N211-D02 TITLE: DIRECT TO PHASE II – Cartridge Actuated Devices/Propellant Actuated Devices Digital Twin
RT&L FOCUS AREA(S): General Warfighting Requirements;Machine Learning/AI
TECHNOLOGY AREA(S): Air Platforms;Information Systems;Weapons
OBJECTIVE: Develop, validate, and migrate to the cloud a digital twin of Cartridge Actuated Devices/Propellant Actuated Devices (CAD/PAD) that use double-base propellants while providing real-time health monitoring of deployed devices based on the environmental exposure.
DESCRIPTION: The Navy requires digital twin technologies, which allow the digital footprint of any product to permeate throughout the CAD/PAD devices’ entire service life from design inception, through development, sustainment, and finally to disposal. Digital twin technology is viable and allows access to the digital image of the asset in real time, leading to secure actionable information that will improve a process, product, or service of any organization [Ref 1]. The concept has been around for a while, as shown during the disaster of the Apollo 13 mission. NASA demonstrated the technology with a mirrored system on the ground, which rescued the flight, and is further illustrated in [Ref 2]. Digital twin technology involves creating a virtual representation of a physical product. Digital twins are powered by machine learning algorithms and are continuously learning systems. The products are connected in a cloud-based environment that receives the data from the sensors or other available data sources. The input data is analyzed and compared to the CAD/PAD device baseline data to identify actionable information.
The goal for the CAD/PAD digital twin technology is to design, test, and develop a product in a virtual environment and to monitor product health to identify potential degradation. This will allow real-time monitoring and replacement of the product utilizing its maximum safe life, which will reduce product’s life cycle costs. In the future, the goal is to have a digital twin model for selected CAD/PAD devices hosted in the cloud and updated with historical deck plate data to make the model more robust. The frequency of the digital twin updates will coincide with approved for release historical deck plate and sensor data.
Because of the large variety of CAD/PAD devices and the numerous failure modes associated with these devices, this topic seeks to pursue a digital twin model for CAD/PAD items that employ double-based propellants. Double-based propellants will deplete its stabilizer at a faster rate when exposed to high temperature. A comparative study of the thermal decomposition of naturally- and artificially-aged double-based propellants has been carried out at five different heating rates and the results show that there is only one decomposition peak on differential scanning calorimetry (DSC) curves, and this decomposition has been accelerated by ageing. The influence of the heating rate on the DSC behavior of the propellants was verified. The kinetic parameters such as activation energy and frequency factor and the thermodynamic parameters were obtained from DSC data [Ref 3]. The aging of CAD/PAD items containing double-based propellants are dependent on the environmental conditions, such as temperatures, to which they are subjected. The digital twin being developed will take these environmental conditions and determine the stabilizer content of the CAD/PAD items. The environmental conditions are dependent upon the operational location and duration of the aircraft at the location.
In order to determine the environmental exposure of the CAD/PAD items, the developed digital twin should all be able to use both collected sensor data and a combination of deck plate data and available weather data, such as National Oceanic and Atmospheric Administration (NOAA) or local installation data, in order to determine stabilizer content. The developed digital twin predicted stabilizer content should be validated against measured stabilizer content of fleet return assets with known environmental exposure.
Upon successful demonstration, the contractor should assist in obtaining the necessary approvals for use of the developed digital twin and migration into a cloud-based environment compliant with all applicable Navy Marine Corp Internet (NMCI) and applicable Operations Security (OPSEC) requirements.
Digital twin technology is not widespread due to the requirements of prohibitive computing power needs, accessibility, bandwidth, and storage issues. Lack of robust data analytics aided by artificial intelligence, machine learning techniques, and visualization tools is impeding technology development. Digital twin technology has the potential to improve supply chain integrity, flight safety, in-flight service, Condition Based Maintenance (CBM), foreign object detection, and predictive maintenance. For example, developing any predictive maintenance algorithm requires sensor data, which can be utilized to train a classification algorithm for fault detection. This algorithm is used for verification and is installed as a code to the control unit of the product. It is nearly impossible to create the fault conditions necessary for training a predictive maintenance algorithm on the actual product. A solution to this challenge is to create a digital twin of the product (a model), and apply simulation and analysis of sensor data for various fault conditions. A neural network detects abnormal patterns of the sensor data, reflects the trends in predictive models, which are then used to predict failures, and allows tests for all fault conditions with severity. The entire procedure should be automated, thereby allowing tests of "what-if" scenarios on the digital twin model. Predictive maintenance helps to determine when an aircraft product needs maintenance or replacement. It reduces downtime and prevents product failure by enabling maintenance or replacement of the CAD/PAD device to be scheduled based on the actual need rather than at predetermined intervals. It can be used to calculate maintenance-related parameters (i.e., MTBR – Mean Time Between Replacement), forecast the behavior of the product under different circumstances, and simulate different maintenance scenarios. Thus, predictive maintenance capability helps to extend the product life and reduce total ownership costs. Collectively, it will contribute significantly to improving the Navy’s mission readiness and sustainment. It is envisioned that the CAD/PAD program will be able to develop a virtual integrated, model-based representation of a physical product, allow the simulation of the product in a real setting in a dynamic fashion, and demonstrate closed loops between the virtual and physical space.
Challenges for this effort include developing an accurate model that precisely reflects the physical twin’s properties. For predicting failures, detailed blueprints of a product’s failure modes are required. Since the digital twin is a replica of the physical product itself, the requirements, qualification, and certification necessary to determine the flight worthiness of the product are the same for the virtual model as well. The expected outcomes of the effort are real-time monitoring and health status of the deployed CAD/PAD items. This will enable prolonged product life to deliver capabilities continually. For the proof of concept, the Parachute Deployment Rocket Motor (PDRM) [DODIC MT29], the Under Seat Rocket Motor (USRM) [DODIC MD68], and the Catapult Primary Cartridge [DODIC WB15] will be used for the demonstration of the digital twin model.
The developed digital twin model should be capable of predicting remaining stabilizer content within 20% of measured values. The developed digital twin should be migrated into a cloud-based environment and be capable of meeting all NMCI and OPSEC regulations and requirements. The developed digital twin should be capable of utilizing both historical aircraft location/weather data and sensor data.
PHASE I: For a Direct to Phase II topic, the Government expects that the small business would have accomplished the following in a Phase I-type effort. Have developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The below actions would be required in order to satisfy the requirements of Phase I:
Designed and developed a digital twin capable of predicting stabilizer content based on environmental exposure of deployed devices.
Determined and demonstrated the model’s prediction of the cockpit temperature gradient based on aircraft location and available weather data.
FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic, but from non-SBIR funding sources) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above. Documentation should include all relevant information including, but not limited to: technical reports, test data, prototype designs/models, and performance goals/results. Work submitted within the feasibility documentation must have been substantially performed by the offeror and/or the principal investigator (PI). Read and follow all of the DON SBIR 21.1 Direct to Phase II Broad Agency Announcement (BAA) Instructions. Phase I proposals will NOT be accepted for this topic.
PHASE II: Build, refine, enhance, and validate (against measured stabilizer content of fleet returned assets (MT29 and WB15) with known environmental exposure) a prototype product (a high-fidelity model) by integrating the physical asset to the digital twin and demonstrate the closed loop between physical - virtual - physical space. Demonstrate the applicability of readiness and sustainment influencing factors such as CBM, predictive maintenance, and flight safety with quantifiable metrics. Quantify the cost benefits, such as reduction in the operation cost and total lifecycle cost, as applicable. Demonstrate the applicability of Navy provided "what-if" scenarios tested against factors such as product performance management, Navy-unique harsh environmental operating conditions, and future operating environments. Assist in obtaining approvals for the developed digital twin’s use and migrate the digital twin into a cloud-based environment. Demonstrate compliance with NMCI requirements and end user access.
PHASE III DUAL USE APPLICATIONS: Develop robust architecture, showing the linkage between connectivity and services. Demonstrate the integration of the product into naval aircraft and perform final testing. Successfully transition, implement, and insert the technology for warfighter benefits. Develop mobile application solutions as applicable. Aerospace industry employs cartridges that use double-based propellants and will benefit from the digital twin technology. The successful demonstration of the digital twin of the product that is operationalized will enable the applicability of the approach to any product/process/service industry to achieve cost benefits.
The private sector (e.g., commercial aerospace industry and private military fleets) use similar cartridges as the ones used in military aircrafts. Some of these cartridges employ double-based propellants as their energy source and experience similar propellant-stabilizer depletion issues. The digital twin model developed under this SBIR topic will provide those industries a mean of tracking the health of the installed devices and assist them in making replacement decisions based on the environmental exposure of the devices. The digital twin model will enable the private sector to utilize maximum safe life of the devices and enhance the safety of their operations.
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KEYWORDS: Digital Twin; Stabilizer depletion; Navy Marine Corp Internet; NMCI; DODIC WB15; DODIC MT29; National Oceanic and Atmospheric Administration; NOAA; Parachute Deployment Rocket Motor; PDRM; Under Seat Rocket Motor; USRM; DODIC MD68; Catapult Primary Cartri