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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 523 records · Page 29

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NASA NTRS2017

Parametric Evaluation of Interstellar Exploration Mission Concepts

One persistent difficulty in evaluating the myriad advanced propulsion concepts proposed over the last 60 years is a true apples to apples comparison of the expected gain in performance. This analysis is complicated by numerous factors including, multiple missions of interest to the advanced propulsion community, the lack of a credible closed form solution to 'medium thrust' trajectories, and lack of detailed design data for most proposed concepts that lend credibility to engine performance estimates. This paper describes a process on how to make fair comparisons of different propulsion concepts for multiple missions over a wide range of performance values. The figure below illustrates this process. This paper describes in detail the process and outlines the status so far in compiling the required data. Parametric data for several missions are calculated and plotted against specific power-specific impulse scatter plots of expected propulsion system performance. The overlay between required performance as defined by the trajectory parametrics and expected performance as defined in the literature for major categories of propulsion systems clearly defines which propulsion systems are the most apt for a given mission. The application of the Buckingham Pi theorem to general parameters for interstellar exploration ( mission time, mass, specific impulse, specific power, distance, propulsion source energy/mass, etc.) yields a number of dimensionless variables. The relationships of these variables can then be explored before application to a particular mission. Like in the fields of fluid mechanics and heat transfer, the use of the Buckingham Pi theorem results in new variables to make apples to apples comparisons.

Adams, Robert B.↗

DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1985

Propeller aircraft interior noise model: User's manual for computer program

A computer program entitled PAIN (Propeller Aircraft Interior Noise) has been developed to permit calculation of the sound levels in the cabin of a propeller-driven airplane. The fuselage is modeled as a cylinder with a structurally integral floor, the cabin sidewall and floor being stiffened by ring frames, stringers and floor beams of arbitrary configurations. The cabin interior is covered with acoustic treatment and trim. The propeller noise consists of a series of tones at harmonics of the blade passage frequency. Input data required by the program include the mechanical and acoustical properties of the fuselage structure and sidewall trim. Also, the precise propeller noise signature must be defined on a grid that lies in the fuselage skin. The propeller data are generated with a propeller noise prediction program such as the NASA Langley ANOPP program. The program PAIN permits the calculation of the space-average interior sound levels for the first ten harmonics of a propeller rotating alongside the fuselage. User instructions for PAIN are given in the report. Development of the analytical model is presented in NASA CR 3813.

Wilby, E. G.↗

NASA NTRS1994

The Mars Observer differential one-way range demonstration

Current methods of angular spacecraft positioning using station differenced range data require an additional observation of an extragalactic radio source (quasar) to estimate the timing offset between the reference clocks at the two Deep Space Stations. The quasar observation is also used to reduce the effects of instrumental and media delays on the radio metric observable by forming a difference with the spacecraft observation (delta differential one-way range, delta DOR). An experiment has been completed using data from the Global Positioning System satellites to estimate the station clock offset, eliminating the need for the quasar observation. The requirements for direct measurement of the instrumental delays that must be made in the absence of a quasar observation are assessed. Finally, the results of the 'quasar-free' differential one-way range, or DOR, measurements of the Mars Observer spacecraft are compared with those of simultaneous conventional delta DOR measurements.

Kroger, P. M.↗

DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗

DOE OSTI2024

Standardized Protocol for Real-Time APIs as Required by Title 23 CFR 680.116(c)

Improving the ability of drivers to easily locate working and available chargers is key to improving the public charging experience. Electric vehicle charging providers who are recipients of federal funds through the National Electric Vehicle Infrastructure (NEVI) Formula Program, Charging and Fueling Infrastructure (CFI) Discretionary Grant Program, and other funding programs as identified under Title 23 of the U.S. Code must deploy and maintain an application programming interface (API) to access information about charging stations they operate.1 This includes information about individual charging ports, pricing, and availability in accordance with the Federal Highway Administration’s National Electric Vehicle Infrastructure Standards and Requirements, 23 CFR 680.116(c), herein referred to as the minimum standards (Federal Highway Administration 2023). Specifically outlined in the minimum standards, states and other designated recipients are required to ensure that charging station information including location, connector type, power level, real-time status, and real-time price to charge are available free of charge to third-party software developers through an API. These requirements are intended to enable effective communication with consumers about available charging stations and help consumers make informed decisions about trip planning, including when and where to charge. This document provides a standardized protocol for how to structure data, data update frequency, and practices for making the data required to be shared via API usable for improving public transparency and the customer experience. These are recommendations only and do not modify the Federal Highway Administration’s minimum standards in any way.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1981

Development of a simple, self-contained flight test data acquisition system

This paper describes a simple, self-contained flight test data acquisition system. The system makes use of the latest sensor and microprocessor technology available, to reduce overall system costs. Coupled with this is the use of modern control theory techniques allowing minimization of data requirements, as well as flight time requirements. Capability of the system includes primarily stability and performance analysis of general aviation airplanes, although system versatility has been designed into the package. Presented are details of the prototype system constructed, as well as details of the data reduction technique utilized. Preliminary results of the flight test program have also been included which demonstrate the capability of this system.

Renz, R. R. L.↗

NASA NTRS1990

Adaptive structures in space

Future NASA missions will need large (20 to 100m) structural systems with precision position (few microns to submicron) requirements. Data are presented which indicate the technology deficiencies of previous programs and analyses in current state-of-the-art structural design approaches, analytical prediction capabilities, control of structure capabilities, and ground test technologies to meet the performance requirements of future large precision structural systems. Test results on laboratory truss structures that demonstrate static displacement control, active damping, and on-orbit system identification are described. It is shown that for large precision structures, adaptive structures provide not only a means to achieve the precision and characteristics required in space, but can also significantly alleviate the ground test requirements for flight-validating the hardware.

Wada, B. K.↗

NASA NTRS1994

Infrared spectroscopy of organics of planetological interest at low temperatures

In the context of prebiotic chemistry in space, some of the outer planetary objects display H, C, N and O rich chemistry similar to the one in the biosphere of Earth. Of particular interest are Saturn's moon, Titan; Neptune's moon, Triton; and Pluto where extreme cold conditions prevail. Identifications of chemical species on these objects (surfaces and atmospheres) is essential to a better understanding of the radiation induced chemical reactions occuring thereon. There have been several ground based observations of these planetary objects in the infrared windows from 1 to 2.5 micrometers. Voyager also provided spectra in the thermal infrared (6 to 50 micrometers) region. Interpretation of these data require laboratory infrared spectra of relevant species under the temperature conditions appropriate to these objects. The results of some of these studies carried out in our laboratory and elsewhere and their impact on the analyses of the observed data will be summarized.

Khanna, R. K.↗

NASA NTRS2000

Aviation Weather Information Communications Study (AWIN)

This two part study examines the communication requirements to provide weather information in the cockpit as well as public and private communication systems available to address the requirements. Ongoing research projects combined with user needs for weather related information are used to identify and describe potential weather products that address decision support in three time frames: Far-Term Strategic, Near-Term Strategic and Tactical. Data requirements of these future products are identified and quantified. Communications systems and technologies available in the public as well as private sector are analyzed to identify potential solutions. Recommendations for further research identify cost, performance, and safety benefits to justify the investment. The study concludes that not all weather information has the same level of urgency to safety-of-flight and some information is more critical to one category of flight than another. Specific weather products need to be matched with communication systems with appropriate levels of reliability to support the criticality of the information. Available bandwidth for highly critical information should be preserved and dedicated to safety. Meanwhile, systems designed for in-flight-entertainment and other passenger/crew services could be used to support less critical information that is used only for planning and economic decision support.

Ball, J. W.↗

NASA NTRS2000

A Probabilistic Framework for the Validation and Certification of Computer Simulations

The paper presents a methodology for quantifying, propagating, and managing the uncertainty in the data required to initialize computer simulations of complex phenomena. The purpose of the methodology is to permit the quantitative assessment of a certification level to be associated with the predictions from the simulations, as well as the design of a data acquisition strategy to achieve a target level of certification. The value of a methodology that can address the above issues is obvious, specially in light of the trend in the availability of computational resources, as well as the trend in sensor technology. These two trends make it possible to probe physical phenomena both with physical sensors, as well as with complex models, at previously inconceivable levels. With these new abilities arises the need to develop the knowledge to integrate the information from sensors and computer simulations. This is achieved in the present work by tracing both activities back to a level of abstraction that highlights their commonalities, thus allowing them to be manipulated in a mathematically consistent fashion. In particular, the mathematical theory underlying computer simulations has long been associated with partial differential equations and functional analysis concepts such as Hilbert spares and orthogonal projections. By relying on a probabilistic framework for the modeling of data, a Hilbert space framework emerges that permits the modeling of coefficients in the governing equations as random variables, or equivalently, as elements in a Hilbert space. This permits the development of an approximation theory for probabilistic problems that parallels that of deterministic approximation theory. According to this formalism, the solution of the problem is identified by its projection on a basis in the Hilbert space of random variables, as opposed to more traditional techniques where the solution is approximated by its first or second-order statistics. The present representation, in addition to capturing significantly more information than the traditional approach, facilitates the linkage between different interacting stochastic systems as is typically observed in real-life situations.

Ghanem, Roger↗

NASA NTRS1961

A Method of Determining Aerodynamic-Influence Coefficients from Wind-Tunnel Data for Wings at Supersonic Speeds

A method is described for determining aerodynamic-influence coefficients from wind-tunnel data for calculating the steady-state load distribution on a wing with arbitrary angle-of-attack distribution at supersonic speeds. The method combines linearized theory with empirical adjustments in order to give accurate results over a wide range of angles of attack. The experimented data required are pressure distributions measured on a flat wing of the desired planform at the desired Mach number and over the desired range of angles of attack. The method has been tested by applying it to wind-tunnel data measured at Mach numbers of 1.61 and 2.01 on wings of the same planform but of different surface shapes. Influence coefficients adjusted to fit the flat wing gave good predictions of the spanwise and chord-wise distributions of loadings measured on twisted and cambered wings.

Gainer, Patrick A.↗

NASA NTRS2007

Progress in Modeling Global Atmospheric CO2 Fluxes and Transport: Results from Simulations with Diurnal Fluxes

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Under NASA Carbon Cycle Science support we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across a wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the SIB 2 and CASA biosphere models, we examine the model's ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of realtime, highly resolved observations into a multiscale carbon cycle analysis system that will begin to bridge the gap between top-down and bottom-up flux estimation, which is a primary focus of NACP.

Collatz, G. James↗

NASA NTRS2016

Temporary Thermocouple Attachment for Thermal/Vacuum Testing at Non-Extreme Temperatures

Post-test examination and data analysis that followed a two week long vacuum test showed that numerous self-stick thermocouples became detached from the test article. The thermocouples were reattached with thermally conductive epoxy and the test was repeated to obtain the required data. Because the thermocouple detachment resulted in significant expense and rework, it was decided to investigate the temporary attachment methods used around NASA and to perform a test to assess their efficacy. The present work describes the original test and the analysis that showed that the thermocouples had become detached, temporary thermocouple attachment methods assessed in the retest and in the thermocouple attachment test, and makes a recommendation for attachment methods for future tests.

Ungar, Eugene K.↗

NASA NTRS2016

Temporary Thermocouple Attachment for Thermal/Vacuum Testing at Non-Extreme Temperatures

Ungar, Eugene K.↗

NASA NTRS2017

Temporary Thermocouple Attachment for Thermal/Vacuum Testing at Non-Extreme Temperatures - Test Results

Post-test examination and data analysis that followed a two week long vacuum test showed that numerous self-stick thermocouples became detached from the test article. The thermocouples were reattached with thermally conductive epoxy and the test was repeated to obtain the required data. Because the thermocouple detachment resulted in significant expense and rework, it was decided to investigate the temporary attachment methods used around NASA and to perform a test to assess their efficacy. The present work describes the testing that was performed in early and mid-2017. The test article and the temporary thermocouple attachment methods tested are described. During the first test, fully half of the thermocouples detached - although the detachment showed subtly in the data for some. The second test was performed to confirm the data from the first test and to investigate the effect of test article and thermocouple grounding. The results of the testing over temperatures ranging from -150 to 200degF are detailed and preliminary recommendations are made for temporary thermocouple attachment methods.

Wright, Sarah E.↗

NASA NTRS2020

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Memarzadeh, Milad↗

NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗