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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 433 records · Page 24
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NASA NTRS1990

Trajectory optimization for an asymmetric launch vehicle

A numerical optimization technique is used to fully automate the trajectory design process for an symmetric configuration of the proposed Advanced Launch System (ALS). The objective of the ALS trajectory design process is the maximization of the vehicle mass when it reaches the desired orbit. The trajectories used were based on a simple shape that could be described by a small set of parameters. The use of a simple trajectory model can significantly reduce the computation time required for trajectory optimization. A predictive simulation was developed to determine the on-orbit mass given an initial vehicle state, wind information, and a set of trajectory parameters. This simulation utilizes an idealized control system to speed computation by increasing the integration time step. The conjugate gradient method is used for the numerical optimization of on-orbit mass. The method requires only the evaluation of the on-orbit mass function using the predictive simulation, and the gradient of the on-orbit mass function with respect to the trajectory parameters. The gradient is approximated with finite differencing. Prelaunch trajectory designs were carried out using the optimization procedure. The predictive simulation is used in flight to redesign the trajectory to account for trajectory deviations produced by off-nominal conditions, e.g., stronger than expected head winds.

Sullivan, Jeanne Marie↗
NASA NTRS2005

STS-114: Discovery Launch Readiness Press Conference

Michael Griffin, NASA Administrator; Wayne Hale, Space Shuttle Deputy Program Manager; Mike Wetmore, Director of Shuttle Processing; and 1st Lieutenant Mindy Chavez, Launch Weather Officer-United States Air Force 45th Weather Squadron are in attendance for this STS-114 Discovery launch readiness press conference. The discussion begins with Wayne Hale bringing to the table a low level sensor device for everyone to view. He talks in detail about all of the extensive tests that were performed on these sensors and the completion of these ambient tests. Chavez presents her weather forecast for the launch day of July 26th 2005. Michael Griffin and Wayne Hale answer questions from the news media pertaining to the sensors and launch readiness. The video ends with footage of Pilot Jim Kelly and Commander Eileen Collins conducting test flights in a Shuttle Training Aircraft (STA) that simulates Space Shuttle landing.

Source record↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This presentation describes the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRS1991

The Ames-Lockheed orbiter processing scheduling system

A general purpose scheduling system and its application to Space Shuttle Orbiter Processing at the Kennedy Space Center (KSC) are described. Orbiter processing entails all the inspection, testing, repair, and maintenance necessary to prepare the Shuttle for launch and takes place within the Orbiter Processing Facility (OPF) at KSC, the Vehicle Assembly Building (VAB), and on the launch pad. The problems are extremely combinatoric in that there are thousands of tasks, resources, and other temporal considerations that must be coordinated. Researchers are building a scheduling tool that they hope will be an integral part of automating the planning and scheduling process at KSC. The scheduling engine is domain independent and is also being applied to Space Shuttle cargo processing problems as well as wind tunnel scheduling problems.

Zweben, Monte↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRSDate not supplied

An Overview of Spacecraft Aerosols: Sources, Dynamics, and Knowledge Gaps

NASA has prioritized development of airborne particulate monitoring systems and mitigation strategies for future Lunar, Martian, and deep-space exploration missions. Airborne particles, also called aerosols, can cause human health problems ranging from mild respiratory discomfort to more severe disease. NASA’s suspended cabin particle and Lunar dust concentration requirements are motivated by protecting crew health and comfort during spaceflight, especially as mission durations lengthen and become increasingly ground-independent. Spacecraft cabin aerosols can originate from a variety of sources, including skin flakes, fibers from clothes and other materials, mechanical wear from equipment, and personal care products (e.g., antiperspirant). As NASA plans future expeditions to the Moon and Mars, Lunar and planetary dust compose an additional, under-characterized source for airborne particles. In addition to these known sources, recent studies on ISS cabin aerosols also revealed particles with unknown origins and poorly understood formation mechanisms, including some that may be formed by oxidative and/or heterogeneous chemical processes. The aerosol monitors payload, to be launched February 2025, will focus on understanding these unknown sources and processes, as well as demonstrating novel technologies for particulate monitoring to be infused in future space flight programs. In this presentation, we will present our knowledge of spacecraft cabin aerosols to date, summarizing aerosol measurement experiments from Shuttle through ISS. Cabin aerosol sources, known and unknown, will be summarized, and knowledge gaps for future long-duration space exploration missions will be discussed.

Claire Fortenberry↗
NASA NTRS2006

Generic Environment for Simulating Launch Operations

GEM-FLO (A Generic Simulation Environment for Modeling Future Launch Operations) is a computer program that facilitates creation of discrete-event simulation models of ground processes in which reusable or expendable launch vehicles (RLVs) are prepared for flight. GEM-FLO includes a component, developed in Visual Basic, that generates a graphical user interface (GUI) and a component, developed in the Arena simulation language, that creates a generic discrete-event simulation model. Through the GUI, GEM-FLO elicits RLV design information from the user. The design information can include information on flight hardware elements, resources, and ground processes. GEM-FLO translates the user s responses into mathematical variables and expressions that populate the generic simulation model. The variables and expressions can represent processing times, resource capacities, status variables, and other process parameters needed to configure a simulation model that reflects the ground processing flow and requirements of a specific RLV. Upon execution of the model, GEMFLO puts out data on many measures of performance, including the flight rate, turnaround time, and utilization of resources. This information can serve as the basis for determining whether design goals can be met, and for comparing characteristics of competing RLV designs

Steele, Martin↗
NASA NTRS2016

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on GOES-R Satellite

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 s) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

validation↗
NASA NTRS2016

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on the GOES-R Satellite

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 microseconds) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

validation↗
NASA NTRS2013

Integrated Main Propulsion System Performance Reconstruction Process/Models

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

Lopez, Eduardo↗
NASA NTRS2005

Vehicle Integrated Performance Analysis, the VIPA Experience: Reconnecting with Technical Integration

Today's NASA is facing significant challenges and changes. The Exploration initiative indicates a large increase in projects with limited increase in budget. The Columbia report has criticized NASA for its lack of insight and technical integration impacting its ability to provide safety. The Aldridge report is advocating NASA find new ways of doing business. Very early in the Space Launch Initiative (SLI) program a small team of engineers at MSFC were asked to propose a process for performing a system level assessment of a launch vehicle. The request was aimed primarily at providing insight and making NASA a "smart buyer." Out of this effort the VIPA team was created. The difference between the VIPA effort and many integration attempts is that VIPA focuses on using experienced people from various disciplines and a process which focuses them on a technically integrated assessment. Most previous attempts have focused on developing an all encompassing software tool. In addition, VIPA anchored its process formulation in the experience of its members and in early developmental Space Shuttle experience. The primary reference for this is NASA-TP-2001-210092, "Launch Vehicle Design Process: Characterization, Technical Integration, and Lessons Learned," and discussions with its authors. The foundations of VIPA's process are described. The VIPA team also recognized the need to drive detailed analysis earlier in the design process. Analyses and techniques typically done in later design phases, are brought forward using improved computing technology. The intent is to allow the identification of significant sensitivities, trades, and design issues much earlier in the program. This process is driven by the T-model for Technical Integration described in the aforementioned reference. VIPA's approach to performing system level technical integration is discussed in detail. Proposed definitions are offered to clarify this discussion and the general systems integration dialog. VIPA's capabilities and process can now be used to significantly enhance the development and monitoring of realizable project requirements. This is done through the use of VIPA's V-model. Starting with a given concept, VIPA's assessment validates the concept's stated performance, identifies significant issues either with the concept or the requirements, and then re-integrates these issues to determine impacts. This process is discussed along with a description of how it may be integrated into a program's insight and review process. The VIPA process has been employed successfully on several projects including SLI, Orbital Space Plane (OSP), and several heavy lift concepts for Exploration. It has also been proposed for use on the Jupiter Icy Moon (JIMO) spacecraft. The VIPA process has gained favor with both engineering and project organizations for being responsive and insightful. Selected results from these assessments will be presented.

McGhee, David S.↗
NASA NTRS1975

Spacelab user implementation assessment study. Volume 1: Concept development and evaluation

The total matrix of alternate Spacelab processing concepts and the rejection rationale utilized to reduce the matrix of 243 alternates to the final candidate processing concepts are developed. The work breakdown structure used for the systematic estimation and compilation of integration and checkout resources is presented along with descriptors of each element. Program models are provided of the space transportation system, the Spacelab, the orbiter, and the ATL that were used as the basis for the study trades, analyses, and optimizations. Resource requirements for all processing concepts are summarized along with the optimizations of the processing concepts. Concept evaluations including flight-rate sensitivities of the GSE, facilities, Spacelab hardware elements, and personnel are delineated. An analysis is presented of the applicability of the candidate concepts to potential spacelab users. The impact of the use of the western test range as an orbiter/spacelab launch site on the candidate processing concepts is evaluated. An assessment of the geographical co-location of experiment, Spacelab, and orbiter-cargo integration is included. Ownership options of the support module/system igloo are discussed.

Source record↗
NASA NTRS2005

Characterization of Space Shuttle Ascent Debris Aerodynamics Using CFD Methods

An automated Computational Fluid Dynamics process for determining the aerodynamic Characteristics of debris shedding from the Space Shuttle Launch Vehicle during ascent is presented. This process uses Cartesian fully-coupled, six-degree-of-freedom simulations of isolated debris pieces in a Monte Carlo fashion to produce models for the drag and crossrange behavior over a range of debris shapes and shedding scenarios. A validation of the Cartesian methods against ballistic range data for insulating foam debris shapes at flight conditions, as well as validation of the resulting models, are both contained. These models are integrated with the existing shuttle debris transport analysis software to provide an accurate and efficient engineering tool for analyzing debris sources and their potential for damage.

Murman, Scott M.↗
NASA NTRS2014

A Failure Propagation Modeling Method for Launch Vehicle Safety Assessment

A method has been developed with the objective of making the potentially intractable problem of launch vehicle failure propagation somewhat less intractable. The approach taken is to essentially decouple the potentially multi-stepped propagation process into a series of bi-component transition probabilities. These probabilities are then used within a simple Monte Carlo simulation process through which the more complex behavior evolves. The process is described using a simple model problem and some discussion of enhancements for real-world applications is included. The role of the model within a broader analysis process for assessing abort effectiveness from launch vehicle failure modes is also described.

Lawrence, Scott↗
NASA NTRS1978

NASA satellite to study earth's oceans from space

The feasibility of using microwave instruments to scan the world's oceans from space in order to obtain scientific data for oceanographers, meteorologists, and commercial users of the seas will be demonstrated during the mission of the Seasat A satellite which will be launched into an 800 kilometer high near circular orbit by an Agena Atlas-Agena launch vehicle. The satellite configuration, its payload, and data collection and processing capabilities are described as well as the launch vehicle system.

Source record↗
NASA NTRS1990

Integration by parts

This paper describes the unique integration and verification challenges associated with the Space Station Freedom and an approach to solve these problems using Data Management Systems (DMS) Kits. These DMS Kits will help alleviate the complex integration problems inherent in building, assembling and testing the Space Station. Particular emphasis has been placed on utilizing the capabilities and services of the on-board DMS to provide the integration and verification tools, not only for the DMS but for the other on-board distributed systems as well. DMS Kits are provided to system/software developers across the program. These DMS Kits provide a common set of integration and verification tools and hardware. Each system developer can then utilize, through the kits, a simulation of the complete data processing environment which will be available on orbit. The paper describes the evolution of the integration process from the system level to the final integration of multiple launch packages. DMS Kits are used throughout this process, which addresses both the ground and on-orbit aspects of the problem.

Barry, Thomas↗