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Results for “model-based predictive control”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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65 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

An Approach for Uncertainty Quantification and Management of Unmanned Aerial Vehicle Health

The increasing interest in low-altitude unmanned aerial vehicle (UAV) operations is bringing along safety concerns. Performance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is lower when compared to reliability of commercial aircrafts, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of different size, propulsion systems, dynamic performance and reliability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research works and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitoring, but uncertainty quantification is rarely addressed. Thus, this paper pro- poses a perspective towards uncertainty quantification for autonomous systems, giving special emphasis to a UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is used as case study. The example illustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.

Health Monitoring↗
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗
NASA NTRSDate not supplied

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

Multi-model Monte Carlo methods have been demonstrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation among the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Preliminary results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC. The challenges and potential benefits to exploring a fully iterative and comprehensive optimization strategy in future work are highlighted.

uncertainty quantification↗
NASA NTRS2020

ROMAN CGI Testbed WFSC Modeling and Validation

Optical diffraction and wavefront sensing and control (WFSC) models validated against the high-fidelity Roman Space Telescope Coronagraph Instrument (CGI) testbed play a key role in mask design selection and the verification of many requirements that cannot be accomplished until the observatory is in orbit. We have been steadily improving our model fidelity for the as-built CGI testbed system, demonstrating recently good agreement between measurements and model predictions while validating the Hybrid Lyot Coronagraph’s (HLC) performance using the in-orbit high order wavefront sensing and control (HOWFSC) operational scenario. We present modeling and testbed validation results that explain the reason many testbed WFSC iterations were needed for HLC in the past. A new, direct application of model-generated deformable mirror (DM) solutions has since been successfully demonstrated on the testbed with significant speed and performance improvement. The benefit of using such a solution opens up new model-based WFSC approaches for CGI. This can greatly reduce flight risk from potentially insufficient ground solution generation due to schedule or cost constraints or from unexpected post-delivery changes.

Poberezhskiy, Ilya↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗
NASA NTRSDate not supplied

Developing and Testing a Common Space Systems Ontology using the Ontological Modeling Language

This paper describes the development and testing of the initial version of a common space systems ontology (CoSSO) for use by the Advanced Concepts Office (ACO) at NASA's Marshall Space Flight Center. The ontology provides a shared conceptualization of concepts of interest to the ACO for modeling aerospace systems concepts in a pre-phase A context to aid with the transition to a more model-based paradigm. The ontological concepts and relations, as well as the anticipated use cases, were developed through interactions with the relevant subject matter experts at the ACO and implemented in the Ontological Modeling Language (OML). The ontology builds on the Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). While most of the ontology is still in the initial stages, an Environmental Control and Life Support System (ECLSS) ontology is being built on top of the main CoSSO and heavily developed as a proof of concept. The ECLSS ontology is designed with different use cases in mind, namely predicting and diagnosing errors in ECLS systems on long-duration missions, with a focus on the Four-Bed CO$_2$ carbon dioxide scrubber currently on board the ISS. The ECLSS ontology is being developed in a similar manner to the CoSSO, and designed to be compatible with it. The current state of both ontologies is presented and discussed, along with plans for future development and testing.

Conceptual Design↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗
NASA NTRSDate not supplied

A Novel Additive Manufacturing Process Metric for Predicting Spatter-related Porosity in Laser Powder Bed Fusion

Components fabricated using the powder bed fusion laser beam metallic (PBF) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Porosity defects resulting from weld pass inconsistencies are a concern for PBF materials and have a strong adverse effect on the mechanical properties. In an effort to understand and avoid spatter induced porosity, this work aims to provide a novel computational technique that can be used to quantify and predict the sequence-sensitive impact of spatter upon the PBF process stability and consequential porosity in as-printed material. A novel spatter impact AM model-based process metric (AM-PM) is introduced to model and quantify the impact that spatter has on the PBF process. The occurrence of spatter induced porosity is studied using thermal rise, lack of fusion, and spatter impact AM-PMs synchronized with porosity measured by high-resolution X-ray computed tomography. Six cylindrical specimens were designed to compare the AM-PM correlations with porosity across two different laser powers and three hatch scanning strategies. A point field based computational approach is shown to be effective for testing the influence of the different AM-PMs and interrogating the physical process conditions underlying the porosity formation. Further, characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated with the spatter impact AM-PM, which can be exploited to predict and control spatter related porosity by the hatch strategy. Efforts to predict porosity composition within PBF material should incorporate the PBF process disturbances that can result from spatter. The spatter impact AM-PM can be readily incorporated into the development of defect prediction models at the part scale.

Additive Manufacturing↗
NASA NTRSDate not supplied

Aero-Propulsive Damping Characterization for eVTOL Aircraft Using Free Motion Wind-Tunnel Testing

This paper describes an electric vertical takeoff and landing (eVTOL) aircraft system identification method applied using three degree-of-freedom (3DOF) free motion wind-tunnel testing. The approach, similar to flight-test system identification, allows for efficient mathematical model development of the aero-propulsive moments applied on an eVTOL vehicle, including aerodynamic damping effects. The approach is demonstrated using a subscale tiltrotor eVTOL aircraft mounted on a new 3DOF wind-tunnel apparatus. To execute the test, a model-based 3DOF control system is designed to track attitude commands and transition the aircraft based on the freestream dynamic pressure. While the flight controller is active, orthogonal phase-optimized multisine inputs are injected into the attitude command and control effector command signals to enable collection of informative data for model identification. Aero-propulsive models are then identified at several reference conditions in the transition flight envelope using the equation-error method in the frequency domain. The identified models are shown to have a good fit to the modeling data and good prediction capability of data not used for model identification. The method yields aerodynamic damping estimates using less wind-tunnel test time compared to traditional forced oscillation experiments and supplements static wind-tunnel testing to produce a comprehensive transition aero-propulsive model suitable for use in flight dynamics simulations.

RAVEN↗