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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

SCOSII OL: A dedicated language for mission operations

The Spacecraft Control and Operations System 2 (SCOSII) is the new generation of Mission Control Systems (MCS) to be used at ESOC. The system is generic because it offers a collection of standard functions configured through a database upon which a dedicated MCS is established for a given mission. An integral component of SCOSII is the support of a dedicated Operations Language (OL). The spacecraft operation engineers edit, test, validate, and install OL scripts as part of the configuration of the system with, e.g., expressions for computing derived parameters and procedures for performing flight operations, all without involvement of software support engineers. A layered approach has been adopted for the implementation centered around the explicit representation of a data model. The data model is object-oriented defining the structure of the objects in terms of attributes (data) and services (functions) which can be accessed by the OL. SCOSII supports the creation of a mission model. System elements as, e.g., a gyro are explicit, as are the attributes which described them and the services they provide. The data model driven approach makes it possible to take immediate advantage of this higher-level of abstraction, without requiring expansion of the language. This article describes the background and context leading to the OL, concepts, language facilities, implementation, status and conclusions found so far.

Baldi, Andrea↗
NASA NTRS2016

Precipitation and Carbon-Water Coupling Jointly Control the Interannual Variability of Global Land Gross Primary Production

Carbon uptake by terrestrial ecosystems is increasing along with the rising of atmospheric CO2 concentration. Embedded in this trend, recent studies suggested that the interannual variability (IAV) of global carbon fluxes may be dominated by semi-arid ecosystems, but the underlying mechanisms of this high variability in these specific regions are not well known. Here we derive an ensemble of gross primary production (GPP) estimates using the average of three data-driven models and eleven process-based models. These models are weighted by their spatial representativeness of the satellite-based solar-induced chlorophyll fluorescence (SIF). We then use this weighted GPP ensemble to investigate the GPP variability for different aridity regimes. We show that semi-arid regions contribute to 57% of the detrended IAV of global GPP. Moreover, in regions with higher GPP variability, GPP fluctuations are mostly controlled by precipitation and strongly coupled with evapotranspiration (ET). This higher GPP IAV in semi-arid regions is co-limited by supply (precipitation)-induced ET variability and GPP-ET coupling strength. Our results demonstrate the importance of semi-arid regions to the global terrestrial carbon cycle and posit that there will be larger GPP and ET variations in the future with changes in precipitation patterns and dryland expansion.

chlorophyll fluorescence↗
NASA NTRS2019

Dust Abrasion Damage on Martian Solar Arrays: Experimental Investigation and Opportunity Rover Performance Analysis

Here we investigate the effects of erosion and weathering that occur on III-V cover-glass interconnected cells (CICs) after exposure to Mars dust storm conditions. The durability of these materials in a Martian environment is not well characterized so we perform analogous experimentation. To replicate the dust impingement, test coupons were placed in an enclosure and sandblasted with Mars dust simulant. We show the J-V response dependency on both incident angle and exposure times. We find that the simulated Martian dust storm often results in damage to the anti-reflective coating and subsequent reduced short circuit current. Reduction in the open circuit voltage is observed, likely caused by structural damage to the crystal lattice after CIC fracture. We employ data-driven modeling to determine a performance degradation rate that is consistent with zero within uncertainty. We also quantify the soiling contribution and power degradation of the photovoltaic cells on Mars through analysis of 4.95 Martian years of report-out power conditions from the Opportunity rover. We find that atmospheric dust suspended due to a weather event does not result in instantaneous settled dust on the PV cells. We calculate via autocorrelation function that the dust settling rate is approximately 21 Sols from atmospheric dust suspension. The findings presented here deliver a realistic approximation for the insolation values and subsequent PV power expected over time on the Martian surface thus informing future dust abatement systems.

McMillon-Brown, Lyndsey↗
NASA NTRS2019

Dust Abrasion Damage on Martian Solar Arrays: Experimental Investigation and Opportunity Rover Performance Analysis

Here we investigate the effects of erosion and weathering that occur on III-V cover-glass interconnected cells (CICs) after exposure to Mars dust storm conditions. The durability of these materials in a Martian environment is not well characterized so we perform analogous experimentation. To replicate the dust impingement, test coupons were placed in an enclosure and sandblasted with Mars dust simulant. We show the J-V response dependency on both incident angle and exposure times. We find that the simulated Martian dust storm often results in damage to the anti-reflective coating and subsequent reduced short circuit current. Reduction in the open circuit voltage is observed, likely caused by structural damage to the crystal lattice after CIC fracture. We employ data-driven modeling to determine a performance degradation rate that is consistent with zero within uncertainty. We also quantify the soiling contribution and power degradation of the photovoltaic cells on Mars through analysis of 4.95 Martian years of report-out power conditions from the Opportunity rover. We find that atmospheric dust suspended due to a weather event does not result in instantaneous settled dust on the PV cells. We calculate via autocorrelation function that the dust settling rate is approximately 21 Sols from atmospheric dust suspension. The findings presented here deliver a realistic approximation for the insolation values and subsequent PV power expected over time on the Martian surface thus informing future dust abatement systems.

McMillon-Brown, Lyndsey↗
NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗
NASA NTRS2023

Simulation of the Solar Energetic Particle Event on 2020 May 29 Observed by Parker Solar Probe

This paper presents a stochastic three-dimensional focused transport simulation of solar energetic particles (SEPs) produced by a data-driven coronal mass ejection (CME) shock propagating through a data-driven model of coronal and heliospheric magnetic fields. The injection of SEPs at the CME shock is treated using diffusive shock acceleration of post-shock suprathermal solar wind ions. A time-backward stochastic simulation is employed to solve the transport equation to obtain the SEP time–intensity profile at any location, energy, and pitch angle. The model is applied to a SEP event on 2020 May 29, observed by STEREO-A close to ∼1 au and by Parker Solar Probe (PSP) when it was about 0.33 au away from the Sun. The SEP event was associated with a very slow CME with a plane-of-sky speed of 337 km s −1 at a height below 6 RS as reported in the SOHO/LASCO CME catalog. We compute the time profiles of particle flux at PSP and STEREO-A locations, and estimate both the spectral index of the proton energy spectrum for energies between ∼2 and 16 MeV and the equivalent path length of the magnetic field lines experienced by the first arriving SEPs. We find that the simulation results are well correlated with observations. The SEP event could be explained by the acceleration of particles by a weak CME shock in the low solar corona that is not magnetically connected to the observers.

Solar energetic particles↗
NASA NTRS2022

Learning-Based State-Dependent Coefficient Form Task Space Tracking Control of Soft Robot

n this paper, a data-driven modeling and control framework is developed for task space control of a soft robot gripper which consists of four individual soft fingers. Each of the four fingers is modeled as a manipulator with high degrees of freedom. The corresponding task space dynamics of the manipulator are derived using a rigid-link approximation of the continuum manipulator. A neural network approach is used to learn the derived dynamics in State Dependent Coefficient (SDC) form. Using the learned SDC matrices, an asymptotically stable optimal closed-loop tracking controller which is based on solving the State Dependent Riccati Equation (SDRE) is derived. The model learning and trajectory tracking controller is implemented on an open source Soft Motion (SoMo) platform simulating the soft gripper motion and corresponding tracking results are presented.

Rounak Bhattacharya↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRS2024

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Zixin Wang↗
NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗
NASA NTRS2024

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Daniel Bruder↗
NASA NTRS2004

Model-Biased, Data-Driven Adaptive Failure Prediction

This final report, which contains a research summary and a viewgraph presentation, addresses clustering and data simulation techniques for failure prediction. The researchers applied their techniques to both helicopter gearbox anomaly detection and segmentation of Earth Observing System (EOS) satellite imagery.

Leen, Todd K.↗
NASA NTRS2021

Self-Consistent Implementation of a Zero-Equation Transport Model Into a Predictive Model for a Hall Effect Thruster

The performance of an axisymmetric multi-fluid Hall thruster code that incorporates a self-consistent, data-driven closure model for the anomalous electron transport is investigated. Five different operating conditions of the H9 magnetically shielded Hall thruster are simulated with the Jet Propulsion Laboratory’s Hall2De. In order to capture the inherent uncertainty associated with the closure model, O(100) simulations are run for each condition, each of using a coefficient set sampled randomly from a probability distribution. The results of these simulations provide probabilistic predictions of thruster performance quantities including thrust, and discharge current, as well as several component efficiencies and centerline plasma properties. The model is found to yield converged solutions at all conditions, with large 10 kHzrange oscillations and performance trends with voltage and flow rate similar to experiment. The model under-predicts the thrust by 15-25% and over-predicts the discharge current by 20% on average compared to experiments at the same discharge voltage and mass flow rate. This performance discrepancy is due to lower beam utilization, mass utilization, and divergence efficiency than experiment, resulting from high Hall parameters in the acceleration region, which lead to a protracted ion acceleration region. The physical processes underlying this result are discussed in the context of future data-driven modeling efforts.

Jorns, Benjamin A.↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
NASA NTRSDate not supplied

Application of Flux Conserved Modeling to an Unsteady Combustion Driven Pseudo-Shock

Quasi-steady correlation-based and flux conserved models of the pseudo-shock pressure profile are experimentally assessed in the Air Force Research Lab RC-18 Sub-Scale DirectConnect Isolator Combustion Facility. A bifurcated normal pseudo-shock is established in the 2.67 aspect ratio isolator test section by a downstream cavity-stabilized combustor. The Waltrup-Billig correlation, Smart flux conserved model, and Fiévet data driven flux conserved models are compared against measured pressure profiles. The literature models are found to perform poorly in a high-amplitude forced environment of RC-18, where shock speeds upwards of 20% the isolator Mach number occur. Two modifications to the quasi-steady Fiévet data driven flux conserved model are proposed including incorporating an estimate of pseudo-shock static pressure recovery and exercising the model in a shock stationary reference frame. It is shown that these modifications allow the quasi-steady flux conserved modeling approach to accurately predict pseudo-shock pressure profiles over the discrete phases of high-amplitude forced dynamics.

Shock Train↗