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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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NASA NTRS2004

Using software security analysis to verify the secure socket layer (SSL) protocol

nal Aeronautics and Space Administration (NASA) have tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information the3, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach '' offers, among its capabilities, formal verification of software security properties, through the use of model based verification (MBV) to address software security risks. [1,2,3,4,5,6] MBV is a formal approach to software assurance that combines analysis of software, via abstract models, with technology, such as model checkers, that provide automation of the mechanical portions of the analysis process. This paper will discuss: The need for formal analysis to assure software systems with respect to software and why testing alone cannot provide it. The means by which MBV with a Flexible Modeling Framework (FMF) accomplishes the necessary analysis task. An example of FMF style MBV in the verification of properties over the Secure Socket Layer (SSL) communication protocol as a demonstration.

software↗
NASA NTRS2014

ISAAC Advanced Composites Research Testbed

The NASA Langley Research Center is acquiring a state-of-art composites fabrication capability to support the Center's advanced research and technology mission. The system introduced in this paper is named ISAAC (Integrated Structural Assembly of Advanced Composites). The initial operational capability of ISAAC is automated fiber placement, built around a commercial system from Electroimpact, Inc. that consists of a multi-degree of freedom robot platform, a tool changer mechanism, and a purpose-built fiber placement end effector. Examples are presented of the advanced materials, structures, structural concepts, fabrication processes and technology development that may be enabled using the ISAAC system. The fiber placement end effector may be used directly or with appropriate modifications for these studies, or other end effectors with different capabilities may either be bought or developed with NASA's partners in industry and academia.

Wu, K. Chauncey↗
NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗
NASA NTRS2014

Exploration Technologies for Operations

Although the International Space Station (ISS) assembly has been completed, the Operations support teams continue to seek more efficient and effective ways to prepare for and conduct the ISS operations and future exploration missions beyond low earth orbit. This search for improvement has led to a significant collaboration between the NASA research and advanced software development community at NASA Ames Research Center and the Mission Operations community at NASA Johnson Space Center. Since 2001, NASA Ames Research Center has been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for several of the unmanned Mars missions operations. Since 2006, NASA Ames Research Center has also been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for manned operations support with the Mission Operations Directorate at NASA Johnson Space Center. This paper discusses the completion of the development and deployment of a variety of intelligent and human systems technologies adopted for manned mission operations. The technologies associated with the projects include advanced software systems for operations and human-centered computing. Human-centered computing looks to the processes and procedures that people do to perform any given job, then attempts to identify opportunities to improve these processes and procedures. In particular, for mission operations, improvements are quantified by specifically identifying how a tool can increase a persons efficiency, enhance a persons functional capability, andor improve the assurance of a persons decisions. The Ames development team has collaborated with the Mission Operations team to identify areas of efficiencies through technology infusion applications in support of the Plan, Train, and Fly activities of human-spaceflight mission operations. The specific applications discussed in this paper are in the areas of mission planning systems, mission operations design modeling and workflow automation, advanced systems monitoring, mission control technologies, search tools, training management tools, spacecraft solar array management, spacecraft power management, and spacecraft attitude planning. We discuss these specific projects between the Ames Research Center and the Johnson Space Centers Mission Operations Directorate, and how these technologies and projects are enhancing the mission operations support for the International Space Station. We also discuss the challenges, problems, and successes associated with long-distance and multi-year development projects between the research team at Ames and the Mission Operations customers at Johnson Space center. Finally, we discuss how these technology infusion applications and underlying technologies might be used in the future to support on-board operations of the crew and spacecraft systems as human exploration expands beyond low earth orbit to destinations in the solar system where communications delays will require more on-board autonomy and planning by the crew. Longer communications delays will require that the ground mission operations support will be primarily strategic in nature, while the tactical level of planning, systems monitoring and control, and failure analysisisolationrecovery will be the responsibility of both the spacecraft autonomous systems and the crew. Our expectation is that the technologies

mission operations↗
NASA NTRS2004

Digital Radar-Signal Processors Implemented in FPGAs

High-performance digital electronic circuits for onboard processing of return signals in an airborne precipitation- measuring radar system have been implemented in commercially available field-programmable gate arrays (FPGAs). Previously, it was standard practice to downlink the radar-return data to a ground station for postprocessing a costly practice that prevents the nearly-real-time use of the data for automated targeting. In principle, the onboard processing could be performed by a system of about 20 personal- computer-type microprocessors; relative to such a system, the present FPGA-based processor is much smaller and consumes much less power. Alternatively, the onboard processing could be performed by an application-specific integrated circuit (ASIC), but in comparison with an ASIC implementation, the present FPGA implementation offers the advantages of (1) greater flexibility for research applications like the present one and (2) lower cost in the small production volumes typical of research applications. The generation and processing of signals in the airborne precipitation measuring radar system in question involves the following especially notable steps: The system utilizes a total of four channels two carrier frequencies and two polarizations at each frequency. The system uses pulse compression: that is, the transmitted pulse is spread out in time and the received echo of the pulse is processed with a matched filter to despread it. The return signal is band-limited and digitally demodulated to a complex baseband signal that, for each pulse, comprises a large number of samples. Each complex pair of samples (denoted a range gate in radar terminology) is associated with a numerical index that corresponds to a specific time offset from the beginning of the radar pulse, so that each such pair represents the energy reflected from a specific range. This energy and the average echo power are computed. The phase of each range bin is compared to the previous echo by complex conjugate multiplication to obtain the mean Doppler shift (and hence the mean and variance of the velocity of precipitation) of the echo at that range.

Berkun, Andrew↗
NASA NTRS2013

Combined Cycle Engine Large-Scale Inlet for Mode Transition Experiments: System Identification Rack Hardware Design

The System Identification (SysID) Rack is a real-time hardware-in-the-loop data acquisition (DAQ) and control instrument rack that was designed and built to support inlet testing in the NASA Glenn Research Center 10- by 10-Foot Supersonic Wind Tunnel. This instrument rack is used to support experiments on the Combined-Cycle Engine Large-Scale Inlet for Mode Transition Experiment (CCE‒ LIMX). The CCE‒LIMX is a testbed for an integrated dual flow-path inlet configuration with the two flow paths in an over-and-under arrangement such that the high-speed flow path is located below the lowspeed flow path. The CCE‒LIMX includes multiple actuators that are designed to redirect airflow from one flow path to the other; this action is referred to as "inlet mode transition." Multiple phases of experiments have been planned to support research that investigates inlet mode transition: inlet characterization (Phase-1) and system identification (Phase-2). The SysID Rack hardware design met the following requirements to support Phase-1 and Phase-2 experiments: safely and effectively move multiple actuators individually or synchronously; sample and save effector control and position sensor feedback signals; automate control of actuator positioning based on a mode transition schedule; sample and save pressure sensor signals; and perform DAQ and control processes operating at 2.5 KHz. This document describes the hardware components used to build the SysID Rack including their function, specifications, and system interface. Furthermore, provided in this document are a SysID Rack effectors signal list (signal flow); system identification experiment setup; illustrations indicating a typical SysID Rack experiment; and a SysID Rack performance overview for Phase-1 and Phase-2 experiments. The SysID Rack described in this document was a useful tool to meet the project objectives.

Thomas, Randy↗
NASA NTRS2014

ISAAC - A Testbed for Advanced Composites Research

The NASA Langley Research Center is acquiring a state-of-art composites fabrication environment to support the Center's research and technology development mission. This overall system described in this paper is named ISAAC, or Integrated Structural Assembly of Advanced Composites. ISAAC's initial operational capability is a commercial robotic automated fiber placement system from Electroimpact, Inc. that consists of a multi-degree of freedom commercial robot platform, a tool changer mechanism, and a specialized automated fiber placement end effector. Examples are presented of how development of advanced composite materials, structures, fabrication processes and technology are enabled by utilizing the fiber placement end effector directly or with appropriate modifications. Alternatively, end effectors with different capabilities may either be bought or developed with NASA's partners in industry and academia.

Wu, K. Chauncey↗
NASA NTRS2016

Light Microscopy Module: International Space Station Premier Automated Microscope

The Light Microscopy Module (LMM) was launched to the International Space Station (ISS) in 2009 and began hardware operations in 2010. It continues to support Physical and Biological scientific research on ISS. During 2016, if all goes as planned, three experiments will be completed: [1] Advanced Colloids Experiments with Heated base-2 (ACE-H2) and [2] Advanced Colloids Experiments with Temperature control (ACE-T1). Preliminary results, along with an overview of present and future LMM capabilities will be presented; this includes details on the planned data imaging processing and storage system, along with the confocal upgrade to the core microscope. [1] a consortium of universities from the State of Kentucky working through the Experimental Program to Stimulate Competitive Research (EPSCoR): Stuart Williams, Gerold Willing, Hemali Rathnayake, et al. and [2] from Chungnam National University, Daejeon, S. Korea: Chang-Soo Lee, et al.

Microgravity Biology Research↗
NASA NTRSDate not supplied

TPSAS-NF1676L-26669-DND

The NASA Langley Research Center has acquired a state-of-art composites fabrication environment to support the Center's research and technology development mission. This overall system described in this paper is named ISAAC, or Integrated Structural Assembly of Advanced Composites. ISAAC's initial operational capability is a commercial robotic automated fiber placement system from Electroimpact, Inc. that consists of a multi-degree of freedom commercial robot platform, a tool changer mechanism, and a specialized automated fiber placement end effector. Examples are presented of how development of advanced composite materials, structures, fabrication processes and technology are enabled by utilizing the fiber placement end effector directly or with appropriate modifications. Several advanced capability end effectors have been procured, and alternate processes with different capabilities can be developed with NASA's partners in industry and academia.

K C Wu↗
NASA NTRSDate not supplied

Studying Microbial Adaptation in the Laboratory: Sensor & Control Upgrades for an Experimental Evolution Biofluidics System

Experimental evolution (EE) involves iteratively exposing a microbial community to specific stressors to study its response to changes in environment over time. EE work is commonly done manually in the laboratory, but, when there are many environmental variables to measure and adjust, it is highly labor intensive, prone to human error, and challenging to scale. Single-purpose automated continuous culturing chambers exist, but implement only limited stressor types. A more general-purpose design is desirable. The BeING Lab at Ames Research Center created the prototype Automated Adaptive Directed Evolution Chamber (AADEC) to address these problems, beginning with Escherichia coli tolerance of short-wave ultraviolet (UV-C) radiation and of temperature. In newer versions, AADEC monitors microbial activity and can adjust the UV-C and temperature levels automatically. An optical density measurement is used to determine how many cells are present in the growth medium—over time, this corresponds to how many survive and reproduce. Oxidation-reduction potential provides information on consumed metabolic energy, and pH and electrical conductivity on metabolic products. Dissolved oxygen content is used to determine aerobic vs anaerobic growth. A Raspberry Pi computer processes all this data to set the UV-C stressor level. AADEC’s auxiliary systems include peristaltic pumps to change media and agitation to counteract cell settling. These actuators can also act as additional stressors. With the Raspberry Pi monitoring sensors and adjusting actuators in real time, AADEC takes measurements and controls the environment much more accurately than can be done with a manual EE implementation. The third and latest AADEC iteration is the first to simplify design and usage with circuits on PCBs and the ability to pre-program experimental protocols. Still planned is expansion to a multi-well design for the study of varying cell cultures in parallel, which will enable researchers to retain and re-inoculate cultures exhibiting the desired trait most strongly while flushing out others. AADEC’s special capabilities make it a valuable tool for studying life under multiple stressors, enabling scientists to replicate changes in climate on microbes for study in a lab setting.

Microbial Adaptation↗
NASA NTRS1984

Optimization of helicopter rotor blade design for minimum vibration

The optimization approach discussed is part of an ongoing effort to develop a general automated procedure for rotor blade design. This procedure can be used to determine the necessary geometric, structural, and material properties of a rotor system to achieve desired objectives relating to vibration, stress, and aerodynamic performance. The approach used for helicopter vibration is emphasized. Based on analytical studies performed at the United Technologies Research Center (UTRC), a simplified vibration analysis was developed to be used in conjunction with a forced response analysis in the optimization process. This simplified analysis improves the efficiency of the design process significantly. Results of applying this approach to the design of an existing rotor blade model are presented.

Davis, M. W.↗
NASA NTRS1992

Information models of software productivity - Limits on productivity growth

Research into generalized information-metric models of software process productivity establishes quantifiable behavior and theoretical bounds. The models establish a fundamental mathematical relationship between software productivity and the human capacity for information traffic, the software product yield (system size), information efficiency, and tool and process efficiencies. An upper bound is derived that quantifies average software productivity and the maximum rate at which it may grow. This bound reveals that ultimately, when tools, methodologies, and automated assistants have reached their maximum effective state, further improvement in productivity can only be achieved through increasing software reuse. The reuse advantage is shown not to increase faster than logarithmically in the number of reusable features available. The reuse bound is further shown to be somewhat dependent on the reuse policy: a general 'reuse everything' policy can lead to a somewhat slower productivity growth than a specialized reuse policy.

Tausworthe, Robert C.↗
NASA NTRS1996

Study and Analysis of The Robot-Operated Material Processing Systems (ROMPS)

This is a report presenting the progress of a research grant funded by NASA for work performed during 1 Oct. 1994 - 31 Sep. 1995. The report deals with the development and investigation of potential use of software for data processing for the Robot Operated Material Processing System (ROMPS). It reports on the progress of data processing of calibration samples processed by ROMPS in space and on earth. First data were retrieved using the I/O software and manually processed using MicroSoft Excel. Then the data retrieval and processing process was automated using a program written in C which is able to read the telemetry data and produce plots of time responses of sample temperatures and other desired variables. LabView was also employed to automatically retrieve and process the telemetry data.

Nguyen, Charles C.↗
NASA NTRS2014

Materials Science Research Rack Onboard the International Space Station

The Materials Science Research Rack (MSRR) is a highly automated facility developed in a joint venture/partnership between NASA and ESA center dot Allows for the study of a variety of materials including metals, ceramics, semiconductor crystals, and glasses onboard the International Space Station (ISS) center dot Multi-user facility for high temperature materials science research center dot Launched on STS-128 in August 2009, and is currently installed in the U.S. Destiny Laboratory Module Research goals center dot Provide means of studying materials processing in space to develop a better understanding of the chemical and physical mechanisms involved center dot Benefit materials science research via the microgravity environment of space where the researcher can better isolate the effects of gravity during solidification on the properties of materials center dot Use the knowledge gained from experiments to make reliable predictions about conditions required on Earth to achieve improved materials

Reagan, Shawn E.↗
NASA NTRS1989

Ground data systems resource allocation process

The Ground Data Systems Resource Allocation Process at the Jet Propulsion Laboratory provides medium- and long-range planning for the use of Deep Space Network and Mission Control and Computing Center resources in support of NASA's deep space missions and Earth-based science. Resources consist of radio antenna complexes and associated data processing and control computer networks. A semi-automated system was developed that allows operations personnel to interactively generate, edit, and revise allocation plans spanning periods of up to ten years (as opposed to only two or three weeks under the manual system) based on the relative merit of mission events. It also enhances scientific data return. A software system known as the Resource Allocation and Planning Helper (RALPH) merges the conventional methods of operations research, rule-based knowledge engineering, and advanced data base structures. RALPH employs a generic, highly modular architecture capable of solving a wide variety of scheduling and resource sequencing problems. The rule-based RALPH system has saved significant labor in resource allocation. Its successful use affirms the importance of establishing and applying event priorities based on scientific merit, and the benefit of continuity in planning provided by knowledge-based engineering. The RALPH system exhibits a strong potential for minimizing development cycles of resource and payload planning systems throughout NASA and the private sector.

Berner, Carol A.↗
NASA NTRS1998

Recent Productivity Improvements to the National Transonic Facility

Productivity gains have recently been made at the National Transonic Facility wind tunnel at NASA Langley Research Center. A team was assigned to assess and set productivity goals to achieve the desired operating cost and output of the facility. Simulations have been developed to show the sensitivity of selected process productivity improvements in critical areas to reduce overall test cycle times. The improvements consist of an expanded liquid nitrogen storage system, a new fan drive, a new tunnel vent stack heater, replacement of programmable logic controllers, an increased data communications speed, automated test sequencing, and a faster model changeout system. Where possible, quantifiable results of these improvements are presented. Results show that in most cases, improvements meet the productivity gains predicted by the simulations.

Popernack, Thomas G., Jr.↗
NASA NTRS2018

Impact of Advanced Synoptics and Simplified Checklists During Aircraft Systems Failures

Abstract—Natural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds demanded in today’s aviation environment. Increasingly Autonomous Systems (IAS) are uniquely suited to solve this problem. NASA is conducting research and development of IAS - hardware and software systems, utilizing machine learning algorithms, seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. IAS offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. A human-in-the-loop test was conducted in NASA Langley’s Integration Flight Deck B-737-800 simulator to evaluate advanced synoptic pages with simplified interactive electronic checklists as an IAS for routine air carrier flight operations and in response to aircraft system failures. Twelve U.S. airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to failures. These data are fundamental to and critical for the design and development of future increasingly autonomous systems that can better support the human in the cockpit. Synoptic pages and electronic checklists significantly improved pilot responses to non-normal scenarios, but implementation of these aids and other intelligent assistants have barriers to implementation (e.g., certification cost) that must overcome.

Etherington, Timothy J.↗
NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗