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Idaho Falls, ID, United States

Grant
Agency: Department of Defense | Branch: Defense Logistics Agency | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2015

In response to Defense Logistics Agencys SBIR for Advanced Manufacturing Technologies, Sentient proposes to develop a life-cycle cost optimization tool based on its DigitalClone-ComponentTM (DCC) modeling technology. This tool will enable users to better understand the tradeoffs between manufacturing cost and performance/durability of the resultant components. This is particularly attractive for low volume components produced through additive manufacturing (AM) processes, which can significantly reduce upfront tooling costs especially in support of legacy fielded weapon systems. However, qualification of AM components may require costly experimental testing to determine reliability. Sentient is proposing a tool that will allow the user to conduct these performance tests in a virtual environment, which provides upfront knowledge of expected mean time between failures. This information can then be factored into the LCC calculation along with up-front manufacturing costs. The proposed tool is equally applicable to components manufactured with traditional processes. During Phase I, Sentient will demonstrate technical feasibility of the tool through life-cycle cost analysis of an example component produced both by additive manufacturing and through traditional machining. Ground truth durability of these components will be determined through experimental testing. These results will then be compared to DCC predictions to validate the potential cost savings.


Grant
Agency: Department of Energy | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

Project Summary/Abstract The object-oriented modeling language Modelica allows users to analyze the performance of their complex systems consisting of mechanical, electrical, hydraulic, control, etc. components. Many manufacturing industries and national laboratories are increasingly using Modelica to develop the next generation of energy efficient systems. However, simulating complex systems that includes components from several domains are computationally inefficient. Sentient and Xogeny are proposing an automated model reduction environment, called Mercator, that takes detailed subsystem models and, using high performance computing resources, automatically generates reduced-order Modelica models (ROMMs). ROMMs replace the original subsystem models to quickly assess overall system performance. Mercator will be a cloud-based product with dynamically adjustable computational resources. Users can access and up-/download sub-systems via a standard web-browser. Anticipated Benefits/Potential Commercial Applications of the Research or Development Several groups across multiple national laboratories (INL, LBNL, ORNL), as well as industrial companies (Boeing) are using Modelica in their modeling efforts and all of them have been extremely enthusiastic about the prospect of a tool that would allow them to easily and automatically generate reduced-order models. Mercator will provide the national laboratories and the manufacturing and engineering markets with the following benefits: 1. Increase computational efficiency of Modelica Models (allows for faster optimization of systems) 2. Use of High Performance Computers (quick creation of reduced-order models) 3. Scalable (Mercator will work on large as well as small clusters) 4. Domain independent 5. Encapsulated (reduced-order models can be easily shared between institutions) 6. Preservation of proprietary information (reduced-order models do not reveal the topology or detailed design information of the original models) 7. User-friendliness (Mercator will allocate computational resources and recommend reduction algorithms) Keywords: Modelica; High-performance computing; Cloud computing; Reduced-order models; Numerical simulation; Computational Efficiency Summary for Members of Congress There have been several important European led initiatives in the area of modeling and simulation in the last decade (e.g. Modelica and FMI) that are clearly having an impact on our shores and even in our national laboratories. This project is an attempt to cultivate such innovations led by US-based companies while benefiting US companies and national laboratories.


Grant
Agency: Department of Energy | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

Project Summary/Abstract Sentient proposes to develop predictive modeling tools for parts made through the additive manufacturing (AM) processes. Our approach requires the use of high performance computing (HPC). The use of additive manufacturing processes to make different engineering components has been increased over the past years. However, there is not a well-established standard for qualifications of these components and industry relies mainly on experimental testing for qualification purposes and behavior analysis of these components. Therefore, in order to obtain a reliable performance and a life prediction model, a physics-based model is needed to analyze the microstructure of these components and reliably predict their performance. During Phase I, Sentient is proposing to incorporate its DigitalClone-Component (DCC) modeling tool to develop modeling software that includes the microstructural features of AM materials and components manufactured from, and use the developed model for their performance analysis and life prediction. The different steps of this model are computationally expensive and use HPC. This model not only accounts for the effect of microstructure on the performance of AM components, but also predicts their fatigue life where currently the experimental testing is heavily used. In Phase II, we will implement our improved model for performance analysis of more complex geometries and inclusion of in situ adjustments. Anticipated Benefits/Potential Commercial Applications of the Research or Development Sentients DCC technology will allow the additive manufacturing companies and related industries to design their components more efficiently and perform more accurate performance and life analysis. This specially is more significant when they use new materials in their design. This will significantly reduce the uncertainty and conservatism in design of new components and required expensive and time-consuming experimental testing, thereby improving design process, increasing performance, reliability and durability, and reducing cost of operation. The physical nature and computational strength of the developed predictive tool will help testing more geometries, materials and design concept resulting in better final products manufactured using AM processes. List of Maximum of 8 Key words that Describe the Project Additive manufacturing, high power computing (HPC), predictive tool, microstructure modeling, performance and life analysis, damage mechanics Summary for Members of Congress Additive manufacturing has increased over the past years. However, there is not a well-established standard for component qualification and industry relies on experimental testing. Sentients technology will reduce the design uncertainty of new components and expensive and time-consuming experimental testing, increasing performance, reliability and durability, and reducing operational costs.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.99K | Year: 2014

ABSTRACT: To address the needs of the U.S. Air Force to improve existing software design packages so that they account better for short crack growth regime, Sentient proposes to incorporate its its DigitalClone-Component(DCC) modeling technology into these packages for short crack growth regime. The use of fracture mechanics to characterize the growth of fatigue cracks in metals is well established in the design of structures. However, initiation of failure generally occurs by nucleation and propagation of critical micro-cracks with sizes ranging from several to a few hundred micrometers. Crack growth doesn"t follow the conventional linear elastic fracture mechanics (LEFM) approach. Therefore, to obtain a reliable life prediction model, a physics-based model is needed to analyze the fatigue crack nucleation and short crack growth. During Phase I, Sentient will use their DCC model for analysis of damage initiation and short crack growth regime. This model accounts for the effect of microstructure on the fatigue crack creation and early growth, and also predicts the fatigue life of the structure where conventional LEFM approach doesn"t work. In Phase II, an improved DCC model will be implemented in a validated design package used in the aerospace industry, through collaboration with aerospace companies. BENEFIT: Sentient"s DigitalClone-Component DCC) technology will allow the aerospace OEMs and other industries to design their structures and components more efficiently and perform more accurate performance and life analysis. This specially is more significant when they use new materials in their design. This will significantly reduce the uncertainty, errors and conservatism in design of new components and structures, thereby improving design process, increasing performance, reliability and durability, and reducing cost of operation. The physical nature and computational strength of the improved design tool will help testing more geometries, materials and design concept resulting in better final products. Sentient"s DCC enhanced modeling capabilities for fracture mechanics analysis will be used throughout the military and commercial aerospace as well as in automotive industries.


Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 79.79K | Year: 2014

To address the Navy"s need for a common, scalable, platform for multi-modal 1pA level current sensing for Electrocardiogram (ECG), Electroencephalogram (EEG), and Electrodermal Response (EDR) to be fielded as a miniature wearable device with non-contact electrodes, Sentient and the State University of New York (SUNY) propose to develop the Bioelectronic Fusion Sensor System (BioFuSenS) that will assess a subject"s stress, fatigue and resilience. BioFuSenS will be modular, real-time, low cost, low bandwidth, open standards-based, including autonomous prediction, health monitoring and management. Innovation is based on an ultra-sensitive, multi-channel, compact low power integrated circuit (IC), combined with mechanical sensing, a Bayesian-based data-model fusion algorithm, and ground truth data model. BioFuSenS is supported by"Predict-Acquire-Confirm-Control"process for monitoring center or remote operation. Energy harvesting for full power management and immunity to motion artifacts and muscle noises through robust and accurate data-model fusion is enabled, addressing the requirements of the Navy. In Phase I, we demonstrate feasibility through transistor-level IC simulations, thorough nonlinear systems analysis, and data-model fusion and noise handling simulations for ECG sensing. In Phase II, we will develop/validate a prototype, demonstrate multi-modal, model-based detection with noise filtering, and optimize the design to meet size, weight, power, and cost (SWAP-C) requirements.

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