strength and orientation on dispersion strengthening in magnesium alloys," Scripta Materialia 48 (2003) 1009-1015. 8. J.F. Nie, “Precipitation and hardening in magnesium alloys,” Metallurgical and Materials Transactions A43 (2012) 3891-39 KEYWORDS: magnesium, casting, texture, microstructure, mechanical properties, high strain rates
TECHNOLOGY AREA(S): Information Systems OBJECTIVE: Develop visual analytics tool to support collaborative decision making with high-dimensional variables using 2D, 2 1/2D, and 3D visualization with interaction for demonstration. DESCRIPTION: Dynamic Collaborative Visualization Ecosystem (DynaCoVE) is a new visualization tool that will support a data-centric, user-centric, visualization algorithm and systems agnostic visualization. It is a visualization software that will allow the user to generate visualization from the data and display it on any of the display systems available in the visualization ecosystem for knowledge discovery and exploration. The display system selected will also support interactive interaction of the visualization created. DynaCoVE will be configured with different visualization systems capable of 2D, 2 1/2D, and 3D display supporting fully-immersive, semi-immersive, and non-immersive visualization. Auditory output, together with touch, 2D, and 3D interaction for the appropriate display system will also be needed to support interactive visual analytics process [1]. The Army needs to analyze and correlate heterogeneous data from multiple sources has created a visual analytics challenges that cannot be addressed by a single type of visualization system. The Army Testing and Evaluation community together with various Army groups working on physics based simulation are getting overwhelmed with heterogeneous big data problems. A hybrid visualization system capable of combining the benefits of both immersive and non-immersive visualization to create a seamless 2D and 3D environment that supports information-rich analysis would overcome some of the challenges [2][3][4]. DynaCoVE will be a visual analytics tool used to develop situational understanding by managing complex visualization ecosystem that will develop and sustain a high degree of situational understanding while operating in complex environments against determined, adaptive enemy organizations. DynaCoVE will also be a visual analytics tool used to set the theater, sustain operations, and maintain freedom of movement. DynaCoVE novel visual analytics tool will provide strategic agility to the joint force, and maintain freedom of movement and action during sustained and high tempo operations at the end of extended lines of communication in austere environments. We seek novel development in a visualization ecology capable of visualizing heterogeneous data with full interaction on the appropriate visualization system. In a typical use case, the user will upload simulation data or sensor data and create a visualization using one of the available visualization techniques. Once created, the user will have the option to push the visualization to one of the display systems that is appropriate for the type of visualization created. For example, if the user selected a fully-immersive display system to visualize a 3D simulation data, the user will be able to walk over to the fully-immersive display system and be fully immersed in the simulation data. Using 3D interaction techniques, the user will then be able to interact with the data in 3D to study and gain new understanding from the process. The user can also create a 2D cut plane of the 3D simulation data and push the data to a touch enabled 2D display to visualize and interact with the data using the touch interface. DynaCoVE will be the realization of an interactive ecosystem of devices, humans, and software that will provide a framework for which a renewed study of the meaning of interaction and computation can be achieved and redefine visual analytics. The applicability of such a system will provides new understanding to data science. Challenges in this topic include complex melding of various visualization systems, visualization techniques, and interaction techniques needed to create a seamless and dynamic visualization environment from multiple spatially aware displays that can evolve over time. Creating interaction mechanisms by using crowd-aware, and context-aware technologies to facilitate communication within the community of devices, and individuals that form the visualization ecosystem can also be equally challenging. PHASE I: Develop feasible concepts and provide a proven methodology within a software design framework to demonstrate a DynaCoVE system to support visual analytics. The deliverables should include a conceptual design for the complete DynaCoVE and a working proof of the design that clearly reflects novel instantiations of the supporting visual analytics approach. Phase II plans should also be provided, to include key component milestones and plans for testing and validation. PHASE II: Develop, demonstrate, and validate a working prototype system for a limited set of display systems (but from different display categories) based on the preliminary design from Phase I. All appropriate usability and engineering testing will be performed to finalize the design. Human factor (usability) study will need to be rigorously performed to demonstrate the usability of the prototype system designed. Phase II deliverables will include a working prototype of the system, specification for its development, and a demonstration of the visual analytics tool. PHASE III DUAL USE APPLICATIONS: Follow-on activities are expected to be aggressively pursued by the offeror, namely in seeking opportunities to integrate the visualization hardware, visualization techniques, data analytics algorithms, and protocols of the developed plug-and-play approach into DynaCoVE visual analytics platforms. Commercial benefits for DynaCoVE are enormous as many existing visual analytics tools capable of supporting heterogeneous visual analysis using heterogeneous display ecosystem are none existence with many big players from the academia and commercial sector working towards prototyping a similar tool. Similar to existing visual analytics tools, getting customer data into the tool will generate unlimited potential for consulting opportunity with the adaptation of the tool. REFERENCES: 1. Endert, A., Hossain, M., Ramakrishnan, N., North, C., Fiaux, P., Andrews, C., "The human is the loop: new directions for visual analytics.", Journal of Intelligent Information Systems, pp. 1-25, 2014 2. Su, S., Chaudhary, A., O'Leary, P., Geveci, B., Sherman, W., Neito, H., Francisco-Revilla, L., Virtual reality enabled scientific visualization workflow, 2015 IEEE 1st workshop on Everyday Virtual Reality (WEVR), 23 March 2015. 3. Febretti, A., Nishimoto, A., Thigpen, T., Talandis, J., Long, L., Pirtle, JD, Peterka, T., Verlo, A., Brown, M., Plepys, D., Sandin, D., Renambot, L., Johnson, A., Leigh, J., CAVE2: A Hybrid Reality Environment for Immersive Simulation and Information Analysis, Proceedings of IS&T / SPIE Electronic Imaging, The Engineering Reality of Virtual Reality 2013, San Francisco, CA, February 4, 2013 4. Kobayashi, D., Su, S., Bravo, L., Leigh, J., Shires, D., ParaSAGE: Scalable Web-based Scientific Visualization for Ultra Resolution Display Environment, IEEE Visualization 2016, Poster, 23-28 October 2016, Baltimore, Maryland, USA KEYWORDS: Visual Analytics, Immersive Visualization, Non-immersive Visualization, Semi-immersive Visualization, Visualization Ecology.
TECHNOLOGY AREA(S): Materials/Processes OBJECTIVE: Develop methods to integrate multiple precursor chemistries and engineered nanoparticles into a plasma enhanced chemical vapor deposition (PECVD) system which can operate at room temperature and atmospheric pressure. DESCRIPTION: Recent advancements in the field of atmospheric pressure plasma systems, including both afterglow and direct barrier discharge plasmas, have enabled the investigation of thin coatings via plasma enhanced chemical vapor deposition (PECVD), in which electromagnetic fields are used to induce and control gas phase chemical reactions.[1] These systems have demonstrated the ability to treat material surfaces at room temperatures and across several square feet of area, cleaning them or depositing conformal coatings via PECVD with very little thermal damage to even delicate materials or surface microstructures [2-3]. Most of these systems, however, utilize only a single-stream, large area treatment head (typically a slot or showerhead), achieving good lateral uniformity but restricting the ability to controllably mix chemical precursors in the reaction, to pattern deposition on the substrate, or to integrate engineered nanoparticles into the growing films. Significant research efforts in the past two decades within the Department of Defense (DOD), industry, and academia have also resulted in the ability to design and synthesize a broad suite of nanoparticles with tailored optical, chemical, and magnetic properties. Applications include the investigation of biological processes, the targeting of cancer therapies, selective absorption of light in solar cells and sensors, and the control of chemical and mechanical processes in nanoscale composite materials [4]. Significant challenges have arisen, however, in the controlled delivery and integration of these particles into useful coatings on realistic size scales. The Army needs the capability to integrate these two emerging technologies – selective non-equilibrium plasma deposition and engineered nanoparticles – to develop multifunctional, responsive, and adaptable thin film coating systems to enhance soldier and vehicle protection. The ability to independently and selectively react multiple gas-phase precursors would create a new capability for Army materials research – a rapid prototyping foundry – to develop multicomponent/multifunctional coatings with engineered environmental interactions, selective and reconfigurable optical properties, tailored energy-absorbing adhesive surfaces, or conformal coatings to enhance the bioresistance or fire retardancy of fabrics. Independent control of the plasma energy applied to each precursor would allow researchers to selectively produce particular gas phase radicals and then combine them at the substrate, enhancing control over film composition, morphology, and resulting functionality. If in addition the individual flows were laterally constrained, one could create a patterned surface, with control over the composition of each surface feature, integrating polymers, biomaterials, organosiloxanes, or engineered nanoparticles in a multitude of synergistic ways. PHASE I: Design concept for delivery of multiple precursor flows to enable the co-deposition of at least 3 separate components (two gas phase plasma reactors, one nanoparticle delivery stream) at atmospheric conditions. The plasma reactors should have independent flow rate control for each individual constituent, and should be able to utilize helium or argon as a primary gas, with the addition of a secondary reactive gas like oxygen at adjustable ratios. If possible, the use of air as a primary gas should also be considered. The concept should include the ability to deliver a flow of dry or wet nanoparticles such as Au to the growing surface during deposition. Demonstrate, build and deliver bench-scale prototype of three-stream system capable of simultaneously depositing 1 micrometer thick organosiloxane coatings and metallic nanoparticles uniformly over an area at least 1” X 1”. PHASE II: Build fully-functional prototype system capable of being integrated into an autonomous robotic system and demonstrate continuous, uniform deposition across substrates of varying sizes and shapes, up to 24” X 24”. Include capability to laterally constrain precursor/nanoparticle arrival to areas <5mm in diameter at the point of deposition on the substrate, and demonstrate the ability to deposit small spots, continuous lines, or patterned surfaces with independent incorporation of two PECVD precursors and metallic nanoparticles. Develop integrated process controls for the plasma head and power supplies with a programmable plug and play system that can be operated by both research and industry personnel, or modified by Army personnel to develop custom recipes for particular application areas. PHASE III DUAL USE APPLICATIONS: Follow-on activities are expected to be aggressively pursued by the offeror, namely in seeking opportunities to integrate the hardware, software, and protocols of the developed prototype into commercial systems for the microelectronics and medical communities, as well as defense applications. Such systems would be actively sought by researchers in academia and industry as a means to investigate the functionality of multicomponent thin film systems. REFERENCES: 1. Pappas, D., “Status and Potential of Atmospheric Plasma Processing of Materials”, J. Vac. Sci. and Tech. A, 2011, 29, 020801. 2. Zhang, H. et al., “Deposition of Silicon Oxide by Atmospheric Plasma Jet for Oxygen Diffusion Barrier Applications”, Thin Solid Films, 2016, 615, 63-68. 3. Cavallin, T. et al., “Metal PVD Honey-Combs Coated with TiO2 and Al2O3 via PECVD Suitable for Sensoring Applications”, Surf. Coat. And Tech., 2013, 230, 66-72. 4. Jiang, C. et al., “A Review on the Application of Inorganic Nanoparticles in Chemical Surface Coatings on Metallic Substrates”, Royal Soc. Of Chem. ADV, 2017, 7, 7531-7539.Hilt, F. et al, “Efficient Flame Retardant Thin Films Synthesized by Atmospheric Pressure PECVD Through the High Co-deposition Rate if Hexamethyldisiloxane and Triethylphosphate on Polycarbonate and Polyamide-6 Substrates”, ACS Appl. Mater. Interfaces, 2016, 8, 12422-12433. KEYWORDS: Atmospheric Plasma, Hybrid Coatings, Additive Manufacturing, Nanoparticles, Thin-Film Deposition, Soldier Protection, manufacturing process, manufacturing coatings, manufacturing equipment
TECHNOLOGY AREA(S): Air Platform OBJECTIVE: Machine Learning system utilizing real-time analysis of Health and Usage Management Systems data for predicting allowable control parameters enabling mission completion under degraded system performance. DESCRIPTION: The Army is seeking novel approaches implemented in a “Black Box”, to receive and analyze heterogeneous real-time sensor data (e.g. unsynchronized data from different types of sensor signals) to enable situational response to maximize in-situ operational performance and to complete mission requirements. The “Black Box” is a device that ingests mission parameters, sensor outputs and/or Health and Usage Management Systems (HUMS) data from a defined system of interest. The output of the “Black Box” are the system executable set of control parameters to meet preset mission requirements. Under normal operation with full system capability the control parameters may be generated for maximum overall performance however upon recognizing a subsystem failure or reduced performance, the “Black Box” will provide modified control parameters minimizing the impact of the affected subsystem. The system monitored by the “Black Box” can be as complex as an unmanned autonomous system or a subcomponent within the system (e.g. transmission, gear box, engine, structure, electronic power). Research and approaches to achieve the objective should include Implementation of new or existing Machine Learning algorithms into an Integrated programmable software-hardware “Black Box”. Technology for this capability may include state-of-the-art data-networking and high performance neural enabled hardware. The proof-of-concept will initially be directed toward monitoring input data from structural or power transfer components (e.g. transmissions or gear sets for rotorcrafts and ground vehicles) and subsequent modification of control when an anomaly is detected. For example, the “Black Box” will operate in a learning capacity during normal state of operation, however upon detection of a failure or precursor to failure the “Black Box” will provide user options to limit the available power applied to a failing gear set to maintain mission effectiveness or safe return. The size, weight, and power requirement of the “Black Box” should not exceed that of a state-of-the-art HUMS for the selected demonstration. The “Black Box” concept is intended to be saleable from a simple to a complex system of Army interest and easily be transitioned to commercial applications in the automotive or aerospace industry. PHASE I: Define innovative approaches for enabling near-real-time assessment and prediction of remaining serviceable life of a simple system (e.g. structural, mechanical systems or subsystems relevant to one or more categories of ground, air, and autonomous vehicles). These approaches will utilize Machine Learning hardware and software to evaluate real-time sensor data in conjunction with surrogate (proposed) or historical time-data benchmarks to provide modified control parameters (e.g. for structural or mechanical systems). Hardware, software, and combined approaches should be considered. (e.g. high-throughput CPU’s (central processing units) designed for neural engines, implementations of machine learning algorithms based on novel hardware, etc.) PHASE II: Establish and expand the Phase I proof-of-concept through the development, testing, and demonstration of a “Black Box” system that will capture and process near-real time data for a proposed Army system component (e.g. structural, mechanical and propulsion systems on an unmanned air vehicle or a ground combat vehicle). The system will provide modified operational control parameters to adjust the flight or driving patterns to extend usable system life and meet mission objectives. The form factor for the “Black Box” shall not exceed that of a state-of-the-art HUMS on the proposed system component demonstrator. PHASE III DUAL USE APPLICATIONS: Provide an adaptive and saleable “Black Box” capable of real-time monitoring and situational response applicable to air, ground, and autonomous systems and subsystems, (e.g. structural components, mechanical, power transfer, and drive systems relevant to both Army and commercial systems). For example, this technology could be applied as an oil pressure monitoring system in military or commercial vehicles. If a significant drop in pressure is sensed, it will provide the usual driver warning, but will also allow the vehicle to continue operation by recommending or automatically implementing measures determined previously through ML, such as limiting top speed, or redirecting cooling capability to the failing area that are most effective in educed performance from prior machine learning data to reach intended mission without further subsystem failures. REFERENCES: 1. Wade, Daniel R., et al., "Machine Learning Algorithms for HUMS Improvement on Rotorcraft Components", Paper presented at the AHS 71st Annual Forum, Virginia Beach, VA May 5-7, 2015 (Distribution Unlimited per AMRDEC PAO (PR 1608). KEYWORDS: Machine Learning, Artificial Intelligence, Real-Time Control, Health and Usage Monitoring (HUMS), Sensors, Neural, Central Processing Unit (CPU).
TECHNOLOGY AREA(S): Information Systems OBJECTIVE: The objective is to develop an innovative time difference of arrival (TDOA) approach to detect and locate GPS spoofing signal emitters in high accuracy and precision. DESCRIPTION: GPS spoofing signal emitters have become an increasing threat to GPS receivers. Due to the low signal power required to operate these devices, they are also difficult to detect. Various approaches have been proposed to defeat such measures, but most of these approaches are not able to localize the emitter. The distance from an emitter source can be accurately measured using a time difference of arrival (TDOA) ranging technique. With accurate measurements of the distances between three or more synchronized receivers, the location of an emitter can be estimated by multilateration. A system consisting of networked receivers will be designed and developed using a TDOA technique capable of: 1) Detecting the presence of a corrupted GPS solution due to a spoofing emitter, 2) Determining the location of the spoofing emitter, and 3) Extending the solution to multiple emitters. The system should detect the presence of GPS spoofing with a high confidence against any spoofing geometry or strategy while the receivers are on the move. Although this topic calls for the implementation of these techniques using the military GPS codes, the GPS C/A code would also provide an acceptable Phase II demonstration. PHASE I: Conduct a feasibility study that identifies and addresses the problems that must be overcome in order to successfully demonstrate the proposed concept. Analyze the accuracy supported by modelling and simulation results. Deliver a final report that covers the outcome of this study, performance specifications, and prototype design and fabrication plan details. PHASE II: Fabricate receiver prototype to test, demonstrate and validate the feasibility of the concept under simulated laboratory conditions. Demonstrate GPS-independent synchronization of networked receivers and pinpoint the locations of multiple emitters. Deliver the final report, TRL 5 networked receiver prototypes (four units), its description and operation guide, and laboratory test reports. PHASE III DUAL USE APPLICATIONS: Implement the demonstrated algorithm in GPS P(Y) and M codes. Develop a small size, weight, and power (SWAP) system applicable to mounted or dismounted platforms. Other military applications could include unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and other robotic platforms. This technology is transitioned to the Army Assured Positioning, Navigation, and Timing (PNT) Program. Yüklə 0,91 Mb. Dostları ilə paylaş:
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