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NASA NTRS2008

Development of Advanced Computational Aeroelasticity Tools at NASA Langley Research Center

NASA Langley Research Center has continued to develop its long standing computational tools to address new challenges in aircraft and launch vehicle design. This paper discusses the application and development of those computational aeroelastic tools. Four topic areas will be discussed: 1) Modeling structural and flow field nonlinearities; 2) Integrated and modular approaches to nonlinear multidisciplinary analysis; 3) Simulating flight dynamics of flexible vehicles; and 4) Applications that support both aeronautics and space exploration.

Bartels, R. E.↗

NASA NTRS2013

Global Propagation of Gravity Waves Generated with the Whole Atmosphere Transfer Function Model

A brief review is presented of the Transfer Function Model (TFM) [e.g., Mayr et al., Space Science Reviews, 1990], which describes acoustic gravity waves (AGW) that propagate across the globe in a dissipative and static (no winds) background atmosphere with globally uniform temperature and density variations extending from the ground to 700 km. Unique among existing models, the TFM can be placed between the analytical approach on one end, and the rigorous numerical approach of general circulation models (GCM). The time consuming numerical integration of the conservation equations is restricted to compute the transfer function (TF) for a broad range of frequencies and spherical harmonics. Given TF, the atmospheric response for a chosen source configuration is then obtained in short order. Computationally efficient, the model is well suited to serve as experimental and educational tool for simulating propagating wave patterns across the globe. By design, the TFM is also semi-analytical and therefore well suited to explore the different wave modes that can be generated under different dynamical conditions.

Transfer Function Model↗

NASA NTRS2014

Exploration Systems Development (ESD) Approach to Enterprise Risk Management

The National Aeronautics and Space Administration (NASA) Exploration Systems Development (ESD) Division has implemented an innovative approach to Enterprise Risk Management under a unique governance structure and streamlined integration model. ESD's mission is to design and build the capability to extend human existence to deep space. The Enterprise consists of three Programs: Space Launch System (SLS), Orion, and Ground Systems Development and Operations (GSDO). The SLS is a rocket and launch system that will be capable of powering humans, habitats, and support systems to deep space. Orion will be the first spacecraft in history capable of taking humans to multiple destinations within deep space. GSDO is modernizing Kennedy's spaceport to launch spacecraft built and designed by both NASA and private industry. ESD's approach to Enterprise Risk Management is commensurate with affordability and a streamlined management philosophy. ESD Enterprise Risk Management leverages off of the primary mechanisms for integration within the Enterprise. The Enterprise integration approach emphasizes delegation of authority to manage and execute the majority of cross-program activities and products to the individual Programs, while maintaining the overall responsibility for all cross-program activities at the Division. The intent of the ESD Enterprise Risk Management approach is to improve risk communication, to avoid replication and/or contradictory strategies, and to minimize overhead process burden. This is accomplished by the facilitation and integration of risk information within ESD. The ESD Division risks, Orion risks, SLS risks, and GSDO risks are owned and managed by the applicable Program. When the Programs have shared risks with multiple consequences, they are jointly owned and managed. When a risk is associated with the integrated system that involves more than one Program in condition, consequence, or mitigation plan, it is considered an Exploration Systems Integration (ESI) Risk. An ESI risk may require visibility and risk handling by multiple organizations. The Integrated Risk Working Group (IRWG) is a small team of Risk experts that are responsible for collaborating and communicating best practices. In addition, the forum facilitates proper integration of risks across the Enterprise. The IRWG uses a Continuous Risk Management approach for facilitating the identification, analysis, planning, tracking, and controlling of ESI Risks. The ESD Division, Programs, and Integrated Task Teams identify ESI Risks. The IRWG maintains a set of metrics for understanding Enterprise Risk process and the overall Risk Posture. The team is also actively involved in the modeling of risk for Enterprise Performance Management. With the Enterprise being constrained in Schedule and Budget, and with significant technical complexity, the appropriate use of Risk Management techniques is crucial to the success of the Enterprise. The IRWG achieves this through the modified approach, providing a forum for collaboration on risks that cross boundaries between the separate entities.

Bauder, Stephen P.↗

NASA NTRS1994

Motion Control of Rover-Mounted Manipulators

This paper presents a simple online approach for motion control of rover-mounted manipulators. An integrated kinetic model of the rover-plus-manipulator system is derived which incorporates the nonholonomic rover constraint with the holonomic end-effector constraint. The redundancy introduced by the rover mobility is exploited to perform a set of user-specified additional tasks during end-effector motion. The configuration control approach is utilized to satisfy the nonholonomic rover constraint, while accomplishing the end-effector motion and the redundancy resolution goal simultaneously. This framework allows the user to assign weighting factors to the rover movement and manipulator motion, as well as to each task specification. The computational efficiency of the control scheme makes it particularly suitable for real-time implementation. The proposed method is applied to a planar two-jointed arm mounted on a rover, and computer simulation results are presented for illustration.

robotics rover manipulator holonomic end-effector ↗

NASA NTRS2013

Parametric Thermal Soak Model for Earth Entry Vehicles

The analysis and design of an Earth Entry Vehicle (EEV) is multidisciplinary in nature, requiring the application many disciplines. An integrated tool called Multi Mission System Analysis for Planetary Entry Descent and Landing or M-SAPE is being developed as part of Entry Vehicle Technology project under In-Space Technology program. Integration of a multidisciplinary problem is a challenging task. Automation of the execution process and data transfer among disciplines can be accomplished to provide significant benefits. Thermal soak analysis and temperature predictions of various interior components of entry vehicle, including the impact foam and payload container are part of the solution that M-SAPE will offer to spacecraft designers. The present paper focuses on the thermal soak analysis of an entry vehicle design based on the Mars Sample Return entry vehicle geometry and discusses a technical approach to develop parametric models for thermal soak analysis that will be integrated into M-SAPE. One of the main objectives is to be able to identify the important parameters and to develop correlation coefficients so that, for a given trajectory, can estimate the peak payload temperature based on relevant trajectory parameters and vehicle geometry. The models are being developed for two primary thermal protection (TPS) materials: 1) carbon phenolic that was used for Galileo and Pioneer Venus probes and, 2) Phenolic Impregnated Carbon Ablator (PICA), TPS material for Mars Science Lab mission. Several representative trajectories were selected from a very large trade space to include in the thermal analysis in order to develop an effective parametric thermal soak model. The selected trajectories covered a wide range of heatload and heatflux combinations. Non-linear, fully transient, thermal finite element simulations were performed for the selected trajectories to generate the temperature histories at the interior of the vehicle. Figure 1 shows the finite element model that was used for the simulations. The results indicate that it takes several hours for the thermal energy to soak into the interior of the vehicle and achieve maximum payload temperatures. In addition, a strong correlation between the heatload and peak payload container temperature is observed that will help establishing the parametric thermal soak model.

Agrawal, Parul↗

NASA NTRS2001

Characteristics of Pressure Sensitive Paint Intrusiveness Effects on Aerodynamic Data

One effect of using pressure sensitive paint (PSP) is the potential intrusiveness to the aerodynamic characteristics of the model. The paint thickness and roughness may affect the pressure distribution, and therefore, the forces and moments on the wind tunnel model. A study of these potential intrusive effects was carried out at NASA Langley Research Center where a series of wind tunnel tests were conducted using the Modem Design of Experiments (MDOE) test approach. The PSP effects on the integrated forces were measured on two different models at different test conditions in both the Low Turbulence Pressure Tunnel (LTPT) and the Unitary Plan Wind Tunnel (UPWT) at Langley. The paint effect was found to be very small over a range of Reynolds numbers, Mach numbers and angles of attack. This is due to the very low surface roughness of the painted surface. The surface roughness, after applying the NASA Langley developed PSP, was lower than that of the clean wing. However, the PSP coating had a localized effects on the pressure taps, which leads to an appreciable decrease in the pressure tap reading.

Amer, Tahani R.↗

NASA NTRS2001

Characterization of Pressure Sensitive Paint Intrusiveness Effects on Aerodynamic Data

One effect of using pressure sensitive paint (PSP) is the potential intrusiveness to the aerodynamic characteristics of the model. The paint thickness and roughness may affect the pressure distribution. and therefore, the forces and moments on the wind tunnel model. A study of these potential intrusive effects was carried out at NASA Langley Research Center where a series of wind tunnel tests were conducted using the Modem Design of Experiments (MDOE) test approach. The PSP effects on the integrated forces were measured on two different models at different test conditions in both the Low Turbulence Pressure Tunnel (LTPT) and the Unitary Plan Wind Tunnel (UPWT) at Langley. The paint effect was found to be very small over a range of Reynolds numbers, Mach numbers and angles of attack. This is due to the very low surface roughness of the painted surface. The surface roughness, after applying the NASA Langley developed PSP, was lower than that of the clean wing. However, the PSP coating had a localized effects on the pressure taps, which leads to an appreciable decrease in the pressure tap reading.

Amer, Tahani R.↗

NASA NTRS1992

Flight evaluation of differential GPS aided inertial navigation systems

Algorithms are described for integration of Differential Global Positioning System (DGPS) data with Inertial Navigation System (INS) data to provide an integrated DGPS/INS navigation system. The objective is to establish the benefits that can be achieved through various levels of integration of DGPS with INS for precision navigation. An eight state Kalman filter integration was implemented in real-time on a twin turbo-prop transport aircraft to evaluate system performance during terminal approach and landing operations. A fully integrated DGPS/INS system is also presented which models accelerometer and rate-gyro measurement errors plus position, velocity, and attitude errors. The fully integrated system was implemented off-line using range-domain (seventeen-state) and position domain (fifteen-state) Kalman filters. Both filter integration approaches were evaluated using data collected during the flight test. Flight-test data consisted of measurements from a 5 channel Precision Code GPS receiver, a strap-down Inertial Navigation Unit (INU), and GPS satellite differential range corrections from a ground reference station. The aircraft was laser tracked to determine its true position. Results indicate that there is no significant improvement in positioning accuracy with the higher levels of DGPS/INS integration. All three systems provided high-frequency (e.g., 20 Hz) estimates of position and velocity. The fully integrated system provided estimates of inertial sensor errors which may be used to improve INS navigation accuracy should GPS become unavailable, and improved estimates of acceleration, attitude, and body rates which can be used for guidance and control. Precision Code DGPS/INS positioning accuracy (root-mean-square) was 1.0 m cross-track and 3.0 m vertical. (This AGARDograph was sponsored by the Guidance and Control Panel.)

Mcnally, B. David↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗

NASA NTRS1987

Wake shield

Progress on the modeling of the flow field around a wake shield using a recently obtained code based on the Monte Carlo method is discussed. The direct simulation Monte Carlo method is a method for solving the Boltzman Equation using an approximation to the collision integral term. The collision integrand is evaluated for randomly selected values of its arguments and the summation will approach the integral for large enough samples. The collision effects may be modeled for either hard sphere or various power law potentials. The convective side of the Boltzman equation is approximated over a time step using a simple trajectory calculation of molecules as they travel through the domain of interest.

Bannister, Tommy↗

NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗

NASA NTRS2007

Recent NASA Research on Aerodynamic Modeling of Post-Stall and Spin Dynamics of Large Transport Airplanes

A simulation study was conducted to investigate aerodynamic modeling methods for prediction of post-stall flight dynamics of large transport airplanes. The research approach involved integrating dynamic wind tunnel data from rotary balance and forced oscillation testing with static wind tunnel data to predict aerodynamic forces and moments during highly dynamic departure and spin motions. Several state-of-the-art aerodynamic modeling methods were evaluated and predicted flight dynamics using these various approaches were compared. Results showed the different modeling methods had varying effects on the predicted flight dynamics and the differences were most significant during uncoordinated maneuvers. Preliminary wind tunnel validation data indicated the potential of the various methods for predicting steady spin motions.

Murch, Austin M.↗

NASA NTRSDate not supplied

Exploration Medical Capability Systems Engineering Overview and Update

Long duration Lunar and Martian missions will change the way NASA currently practices medicine. The missions will require more autonomous capability compared to current low Earth orbit operations. For the medical system, lack of consumable resupply, evacuation opportunities, and real-time ground support are key drivers toward greater autonomy. Recognition of the limited mission and vehicle resources available to carry out exploration missions motivates the Exploration Medical Capability (ExMC) Element’s approach to enabling the necessary autonomy. The ExMC Systems Engineering (SE) team’s mission is to “Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds.” The Element’s work must integrate with the overall exploration mission and vehicle design efforts to successfully provide exploration medical capabilities. ExMC is using Model-Based System Engineering (MBSE) to accomplish its integrative goals. The MBSE approach to medical system design offers a paradigm shift toward greater integration between the vehicle and the medical system, and directly supports the transition of Earth-reliant ISS operations to the Earth-independent operations envisioned for Mars. This talk will provide a high-level overview of what the ExMC SE team has accomplished since the last IWS and what they are focusing on for the upcoming year.

Kerry McGuire↗

NASA NTRS1997

Revised Simulation Model of the Control System, Displays, and Propulsion System for a ASTOVL Lift Fan Aircraft

This report describes revisions to a simulation model that was developed for use in piloted evaluations of takeoff, transition, hover, and landing characteristics of an advanced short takeoff and vertical landing lift fan fighter aircraft. These revisions have been made to the flight/propulsion control system, head-up display, and propulsion system to reflect recent flight and simulation experience with short takeoff and vertical landing operations. They include nonlinear inverse control laws in all axes (eliminating earlier versions with state rate feedback), throttle scaling laws for flightpath and thrust command, control selector commands apportioned based on relative effectiveness of the individual controls, lateral guidance algorithms that provide more flexibility for terminal area operations, and a simpler representation of the propulsion system. The model includes modes tailored to the phases of the aircraft's operation, with several response types which are coupled to the aircraft's aerodynamic and propulsion system effectors through a control selector tailored to the propulsion system. Head-up display modes for approach and hover are integrated with the corresponding control modes. Propulsion system components modeled include a remote lift fan and a lift-cruise engine. Their static performance and dynamic responses are represented by the model. A separate report describes the subsonic, power-off aerodynamics and jet induced aerodynamics in hover and forward flight, including ground effects.

Franklin, James A.↗

NASA NTRS2018

Solution of Ordinary Differential Equations in Gradient-Based Multidisciplinary Design Optimization

A gradient-based approach to multidisciplinary design optimization enables efficient scalability to large numbers of design variables. However, the need for derivatives causes difficulties when integrating ordinary differential equations (ODEs) in models. To simplify this, we propose the use of the general linear methods framework, which unifies all Runge-Kutta and linear multistep methods. This approach enables rapid implementation of integration methods without the need to differentiate each one, even in a gradient-based optimization context. We also develop a new parallel time integration algorithm that enables vectorization across time steps. We present a set of benchmarking results using a stiff ODE, a non-stiff nonlinear ODE, and an orbital dynamics ODE, and compare integration methods. In a modular gradient-based multidisciplinary design optimization context, we find that the new parallel time integration algorithm with high-order implicit methods, especially Gauss-Legendre collocation, is the best choice for a broad range of problems.

Hwang, John T.↗

NASA NTRS2009

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include (1) diagnostic/detection methodology, (2) prognosis/lifing methodology, (3) diagnostic/prognosis linkage, (4) experimental validation, and (5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multimechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

Arnold, Steven M.↗

NASA NTRS2009

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

Arnold, Steven M.↗