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NASA NTRS2015

Use of a Viscous Flow Simulation Code for Static Aeroelastic Analysis of a Wing at High-Lift Conditions

In this paper, we present a static aeroelastic analysis of a wind tunnel test model of a wing in high-lift configuration using a viscous flow simulation code. The model wing was tailored to deform during the tests by amounts similar to a composite airliner wing in highlift conditions. This required use of a viscous flow analysis to predict the lift coefficient of the deformed wing accurately. We thus utilized an existing static aeroelastic analysis framework that involves an inviscid flow code (Cart3d) to predict the deformed shape of the wing, then utilized a viscous flow code (Overflow) to compute the aerodynamic loads on the deformed wing. This way, we reduced the cost of flow simulations needed for this analysis while still being able to predict the aerodynamic forces with reasonable accuracy. Our results suggest that the lift of the deformed wing may be higher or lower than that of the non-deformed wing, and the washout deformation of the wing is the key factor that changes the lift of the deformed wing in two distinct ways: while it decreases the lift at low to moderate angles of attack simply by lowering local angles of attack along the span, it increases the lift at high angles of attack by alleviating separation.

Aeroelastic↗

NASA NTRS2006

A Framework for Performing Multiscale Stochastic Progressive Failure Analysis of Composite Structures

A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis - Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.

Bednarcyk, Brett A.↗

NASA NTRS2007

A Framework for Performing Multiscale Stochastic Progressive Failure Analysis of Composite Structures

A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis-Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.

Bednarcyk, Brett A.↗

NASA NTRS2002

A Closed-Loop Hardware Simulation of Decentralized Satellite Formation Control

In recent years, there has been significant interest in the use of formation flying spacecraft for a variety of earth and space science missions. Formation flying may provide smaller and cheaper satellites that, working together, have more capability than larger and more expensive satellites. Several decentralized architectures have been proposed for autonomous establishment and maintenance of satellite formations. In such architectures, each satellite cooperatively maintains the shape of the formation without a central supervisor, and processing only local measurement information. The Global Positioning System (GPS) sensors are ideally suited to provide such local position and velocity measurements to the individual satellites. An investigation of the feasibility of a decentralized approach to satellite formation flying was originally presented by Carpenter. He extended a decentralized linear-quadratic-Gaussian (LQG) framework proposed by Speyer in a fashion similar to an extended Kalman filter (EKE) which processed GPS position fix solutions. The new decentralized LQG architecture was demonstrated in a numerical simulation for a realistic scenario that is similar to missions that have been proposed by NASA and the U.S. Air Force. Another decentralized architecture was proposed by Park et al. using carrier differential-phase GPS (CDGPS). Recently, Busse et al demonstrated the decentralized CDGPS architecture in a hardware-in-the-loop simulation on the Formation Flying TestBed (FFTB) at Goddard Space Flight Center (GSFC), which features two Spirent Cox 16 channel GPS signal generator. Although representing a step forward by utilizing GPS signal simulators for a spacecraft formation flying simulation, only an open-loop performance, in which no maneuvers were executed based on the real-time state estimates, was considered. In this research, hardware experimentation has been extended to include closed-loop integrated guidance and navigation of multiple spacecraft formations using GPS receivers and real-time vehicle telemetry. A hardware closed-loop simulation has been performed using the decentralized LQG architecture proposed by Carpenter in the GPS test facility at the Center for Space Research (CSR). This is the first presentation using this type of hardware for demonstration of closed-loop spacecraft formation flying.

Ebimuma, Takuji↗

NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite NO2 Data Assimilation and Improvements in Background NOx Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

V. Shah↗

NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗

NASA NTRSDate not supplied

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗

NASA NTRS1992

Self-organization via active exploration in robotic applications

We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization of sequence of actions.

Ogmen, H.↗

NASA NTRS2015

A Sampling Based Approach to Spacecraft Autonomous Maneuvering with Safety Specifications

This paper presents a methods for safe spacecraft autonomous maneuvering that leverages robotic motion-planning techniques to spacecraft control. Specifically the scenario we consider is an in-plan rendezvous of a chaser spacecraft in proximity to a target spacecraft at the origin of the Clohessy Wiltshire Hill frame. The trajectory for the chaser spacecraft is generated in a receding horizon fashion by executing a sampling based robotic motion planning algorithm name Fast Marching Trees (FMT) which efficiently grows a tree of trajectories over a set of probabillistically drawn samples in the state space. To enforce safety the tree is only grown over actively safe samples for which there exists a one-burn collision avoidance maneuver that circularizes the spacecraft orbit along a collision-free coasting arc and that can be executed under potential thrusters failures. The overall approach establishes a provably correct framework for the systematic encoding of safety specifications into the spacecraft trajectory generations process and appears amenable to real time implementation on orbit. Simulation results are presented for a two-fault tolerant spacecraft during autonomous approach to a single client in Low Earth Orbit.

Spacecraft↗

NASA NTRS2014

Evaluating LSM-Based Water Budgets Over a West African Basin Assisted with a River Routing Scheme

Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Oum River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region,a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005-08 AMMA field campaign period during which rainfall and stream flow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily stream flow at five gauges within the basin. Results demonstrate that the RRS simulates stream flow at all gauges with relative errors varying from -22% to 3% and Nash-Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the sub-basin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated stream flows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.

Multidisciplinary↗

NASA NTRS2018

Urban Air Mobility Airspace Integration Concepts and Considerations

Urban Air Mobility (UAM) - defined as safe and efficient air traffic operations in a metropolitan area for manned aircraft and unmanned aircraft systems - is being researched and developed by industry, academia, and government. Significant resources have been invested toward cultivating an ecosystem for Urban Air Mobility that includes manufacturers of electric vertical takeoff and landing aircraft, builders of takeoff and landing areas, and researchers of the airspace integration concepts, technologies, and procedures needed to conduct Urban Air Mobility operations safely and efficiently alongside other airspace users. This paper provides high-level descriptions of both emergent and early expanded operational concepts for Urban Air Mobility that NASA is developing. The scope of this work is defined in terms of missions, aircraft, airspace, and hazards. Past and current Urban Air Mobility operations are also reviewed, and the considerations for the data exchange architecture and communication, navigation, and surveillance requirements are also discussed. This paper will serve as a starting point to develop a framework for NASA's Urban Air Mobility airspace integration research and development efforts with partners and stakeholders that could include fast-time simulations, human-in-the-loop (HITL) simulations, and flight demonstrations.

airspace integration↗

NASA NTRSDate not supplied

Building Collaborative Proving Grounds for R2O2R – NASA/CCMC - NOAA/Space Weather Prediction Testbed (SWPT) Partnership

In 2016 and 2019, Executive Orders were signed by the President to prepare the Nation for Space Weather Events. At the forefront of being able to achieve this goal is the necessity to accelerate and enhance R2O2R. To drive this initiative, the Space Weather R2O2R Framework, initially led by NOAA and NASA, was developed. At the very center of the framework is the Space Weather Proving Grounds effort that will allow various institutions to collaborate, share, and advance their space weather modeling and simulation capabilities. The first of these Proving Grounds is the Architecture for Collaborative Evaluation (ACE) environment shared between the Community Coordinated Modeling Center (CCMC) at NASA and the Space Weather Prediction Testbed (SWPT) at NOAA. Since 2019, the ACE environment has been set up on the AWS cloud where it utilizes AWS cloud services and Docker containerization technology.

T. Tsui↗

NASA NTRSDate not supplied

Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗

NASA NTRS2023

Evaluation of a Regional Crop Model Implementation for Sub-National Yield Assessments in Kenya

CONTEXT: Cropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVE: This study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODS: The RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONS: The median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ~1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCE: One of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Crop model↗

NASA NTRSDate not supplied

Ab initio ground states of strongly-correlated materials on quantum computers

The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving accurate many-body Hamiltonians including strong correlations, representing a subspace of interest of a material, using density functional theory calculations as a starting point. However, the solution of these material-specific models can scale exponentially on classical computers, constituting a challenge. Here we propose that utilizing quantum computers for obtaining the properties of downfolded Hamiltonians yields an accurate description of the ground state properties of strongly-correlated systems, while circumventing the exponential scaling problem. We benchmark the solution of Hubbard-like models obtained through downfolding by utilizing a classical tensor network implementation of variational quantum eigensolvers (VQE), and we reveal a strategy for driving the optimization through a hybrid minimization of the energy and maximization of the overlap with an approximate solution obtained through low-cost computational methods. This results in a reduction of the energy error by orders of magnitude compared to conventional VQE approaches, and allows us to reproduce long-range correlations for the first time. We demonstrate our first-principles approach for diverse strongly-correlated materials, correctly predicting the antiferromagnetic state of one-dimensional cuprate Ca 2 CuO 3 , the excitonic ground state of monolayer WTe2, and the charge-ordered state of correlated metal SrVO 3 . Our efficient computational implementation allows us to simulate large systems with up to 54 qubits and encompassing up to four correlated bands, which is indicative of the complexity that our framework can address.

Antonios M Alvertis↗

NASA NTRSDate not supplied

Investigating Risks Due to Artemis EVA Tempo Via Probabilistic Risk Assessment

Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.

Modeling↗

NASA NTRS2012

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Armstrong, Jeffrey B.↗

NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗