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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

Globus, Al↗
NASA NTRS2005

An Ecological Forecasting Agent

The project goals are: Make data analysis faster and cheaper. Increase use of NASA data by removing barriers to data access. Cope with data heterogeneity. Support code reuse and rapid application development. Support multiple applications, users. Including fire and health domains. Improve QOS. Always provide an answer. Tell user how good it is, where it come from.

Golden, Keith↗
NASA NTRS2009

A Distributed Prognostic Health Management Architecture

This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element ( CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.

Bhaskar, Saha↗
NASA NTRS2004

Hard Real-Time: C++ Versus RTSJ

In the domain of hard real-time systems, which language is better: C++ or the Real-Time Specification for Java (RTSJ)? Although ordinary Java provides a more productive programming environment than C++ due to its automatic memory management, that benefit does not apply to RTSJ when using NoHeapRealtimeThread and non-heap memory areas. As a result, RTSJ programmers must manage non-heap memory explicitly. While that's not a deterrent for veteran real-time programmers-where explicit memory management is common-the lack of certain language features in RTSJ (and Java) makes that manual memory management harder to accomplish safely than in C++. This paper illustrates the problem for practitioners in the context of moving data and managing memory in a real-time producer/consumer pattern. The relative ease of implementation and safety of the C++ programming model suggests that RTSJ has a struggle ahead in the domain of hard real-time applications, despite its other attractive features.

programming model↗
NASA NTRS2012

Effect of Inflow Boundary Conditions on Hovering Tilt-Rotor Flows

A study on the effect of inflow turbulence boundary conditions on the local flow on and around a body in flight has been carried out. The study has been carried out using OVERFLOW2 flow solver using the default Spalart-Allmaras turbulence model in OVERFLOW. Many OVERFLOW turbulent flow simulations have been reported using the SA-fv3 model1 over the years. The present study demonstrates that the turbulence levels imposed as a boundary condition (b.c.) in the far-field as implemented in OVERFLOW are not correct. In fact, very low level of turbulence at the far-field boundary as implemented in OVERFLOW results in the SA-fv3 model predicting transition-like profiles on a given body. By choosing sufficiently high levels of the Reynolds number of turbulence, Rt, as a boundary condition, this anomalous behavior of SA-fv3 model is eliminated. Since numerous papers using OVERFLOW have been presented in the literature including that by the author2 using low level of inflow turbulence, it will be beneficial to the CFD community at large and in particular to the OVERFLOW community to understand this effect of the inflow b.c. in the SA-fv3 model. Various results reported over the years in domains such as high-lift applications, drag prediction applications and rotorcraft flow applications can be revisited using the new turbulence inflow boundary condition prescription as suggested in this study. It has been demonstrated in the literature that the effect of inflow turbulence level on the downstream development of flow is too important to be ignored. Demonstrative results are shown and compared with experiment for the case of hovering XV-15 rotor flow at a tip Reynolds number of 4.9 x 10(exp 6) and Mach number of 0.69 in Figs. 1 and 2 below. Fig. 1(a,b) shows skin friction predictions at radial station of r/R = 0.28 corresponding to 5 different inflow boundary conditions obtained with the SA-fv3 model. As the inflow turbulence is increased monotonically, from a value of Rt = 0.1 to 100.0 as shown in Fig. 1, a fully developed turbulent skin friction profile is realized progressively as shown in Fig. 1(b). Similarly skin friction results shown in Fig. 2(a,b) corresponding to radial stations of r/R = 0.72 reinforce the postulate made here that as the inflow turbulence level is increased gradually, fully turbulent on-the-blade simulation is realized uniformly. In the full paper extensive results on skin friction, pressure and velocity profiles as well as the Figure of Merit will be presented and compared with experiment that will illustrate the substantial effect of the inflow boundary conditions on the development of hovering rotor flow downstream.

Kaul, Upender K.↗
NASA NTRS2014

Using SpF to Achieve Petascale for Legacy Pseudospectral Applications

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community. We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. Highlevel abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely inprocessor. The granularity of domain decomposition provided by SpF is only constrained by the datalocality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures. In this presentation, we will describe our experience in porting legacy pseudospectral models, MoSST and DYNAMO, to use SpF as well as present preliminary performance results provided by the improved scalability.

DYNAMO↗
NASA NTRS2015

Conjunction Assessment Past, Present, and Future

Since 1957, humankind's reliance on the space domain for military, humanitarian, and commercial applications has continued to increase. 1960 first successful use of a meteorological satellite,1963 first use of a geosynchronous communications satellite, 1985 Block I of GPS fielded, 1998 first module of ISS, 2001 first satellite radio broadcast over North America. What you take into space, stays in space: launch vehicle, rocket-bodies, mission-related debris. Debris can also be generated on-orbit: fuel/battery explosions, collisions. Only naturally-occurring retarding effect is orbital decay due to atmospheric drag: some remediation measures available, active debris removal not yet viable option. Because of our reliance on space and the fact that space really isn't limitless, the Big Sky theory is no longer an acceptable risk posture. There have been eight (8) on-orbit collisions reported to date, half of which occurred in the last 10 years.

Assessment↗
NASA NTRS2017

Developing a General Framework for Human Autonomy Teaming

Automation has entered nearly every aspect of our lives, but it often remains hard to understand. Why is this? Automation is often brittle, requiring constant human oversight to assure it operates as intended. This oversight has become harder as automation has become more complicated. To resolve this problem, Human-Autonomy Teaming (HAT) has been proposed. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. This paper proposes a framework for HAT, incorporating two key tenets: bi-directional communication, and operator directed authority. We have successfully applied these tenets to integrating the autonomous constrained flight planner (an aide for planning diverts) into a dispatch station. We propose the development of general design patterns that may allow these results to be generalized to domains such as photography and automotive navigation. While these domains are very different, we find application of our HAT tenets provides a number of opportunities for improving interaction between human operators and automation.

human factors↗
NASA NTRS2017

Estimates for the Aerodynamic Coefficients of Ringsail and Disk-Gap-Band Parachutes Operating on Mars

Models are presented for the aerodynamic coefficients of Supersonic Ringsail and Disk-Gap-Band parachutes as functions of total porosity, Lambda(sub t), Mach number, M, and total angle of attack, Alpha(sub t) (when necessary). The source aerodynamic coefficients data used for creating these models were obtained from a wind tunnel test of subscale parachutes. In this wind tunnel test, subscale parachutes of both parachute types were fabricated from two different fabrics with very different permeabilities. By varying the fabric permeability, while maintaining the parachute geometry constant, it was possible to vary Alpha(sub t). The fabric permeability test data necessary for the calculation of Alpha(sub t) were obtained from samples of the same fabrics used to fabricate the subscale parachutes. Although the models for the aerodynamic coefficients are simple polynomial functions of Alpha(sub t) and M, they are capable of producing good reproductions of the source data. The (Alpha(sub t), M) domains over which these models are applicable are clearly defined. The models are applicable to flight operations on Mars.

Cruz, Juan R.↗
NASA NTRS2017

Human-Robot Teaming: Communication, Coordination, and Collaboration

In this talk, I will describe how NASA Ames has been studying how human-robot teams can increase the performance, reduce the cost, and increase the success of a variety of endeavors. The central premise of our work is that humans and robots should support one another in order to compensate for limitations of automation and manual control. This principle has broad applicability to a wide range of domains, environments, and situations. At the same time, however, effective human-robot teaming requires communication, coordination, and collaboration -- all of which present significant research challenges. I will discuss some of the ways that NASA Ames is addressing these challenges and present examples of our work involving planetary rovers, free-flying robots, and self-driving cars.

robotics↗
NASA NTRS2017

Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation

Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.

Bakowski, Deborah L.↗
NASA NTRS2017

Beyond Point Design: General Pattern to Specific Implementations

Elsewhere we have discussed a number of problems typical of highly automated systems and proposed tenets for addressing these problems based on Human-Autonomy Teaming (HAT)[1]. We have examined these principles in the context of aviation [2,3]. Here we discuss the generality of these tenets by examining how they might be applied to photography and automotive navigation. While these domains are very different, we find application of our HAT tenets provides a number of opportunities for improving interaction between human operators and automation. We then illustrate how the generalities found across aviation, photography and navigation can be captured in a design pattern.

Lachter, Joel Benjamin↗
NASA NTRS2018

GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning

Obtaining accurate segmentation on large scale images is an open problem in deep learning. The main problem is the amount of labeled data that exists for large scale images. Traditionally, the common solution to this problem is to crop the large images into smaller images to increase the amount of available data and train a Conditional Generative Adversarial Network (CGAN). CGANs are currently the state of the art in image to image translation and provide better accuracy than the traditional method of training a encoder based conv-net architecture to minimize the loss at each pixel. This method can produce noisy and discontinuous images with inaccurate results. We seek to solve this problem by utilizing the concepts of transfer learning and progressive training to create a CGAN that can segment large scale images with a limited amount of labeled data. In transfer learning we recognize that many learned features are applicable to many classes from multiple domains. This introduces the concept of feature reusability, which is the basis for finetuning. Progressive training got its start in training models on the same images at different resolutions. In this work we instead train a GAN on increasing image scales by transferring the weights from the smaller scales to the larger scales. The learned features at the smaller scales are continually reused and applied to larger scales to create a CGAN that can perform accurate segmentation on large scale images. We apply this method to detect building footprints on very high-resolution overhead imagery (e.g Digital Globe and high resolution airborne platforms).

GEONEX↗
NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗
NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗
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↗