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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 Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 80.00K | Year: 2015

To address U.S. Navy needs, Sentient proposes to establish a Design Framework for reliability assurance of additive manufactured (AM) parts using their DigitalCloneTM Component (DCC) software. The framework will be tailored to metal components built through AM processes with complex geometries. Sensors are embedded during AM component build to allow for exploitation of sensor data, physics-based material models, and Bayesian-based algorithms for measurement and operational uncertainty management. Computational material model(s) virtually serialized to each AM part are regularly updated to best capture current health state and adaptively assess reliability. Sentient?s Design Framework allows for inclusion of life-cycle forecasts during the creation and exploration of the AM design space. In the Phase I, coupon samples with embedded sensors are built and modeled in the DCC framework for validation against physical testing results. A Bayesian-based algorithm for information fusion will be used to validate dynamic fatigue assessment capability for AM part reliability assurance. In Phase II, the framework will be expanded to different AM material processes with more complex geometries and loadings. It will include Multi-Objective Optimization tools for use in design, and reliability monitoring of a small air-to-air heat exchanger with forecasting of life-cycle and performance metrics compared to 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 Energy | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 999.99K | Year: 2016

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.


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 1.05M | Year: 2016

Historically, the porosity associated with ceramic matrix composite structures has been viewed as a detriment for many missile applications. The standard practice is to perform several treatment/retreatment processes to reduce the porosity to an acceptable level while increasing the strength of the structure. The processing methods inherent to the production of many composite materials may provide an underexplored benefit with respect to vehicle drag reduction and engine cooling for hypersonic vehicles. Several research studies have been performed using plates with slots and holes. Depending on the mass flux rate of the cooling gas, heat transfer results incorporating staggered arrays of holes have been shown to become potentially worse than those measured without cooling. Thus blowing rate, hole-patterns, and testing conditions all play a part in determining the effectiveness of this thermal management technique. One of the benefits to be quantified is the achieved uniformity of the composite matrix ceramic porosity. The intent is to demonstrate that the closely spaced openings, associated with the composite structure, does not suffer from the same drawbacks experimentally observed with distributed orifices and slotted injection techniques. Or another way to quantify it would be a very dense hole pattern per unit area that releases very small amounts of gas (zero momentum) minimizing the strength or formation of the vortex pairs that typically degrades intended performance.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2013

The Innovation of this Phase II project is developing physics-based analytical models to analyze gearbox components for safety, longevity, reliability and cost by predicting (1) New component performance, and optimal time-to-remanufacture, (2) Qualification of used components for remanufacturing process, and (3) Predicting the remanufactured component performance. Current industry approach is to design, manufacture, operate, and retire assets based on traditional methods, which typically rely on standards-based estimates, historical data/domain experience, physical examination, testing, monitoring, and inspection. This process is extremely time and resource intensive. Further, this process often does not consider the opportunity to use remanufacturing processes to extend/enhance product performance. Sentient technology will address these issues and fulfill the industry requirements. Phase II will expand the Phase I technology to include additional gearbox materials, damage modes and remanufacturing processes in a more comprehensive design and analysis framework capable of predicting optimal time-to-remanufacture and optimizing refurbishing operations to extend the useful life of components. This SBIR technology reduces physical testing using virtual testing, and will assist in remanufacturing of high value, high demand rotorcraft, automotive and wind turbine gearbox components. Hence, it decreases the energy, material resources, and costs associated with manufacturing, and ensures that the product performance is maintained/improved. The broader/commercial impact of the SBIR technology is within aerospace, energy, and transportation industries on high dollar assets that rely on the reliable function of highly engineered (and thus expensive) gearboxes. Our new Advanced Manufacturing partnership based application provides the US manufacturing supply chain a first mover and a sustainable competitive advantage greater than the 6% offshore labor rate advantage. This advantage comes through reuse of high value-added assets optimized for maximum lifetime use, coupled with decreased time and costs associated with traditional physical testing and analysis methods. This is possible based on the high-level of detail included in physics-based models, which (conceptually) decode material information at the microstructure level just as the Human Genome Project decodes genetic information at the DNA level. Our innovation enables the customer to rapidly, cost effectively, and accurately predict a product?s lifecycle (design, manufacture, operation, degradation, maintenance, repair/remanufacture, and retirement) at the material, component, and assembly/system scales. We foresee a future opportunity due to the fact that our innovation developed under this NSF grant will give us a competitive advantage of lower costs to provide the software and service. Our cost to deploy the technology is 10X lower than traditional companies in this space.


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: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 1.25M | Year: 2013

The Innovation of this Phase II project is developing physics-based analytical models to analyze gearbox components for safety, longevity, reliability and cost by predicting (1) New component performance, and optimal time-to-remanufacture, (2) Qualification of used components for remanufacturing process, and (3) Predicting the remanufactured component performance. Current industry approach is to design, manufacture, operate, and retire assets based on traditional methods, which typically rely on standards-based estimates, historical data/domain experience, physical examination, testing, monitoring, and inspection. This process is extremely time and resource intensive. Further, this process often does not consider the opportunity to use remanufacturing processes to extend/enhance product performance. Sentient technology will address these issues and fulfill the industry requirements. Phase II will expand the Phase I technology to include additional gearbox materials, damage modes and remanufacturing processes in a more comprehensive design and analysis framework capable of predicting optimal time-to-remanufacture and optimizing refurbishing operations to extend the useful life of components. This SBIR technology reduces physical testing using virtual testing, and will assist in remanufacturing of high value, high demand rotorcraft, automotive and wind turbine gearbox components. Hence, it decreases the energy, material resources, and costs associated with manufacturing, and ensures that the product performance is maintained/improved.

The broader/commercial impact of the SBIR technology is within aerospace, energy, and transportation industries on high dollar assets that rely on the reliable function of highly engineered (and thus expensive) gearboxes. Our new Advanced Manufacturing partnership based application provides the US manufacturing supply chain a first mover and a sustainable competitive advantage greater than the 6% offshore labor rate advantage. This advantage comes through reuse of high value-added assets optimized for maximum lifetime use, coupled with decreased time and costs associated with traditional physical testing and analysis methods. This is possible based on the high-level of detail included in physics-based models, which (conceptually) decode material information at the microstructure level just as the Human Genome Project decodes genetic information at the DNA level. Our innovation enables the customer to rapidly, cost effectively, and accurately predict a product?s lifecycle (design, manufacture, operation, degradation, maintenance, repair/remanufacture, and retirement) at the material, component, and assembly/system scales. We foresee a future opportunity due to the fact that our innovation developed under this NSF grant will give us a competitive advantage of lower costs to provide the software and service. Our cost to deploy the technology is 10X lower than traditional companies in this space.


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2014

Historically, the porosity associated with ceramic matrix composite structures has been viewed as a detriment for many missile applications. The standard practice is to perform several treatment/retreatment processes to reduce the porosity to an acceptable level while increasing the strength of the structure. The processing methods inherent to the production of many composite materials may provide an underexplored benefit with respect to vehicle drag reduction and engine cooling for hypersonic vehicles. Several research studies have been performed using plates with slots and holes. Depending on the mass flux rate of the cooling gas, heat transfer results incorporating staggered arrays of holes have been shown to become potentially worse than those measure without cooling. Slotted coolant injection has been shown to minimize/remove the vortex development there by reducing the mixing and localized heating phenomenon. One of the benefits to be quantified is the achieved uniformity of the composite matrix ceramic porosity. The intent is to demonstrate that the closely spaced openings associated with the composite structure emulates or more closely resembles the injection results observed with slotted coolant injection.

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