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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

An Evaluation of Extended Reality Technologies for Use in Verification Testing at NASA 2024 HRP IWS Abstract

BACKGROUND At NASA, verification testing is the formal process of ensuring that a product conforms to requirements set by a project or program. Some verification methods, such as Demonstrations and Test, require either the end product or a mockup of the product with sufficient fidelity to stand-in for the product during the test. Traditionally, these mockups have been physical (e.g., foam-core and wood) but there is growing interest in exploring new methods for testing with these mockups. These methods include virtual reality (VR), mixed reality, and augmented reality which are collectively referred to as eXtended Reality (XR) technologies. VR has already been adopted and used by many in the aerospace industry as a tool for use in early design phases (e.g., developmental testing) and may have the most potential for use in verification tests. Benefits of using VR mockups offer cost effectiveness, ease of iteration, simulation of hazardous conditions (e.g., an egress through a hatch with smoke obscuring vision), and the ability to simulate microgravity conditions, which are challenging to do with physical mockups. However, the validity of test results obtained from VR mockup demonstrations or testing, compared to the current gold standard of physical mockups, remains uncertain. It is unlikely that there is one clean answer as there are many different types of verification outcomes and each XR technology must be evaluated on its own merits. This is not an issue during developmental testing as the design is still in flux and the total success of the design is not dependent upon the results of a developmental test. Verification tests, however, only happen once, assuming no change to the design, and the results are used to certify the product. Therefore, establishing the validity of XR mockup-based verification outcomes is essential before considering them for any use in verification tests. OBJECTIVE AND METHOD To address this concern, the Human Research Program has funded a project to explore and qualify how XR technologies might be used in verification demonstration and testing at NASA. Currently, we are conducting a review of the literature on the utilization of XR mockups for design activities, prototyping, and user testing. We are employing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis method to identify the pros, cons, and barriers to adoption of XR technologies for verification testing at NASA. Additionally, we are developing a framework to guide the deployment of XR mockups for verification tests. Building upon available evidence from the literature and subject-matter expert feedback, our goal for the framework is to provide guidelines for which forms of XR mockups are suitable for a given verification test, when only physical mockups should be employed and to highlight areas for which more evidence is needed. To further refine our framework and to contribute to the body of evidence, we are planning a lab-based experiment comparing a VR mockup to a physical twin for a set of select verification outcomes. ANTICIPATED RESULTS In this presentation, we will present the work we conducted to evaluate XR technologies for use in verification tests at NASA. We will summarize and report our findings from the SWOT analysis and our lab-based study, and we will present the current state of the XR Technologies for Verification Testing framework. We will conclude by summarizing remaining work and future directions for the project. Technologies for Verification Testing framework. We will conclude by summarizing remaining work and future directions for the project.

Extended Reality↗
NASA NTRS2001

Lower body negative pressure as a tool for research in aerospace physiology and military medicine

Lower body negative pressure (LBNP) has been extensively used for decades in aerospace physiological research as a tool to investigate cardiovascular mechanisms that are associated with or underlie performance in aerospace and military environments. In comparison with clinical stand and tilt tests, LBNP represents a relatively safe methodology for inducing highly reproducible hemodynamic responses during exposure to footward fluid shifts similar to those experienced under orthostatic challenge. By maintaining an orthostatic challenge in a supine posture, removal of leg support (muscle pump) and head motion (vestibular stimuli) during LBNP provides the capability to isolate cardiovascular mechanisms that regulate blood pressure. LBNP can be used for physiological measurements, clinical diagnoses and investigational research comparisons of subject populations and alterations in physiological status. The applications of LBNP to the study of blood pressure regulation in spaceflight, groundbased simulations of low gravity, and hemorrhage have provided unique insights and understanding for development of countermeasures based on physiological mechanisms underlying the operational problems.

Review↗
NASA NTRS1992

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 368)

This bibliography lists 305 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Sep. 1992. The subject coverage concentrates on the biological, physiological, psychological, and environmental effects to which humans are subjected during and following simulated or actual flight in the Earth's atmosphere or in interplanetary space. References describing similar effects on biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. Applied research receives the most emphasis, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion.

Source record↗
NASA NTRS2023

Modeling and Simulation for Exercise Vibration Isolation and Stabilization System Design

The microgravity environment that crew members experience on orbit presents a well-known health challenge, particularly when it comes to loss of muscle and bone mass. To counteract these negative effects, exercise countermeasures play a critical role in the daily routine of the crew on the International Space Station (ISS). To help inform requirements for upcoming exploration missions such as the Gateway Program, a new device, called the European Enhanced Exploration Exercise Device (E4D), is being built by the European Space Agency through their contractor, the Danish Aerospace Company. The E4D is being demonstrated on the ISS and is unique from other current exercise devices in that it provides four separate modalities in a single device: resistive, cycle ergometry, seated aerobic rowing, and rope pulling. To support the integration of E4D on ISS, a passive Vibration Isolation and Stabilization (VIS) system was required by NASA, and this responsibility was given to the Johnson Space Center. This paper describes the end-to-end process of modeling, simulation, and analysis used to inform the mechanical design of the VIS system. The process begins with the collection of representative exerciser motion capture (MoCap) through ground-based testing with the developmental E4D in both the Prototype Immersive Technology (PIT) Laboratory and the Active Response Gravity Offload System (ARGOS) facility at the Johnson Space Center. These collected MoCap data are processed through human biomechanics modeling to create forcing functions as input to a multibody dynamics simulation of the combined E4D/VIS system, with numerous resulting outputs. These outputs include microgravity accelerations, overall system displacements, internal and transmitted loads, as well as collisions. Microgravity accelerations are compared for compliance against ISS requirements while displacements are used for sway space determinations on the design and volumetric constraints within the targeted module. Internal loads are supplied to the supporting stress analysis teams and external loads for structural loads and dynamics teams, both at NASA and ESA. Finally, contact and clearance analysis is performed using the simulation to eliminate potential design issues. To ensure that the elements of the multibody simulation were verified and validated, correlation against multiple VIS related ground hardware testbeds was performed and characterized. In addition to the isolation part of the VIS problem, stabilization is also key to the integrated performance and evaluation. Due to the difficulty in defining ISS requirements in this area, the stability of the exerciser was inspected via analysis. Loss of balance was defined analytically as occurring when the resultant force vector acting on the exerciser lies outside the base of support of the feet. Both the VIS and E4D teams have recently gone through their Critical Design Reviews (CDRs) and the iterative model-based approach has been integral to inform mechanical design, particularly in the case of the VIS. This same end-to-end approach is now being applied for the Gateway Program, where an Exploration Exercise Device (EED) derived from the E4D and notional VIS for the device are under concept development.

Countermeasures↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Flight evaluation of advanced controls and displays for transition and landing on the NASA V/STOL systems research aircraft

Flight experiments were conducted on Ames Research Center's V/STOL Systems Research Aircraft (VSRA) to assess the influence of advanced control modes and head-up displays (HUD's) on flying qualities for precision approach and landing operations. Evaluations were made for decelerating approaches to hover followed by a vertical landing and for slow landings for four control/display mode combinations: the basic YAV-8B stability augmentation system; attitude command for pitch, roll, and yaw; flightpath/acceleration command with translational rate command in the hover; and height-rate damping with translational-rate command. Head-up displays used in conjunction with these control modes provided flightpath tracking/pursuit guidance and deceleration commands for the decelerating approach and a mixed horizontal and vertical presentation for precision hover and landing. Flying qualities were established and control usage and bandwidth were documented for candidate control modes and displays for the approach and vertical landing. Minimally satisfactory bandwidths were determined for the translational-rate command system. Test pilot and engineer teams from the Naval Air Warfare Center, the Boeing Military Airplane Group, Lockheed Martin, McDonnell Douglas Aerospace, Northrop Grumman, Rolls-Royce, and the British Defense Research Agency participated in the program along with NASA research pilots from the Ames and Lewis Research Centers. The results, in conjunction with related ground-based simulation data, indicate that the flightpath/longitudinal acceleration command response type in conjunction with pursuit tracking and deceleration guidance on the HUD would be essential for operation to instrument minimums significantly lower than the minimums for the AV-8B. It would also be a superior mode for performing slow landings where precise control to an austere landing area such as a narrow road is demanded. The translational-rate command system would reduce pilot workload for demanding vertical landing tasks aboard ship and in confined land-based sites.

Franklin, James A.↗
NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗
NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗
NASA NTRS2015

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

propulsion simulation↗
NASA NTRS2022

Aerospace Regenerative Fuel Cell Fluidic Component Design Challenges

A discrete regenerative fuel cell (RFC) system is currently under development at the NASA Glenn Research Center and Johnson Space Center. The objective of this activity is to produce an energy storage system with a higher specific energy (W·hr/kg) than the state-of-the-art battery technologies then test that system within an environment simulating the temperatures and pressures expected over a full lunar day cycle on the lunar surface. The system is designed to be as flight-like as possible given resource and commercially available technology limitations. The RFC system is comprised of a fuel cell power system, a high pressure electrolysis system, fluidic and electronic balance of plant systems, avionics, power management and distribution system, and software. This technical memorandum discusses components and subsystems that present challenges in developing flight ready RFCs. To remedy identified issues, functional application test plans are presented for components and subsystems primarily constructed with commercially available options due to the budget and schedule constraints of this ground technology demonstration project.

fuel cell↗
NASA NTRS2016

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5% per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM(Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas/Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte/Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASA's Airspace Technology Demonstration - 2 (ATD-2) project for technology demonstration of Integrated Arrival/Departure/Surface (ADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement)/KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASA's fast-time simulation tool.

scheduling↗
NASA NTRS1997

Lightning as an Aircraft Hazard: (Latest citations from the Aerospace Database)

The bibliography contains citations concerning lightning strikes as an aircraft hazard. Aircraft designs to prevent or withstand lightning strikes, statistics on lightning strikes of aircraft, detection of strikes, remote monitoring and detection of lightning, initiation of lightning strikes by aircraft, effects of lightning strikes on aircraft structural and electronic components, modeling, and simulation of lightning strikes on aircraft are discussed. Remote detection of storms with regard to aircraft safety are discussed in another bibliography. (Contains 50-250 citations and includes a subject term index and title list.)

LIGHTNING↗
NASA NTRS1990

A test fixture for measuring high-temperature hypersonic-engine seal performance

A test fixture for measuring the performance of several high temperature engine seal concepts was installed at the NASA Lewis Research Center. The test fixture was developed to evaluate seal concepts under development for advanced hypersonic engines such as those being considered for the National Aerospace Plane. The fixture can measure static seal leakage performance from room temperature up to 1500 F and air pressure differentials up to 100 psi. Performance of the seals can be measured while sealing against flat or engine simulated distorted walls, where distortions can be as large as 0.150 in. in only an 18 in. span. The fixture is designed to evaluate seals 3 feet long, a typical engine panel length. The seal channel can be configured to test square, circular, or rectangular seals that are nominally 0.5 in. high. The sensitivity of leakage performance to lateral or axial loading can also be measured using specially designed high temperature lateral and axial bellows preload systems. Leakage data for a candidate ceramic wafer engine seal is provided by way of example to demonstrate the test fixture's capabilities.

Steinetz, Bruce M.↗
NASA NTRS1992

A domain-specific design architecture for composite material design and aircraft part redesign

Advanced composites have been targeted as a 'leapfrog' technology that would provide a unique global competitive position for U.S. industry. Composites are unique in the requirements for an integrated approach to designing, manufacturing, and marketing of products developed utilizing the new materials of construction. Numerous studies extending across the entire economic spectrum of the United States from aerospace to military to durable goods have identified composites as a 'key' technology. In general there have been two approaches to composite construction: build models of a given composite materials, then determine characteristics of the material via numerical simulation and empirical testing; and experience-directed construction of fabrication plans for building composites with given properties. The first route sets a goal to capture basic understanding of a device (the composite) by use of a rigorous mathematical model; the second attempts to capture the expertise about the process of fabricating a composite (to date) at a surface level typically expressed in a rule based system. From an AI perspective, these two research lines are attacking distinctly different problems, and both tracks have current limitations. The mathematical modeling approach has yielded a wealth of data but a large number of simplifying assumptions are needed to make numerical simulation tractable. Likewise, although surface level expertise about how to build a particular composite may yield important results, recent trends in the KBS area are towards augmenting surface level problem solving with deeper level knowledge. Many of the relative advantages of composites, e.g., the strength:weight ratio, is most prominent when the entire component is designed as a unitary piece. The bottleneck in undertaking such unitary design lies in the difficulty of the re-design task. Designing the fabrication protocols for a complex-shaped, thick section composite are currently very difficult. It is in fact this difficulty that our research will address.

Punch, W. F., III↗
NASA NTRS2020

Laser Based Remote and Rapid Inspection for Composite Plates

Composites have been extensively used in aero structure and become the predominate components in the new airframes. Thus, rapid and effective inspection for composite structures is highly desired in aerospace engineering in order to shorten the certificate cycle for new structures or provide safety guarantee for existing ones. In this paper, a laser based remote Lamb wave inspection system is presented and implemented on composite plates for simulated damage detection. The system employs pulsed laser (PL) and scanning laser Doppler vibrometer (SLDV) for noncontact and remote Lamb wave actuation and wavefield sensing. A composite plate with simulated defect (surface bonded quartz rod) is inspected with the PL-SLDV laser system. Wave scattering are observed in the SLDV acquired wavefield and the damage is further evaluated with wavefield imaging and frequency wavenumber analysis. Potential application towards automatic PL Lamb wave excitation is also explored through employing an industry robotic arm towards rapid inspection.

Lamb waves↗
NASA NTRS2022

Building and Breaking Carbon Composites with REACTER

Carbon-based composites have become indispensable materials in aerospace and other high-performance applications, yet obtaining a detailed, nanoscale understanding of their morphology and failure mechanisms using only experimental methods remains a difficult challenge. REACTER is a versatile computational modeling tool for atomistic molecular dynamics simulations designed to model chemical reactions at the speed and length scales of classical force fields. In this work, several recent features of REACTER were applied to the creation and subsequent mechanical testing of two classes of carbon composites: carbon nanotube (CNT) composites and carbon fiber reinforced polymers (CFRP). A network of CNTs was grown dynamically using the new ‘create atoms’ feature of REACTER. The CNT filler was embedded into a polyarylacetylene (PAA) matrix by simulated in situ polymerization to obtain the final composite model. To generate the second class of carbon composite, fully carbonized (graphitic) carbon fiber morphologies were created by the method of Desai et al. [1], but using the advanced reaction constraints framework of REACTER. Two fiber models were created, representing a circular carbon fiber core and a flat surface, and similarly infiltrated with resin to obtain the final CFRP structure. Failure mechanisms were elucidated by simulating mechanically induced bond breaking, as characterized by third order DFT-based tight-binding simulations, via a reaction constraint on the total potential energy of the involved atoms.

Polymer↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗