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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Quattrochi, Dale A.↗
NASA NTRS2006

Space and Ground Trades for Human Exploration and Wearable Computing

Human exploration of the Moon and Mars will present unique trade study challenges as ground system elements shift to planetary bodies and perhaps eventually to the bodies of human explorers in the form of wearable computing technologies. This presentation will highlight some of the key space and ground trade issues that will face the Exploration Initiative as NASA begins designing systems for the sustained human exploration of the Moon and Mars, with an emphasis on wearable computing. We will present some preliminary test results and scenarios that demonstrate how wearable computing might affect the trade space noted below. We will first present some background on wearable computing and its utility to NASA's Exploration Initiative. Next, we will discuss three broad architectural themes, some key ground and space trade issues within those themes and how they relate to wearable computing. Lastly, we will present some preliminary test results and suggest guidance for proceeding in the assessment and creation of a value-added role for wearable computing in the Exploration Initiative. The three broad ground-space architectural trade themes we will discuss are: 1. Functional Shift and Distribution: To what extent, if any, should traditional ground system functionality be shifted to, and distributed among, the Earth, Moon/Mars, and the human. explorer? 2. Situational Awareness and Autonomy: How much situational awareness (e.g. environmental conditions, biometrics, etc.) and autonomy is required and desired, and where should these capabilities reside? 3. Functional Redundancy: What functions (e.g. command, control, analysis) should exist simultaneously on Earth, the Moon/Mars, and the human explorer? These three themes can serve as the axes of a three-dimensional trade space, within which architectural solutions reside. We will show how wearable computers can fit into this trade space and what the possible implications could be for the rest of the ground and space architecture(s). We intend this to be an example of explorer-centric thinking in a fully integrated explorer paradigm, where integrated explorer refers to a human explorer having instant access to all relevant data, knowledge of the environment, science models, health and safety-related events, and other tools and information via wearable computing technologies. The trade study approach will include involvement from the relevant stakeholders (Constellation Systems, CCCI, EVA Project Office, Astronaut office, Mission Operations, Space Life Sciences, etc.) to develop operations concepts (and/or operations scenarios) from which a basic high-level set of requirements could be extracted. This set of requirements could serve as a foundation (along with stakeholder buy-in) that would help define the trade space and assist in identifying candidate technologies for further study and evolution to higher-level technology readiness levels.

Lupisella, Mark↗
NASA NTRS2023

Additive Manufacturing of Oxide Dispersion Strengthened Multi Principle Element Alloys for Future Aerospace Applications

Oxide Dispersion Strengthened (ODS) materials have long been of interest for their high temperature applications, and additive manufacturing enables their manufacturing viability. The ODS multi-principle element alloy NiCoCr was prepared using powder metallurgy techniques, additively manufactured, and evaluated for its processingmicrostructure-property relationships. The high temperature foundations of nickel-base superalloys and ODS materials were combined with the manufacturing advantages of 3D printing and the chemical simplicity of NiCoCr to inspire this work, which was divided into powder and printed material assessments. The project was achieved through multiple iterative project loops to assess the processing parameters’ impact on the microstructure and mechanical properties of the feedstock powder and printed material. The powder investigations (Chapter 3) focused on understanding the oxide coating that formed on the metal powder following acoustic mixing. Time of Flight Secondary Ion Mass Spectrometry was used to semi-quantitatively assess the amount of yttrium on the surface of the mixed powders, and indicated that a combination of higher mixing condition energy and moderate mixing time resulted in the most oxide coating on the NiCoCr powder. The results were supported by a qualitative assessment of scanning electron images of coated powder particles. Following mixing, the ODS NiCoCr was consolidated by Laser Powder Bed Fusion. The evaluations of the printed material (Chapter 4) frst considered screening experiments including Archimedes’ density, porosity, and grain size and number metrics from electron backscatter diffraction data. After the ideal additive manufacturing parameters were identifed, both the oxide homogeneity and yield strength were discussed for the idealized printed material. Overall, the project suggests that the combined use of qualitative or semi-quantitative powder surface analysis with Archimedes’ density analyses can be a valid high-throughput technique which can lead to process optimization of Laser Powder Bed Fusion additively manufactured ODS material.

Laura G Wilson↗
NASA NTRS2016

Investigating the Simulink Auto-Coding Process

Model based program design is the most clear and direct way to develop algorithms and programs for interfacing with hardware. While coding "by hand" results in a more tailored product, the ever-growing size and complexity of modern-day applications can cause the project work load to quickly become unreasonable for one programmer. This has generally been addressed by splitting the product into separate modules to allow multiple developers to work in parallel on the same project, however this introduces new potentials for errors in the process. The fluidity, reliability and robustness of the code relies on the abilities of the programmers to communicate their methods to one another; furthermore, multiple programmers invites multiple potentially differing coding styles into the same product, which can cause a loss of readability or even module incompatibility. Fortunately, Mathworks has implemented an auto-coding feature that allows programmers to design their algorithms through the use of models and diagrams in the graphical programming environment Simulink, allowing the designer to visually determine what the hardware is to do. From here, the auto-coding feature handles converting the project into another programming language. This type of approach allows the designer to clearly see how the software will be directing the hardware without the need to try and interpret large amounts of code. In addition, it speeds up the programming process, minimizing the amount of man-hours spent on a single project, thus reducing the chance of human error as well as project turnover time. One such project that has benefited from the auto-coding procedure is Ramses, a portion of the GNC flight software on-board Orion that has been implemented primarily in Simulink. Currently, however, auto-coding Ramses into C++ requires 5 hours of code generation time. This causes issues if the tool ever needs to be debugged, as this code generation will need to occur with each edit to any part of the program; additionally, this is lost time that could be spent testing and analyzing the code. This is one of the more prominent issues with the auto-coding process, and while much information is available with regard to optimizing Simulink designs to produce efficient and reliable C++ code, not much research has been made public on how to reduce the code generation time. It is of interest to develop some insight as to what causes code generation times to be so significant, and determine if there are architecture guidelines or a desirable auto-coding configuration set to assist in streamlining this step of the design process for particular applications. To address the issue at hand, the Simulink coder was studied at a foundational level. For each different component type made available by the software, the features, auto-code generation time, and the format of the generated code were analyzed and documented. Tools were developed and documented to expedite these studies, particularly in the area of automating sequential builds to ensure accurate data was obtained. Next, the Ramses model was examined in an attempt to determine the composition and the types of technologies used in the model. This enabled the development of a model that uses similar technologies, but takes a fraction of the time to auto-code to reduce the turnaround time for experimentation. Lastly, the model was used to run a wide array of experiments and collect data to obtain knowledge about where to search for bottlenecks in the Ramses model. The resulting contributions of the overall effort consist of an experimental model for further investigation into the subject, as well as several automation tools to assist in analyzing the model, and a reference document offering insight to the auto-coding process, including documentation of the tools used in the model analysis, data illustrating some potential problem areas in the auto-coding process, and recommendations on areas or practices in the current Ramses model that should be further investigated. Several skills were required to be built up over the course of the internship project. First and foremost, my Simulink skills have improved drastically, as much of my experience had been modeling electronic circuits as opposed to software models. Furthermore, I am now comfortable working with the Simulink Auto-coder, a tool I had never used until this summer; this tool also tested my critical thinking and C++ knowledge as I had to interpret the C++ code it was generating and attempt to understand how the Simulink model affected the generated code. I had come into the internship with a solid understanding of Matlab code, but had done very little in using it to automate tasks, particularly Simulink tasks; along the same lines, I had rarely used shell script to automate and interface with programs, which I gained a fair amount of experience with this summer, including how to use regular expression. Lastly, soft-skills are an area everyone can continuously improve on; having never worked with NASA engineers, which to me seem to be a completely different breed than what I am used to (commercial electronic engineers), I learned to utilize the wealth of knowledge present at JSC. I wish I had come into the internship knowing exactly how helpful everyone in my branch would be, as I would have picked up on this sooner. I hope that having gained such a strong foundation in Simulink over this summer will open the opportunity to return to work on this project, or potentially other opportunities within the division. The idea of leaving a project I devoted ten weeks to is a hard one to cope with, so having the chance to pick up where I left off sounds appealing; alternatively, I am interested to see if there are any opening in the future that would allow me to work on a project that is more in-line with my research in estimation algorithms. Regardless, this summer has been a milestone in my professional career, and I hope this has started a long-term relationship between JSC and myself. I really enjoy the thought of building on my experience here over future summers while I work to complete my PhD at Missouri University of Science and Technology.

Gualdoni, Matthew J.↗
NASA NTRSDate not supplied

Creating Portfolio Management Concepts for Highly Agile and Innovative Government Research Programs Using Design Thinking and Lean Startup Methodologies

Managing a research and development (R&D) portfolio presents numerous challenges, such as prioritizing research areas, remaining agile, strategic workforce planning, and measuring return on investment, impact, and innovation. One government R&D program is charged with producing a high degree of innovative, transformational breakthroughs in aviation technology. A practical, structured methodology for strategically prioritizing emerging aviation R&D in such an environment is lacking. The existing multi-criteria decision aid tools are primarily utilized at an enterprise level, take months to set up and collect data, require large teams of experts, are used on an infrequent basis, and are not conducive to a highly innovative, high-risk portfolio. Prior to audaciously creating a new portfolio prioritization process, the exact challenges with portfolio management for R&D projects were identified using the design thinking and lean start-up methods. A key part of this discovery process was interviewing stakeholders, as well as other managers of organizations charged with producing innovative portfolios. These interviews, as well as additional techniques, were used to develop a deeper understanding of the challenges and, subsequently, lay the foundation for development of an effective portfolio prioritization process. Four potential concepts that represent key findings emerged: carefully selected criteria for portfolio assessment and selection, targeted portfolio turnover rate, dynamic portfolio prioritization framework, and streamlined transition or commercialization of R&D. Acting on any one of the resulting portfolio management concepts will increase the transparency and confidence in portfolio decisions and, ideally, result in a greater degree of transformational breakthroughs in aviation technology.

project portfolio management↗
DOE OSTI2024

Offshore Wind Energy Fundamentals for Bangladesh [Slides]

Bangladesh is aiming to limit emissions, and gain access to cleaner, more efficient technologies to support economic growth as the nation strives to achieve high-income status by 2041. Bangladesh identified that it has a potential for offshore wind energy, especially at hub heights of 140 to 160 meters, which is pivotal in diversifying the country's energy mix and ensuring energy security. This one-day webinar is funded by the United States Agency for International Development (USAID) through two projects: (1) Reinforcing Advanced Energy Systems (RAES) project, implemented by the U.S. Department of Energy's National Renewable Energy Laboratory (NREL), and (2) Bangladesh Advancing Development and Growth through Energy (BADGE) project, implemented by Tetra Tech.

17 WIND ENERGY↗
DOE OSTI2025

Enabling The Next Generation of Smart Sensors in Coal Fired Power Plants using Cellular 5G Technology

Ohio University (OHIO), West Virginia University (WVU), and American Electric Power (AEP) proposed to study and report on the benefits of 5G wireless cellular technologies for coal-fired power plants. The significant advantages, cost savings, and potential of 5G wireless data communications based sensors promised to usher in a new era of reliable, inexpensive, and powerful embedded systems that had not previously been available for coal-fired power plants. The team built upon existing experience with cellular-based systems, power plant water quality sensing, and high temperature sensors developed during past projects. Principal Investigator Wilhelm had been developing cellular-based sensor data systems with a commercial partner for 10 years, pioneering innovative solar-powered devices that began with 2G technology. The lessons and knowledge gained served as a foundation to demonstrate innovations and potential impacts specific to coal fired power plants enabled by 5G technology, along with integration with existing sensors and systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗
NASA NTRS1981

Sources of support for remote sensing education

Past financial support for educational programs in remote sensing came largely in the form of short courses funded by the National Science Foundation. Later NASA began to fund such courses for local and state government and for some university participants in its regional programs. The greater impact came from the funding by a variety of federal agencies for remote sensing research projects at educational institutions throughout the country. Probably the best and most significant example of these programs, from the university standpoint is, and should continue to be, the NASA university affairs programs, which with its long term step funding of a number of institutions has probably done more for remote sensing education than any other federal program in this country. An incomplete listing of federal agencies that support remote sensing research at the university level is presented.

Estes, J. E.↗
NASA NTRS2013

Growing Spaceships?

NASA currently has a program called the Space Synthetic Biology Project. Synthetic Biology or SynBio is the design and construction of new biological functions and systems not found in nature. Four NASA field centers, along with experts from industry and academia, have been partnering on the Space Synthetic Biology Project and are working on new breakthroughs in this increasingly useful pursuit, which is part a science discipline and part engineering. Led by researchers at NASA s Ames Research Center, the team is studying how this powerful new tool can help NASA now and in the future. The project was created to harness biology in reliable, robust, engineered systems to support the agency s exploration and science missions, to improve life on Earth and to help shape NASA's future. The program also is intended to contribute foundational tools to the synthetic biology research community.

Robertson, Glen A.↗
NASA NTRS2017

Survey of Constellation-Era LOX/Methane Development Activities and Future Development Needs

NASA formed the Constellation Program in 2005 to achieve the objectives of maintaining American presence in low-Earth orbit, returning to the moon for purposes of establishing an outpost, and laying the foundation to explore Mars and beyond in the first half of the 21st century. The Exploration Technology Development Program (ETDP) was formulated to address the technology needs to address Constellation architecture decisions. The Propellants and Cryogenic Advanced Development (PCAD) project was tasked with risk mitigation of specific propulsion related technologies to support ETDP. Propulsion systems were identified as critical technologies owing to the high gear-ratio of lunar Mars landers Cryogenic propellants offer performance advantage over storables (NTOMMH) Mass savings translate to greater payload capacity In-situ production of propellant an attractive feature; methane and oxygen identified as possible Martian in-situ propellants New technologies were required to meet more difficult missions High performance LOX/LH2 deep throttle descent engines High performance LOX/LCH4 ascent main and reaction control system (RCS) engines The PCAD project sought to provide those technologies through Reliable ignition pulse RCS Fast start High efficiency engines Stable deep throttling.

Propulsion system components↗
NASA NTRS2026

NASA Agile Community of Practice 2024-2026 Report

This 2024-2026 report provides a summary of the products and activities executed by the NASA Agile Community of Practice (CoP) during its second and third years. Building on the foundation established in its inaugural year, the CoP continued to advance Agile values and principles across NASA centers. The report highlights key initiatives, including specialized framework training, AI integration in Agile toolkits, and active participation in agency-wide project management and systems engineering workshops.

Agile↗
NASA NTRS2015

The NASA Advanced Exploration Systems Nuclear Thermal Propulsion Project

The fundamental capability of Nuclear Thermal Propulsion (NTP) is game changing for space exploration. A first generation NTP system could provide high thrust at a specific impulse (Isp) above 900 s, roughly double that of state of the art chemical engines. Characteristics of fission and NTP indicate that useful first generation systems will provide a foundation for future systems with extremely high performance. The role of a first generation NTP in the development of advanced nuclear propulsion systems could be analogous to the role of the DC-3 in the development of advanced aviation systems.

Houts, Michael G.↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

The Federal Conference on Intelligent Processing Equipment

Research and development projects involving intelligent processing equipment within the following U.S. agencies are addressed: Department of Agriculture, Department of Commerce, Department of Energy, Department of Defense, Environmental Protection Agency, Federal Emergency Management Agency, NASA, National Institutes of Health, and the National Science Foundation.

Source record↗
NASA NTRS2007

Analytical Approach for Estimating Preliminary Mass of ARES I Crew Launch Vehicle Upper Stage Structural Components

In January 2004, President Bush gave the National Aeronautics and Space Administration (NASA) a vision for Space Exploration by setting our sight on a bold new path to go back to the Moon, then to Mars and beyond. In response to this vision, NASA started the Constellation Program, which is a new exploration launch vehicle program. The primary mission for the Constellation Program is to carry out a series of human expeditions ranging from Low Earth Orbit to the surface of Mars and beyond for the purposes of conducting human exploration of space, as specified by the Vision for Space Exploration (VSE). The intent is that the information and technology developed by this program will provide the foundation for broader exploration activities as our operational experience grows. The ARES I Crew Launch Vehicle (CLV) has been designated as the launch vehicle that will be developed as a "first step" to facilitate the aforementioned human expeditions. The CLV Project is broken into four major elements: First Stage, Upper Stage Engine, Upper Stage (US), and the Crew Exploration Vehicle (CEV). NASA's Marshall Space Flight Center (MSFC) is responsible for the design of the CLV and has the prime responsibility to design the upper stage of the vehicle. The US is the second propulsive stage of the CLV and provides CEV insertion into low Earth orbit (LEO) after separation from the First Stage of the Crew Launch Vehicle. The fully integrated Upper Stage is a mix of modified existing heritage hardware (J-2X Engine) and new development (primary structure, subsystems, and avionics). The Upper Stage assembly is a structurally stabilized cylindrical structure, which is powered by a single J-2X engine which is developed as a separate Element of the CLV. The primary structure includes the load bearing liquid hydrogen (LH2) and liquid oxygen (LOX) propellant tanks, a Forward Skirt, the Intertank structure, the Aft Skirt and the Thrust Structure. A Systems Tunnel, which carries fluid and electrical power functions to other Elements of the CLV, is included as secondary structure. The MSFC has an overall responsibility for the integrated US element as well as structural design an thermal control of the fuel tanks, intertank, interstage, avionics, main propulsion system, Reaction Control System (RCS) for both the Upper Stage and the First Stage. MSFC's Spacecraft and Vehicle Department, Structural and Analysis Design Division is developing a set of predicted mass of these elements. This paper details the methodology, criterion and tools used for the preliminary mass predictions of the upper stage structural assembly components. In general, weight of the cylindrical barrel sections are estimated using the commercial code Hypersizer, whereas, weight of the domes are developed using classical solutions. HyperSizer is software that performs automated structural analysis and sizing optimization based on aerospace methods for strength, stability, and stiffness. Analysis methods range from closed form, traditional hand calculations repeated every day in industry to more advanced panel buckling algorithms. Margin-of-safety reporting for every potential failure provides the engineer with a powerful insight into the structural problem. Optimization capabilities include finding minimum weight panel or beam concepts, material selections, cross sectional dimensions, thicknesses, and lay-ups from a library of 40 different stiffened and sandwich designs and a database of composite, metallic, honeycomb, and foam materials. Multiple different concepts (orthogrid, isogrid, and skin stiffener) were run for multiple loading combinations of ascent design load with and with out tank pressure as well as proof pressure condition. Subsequently, selected optimized concept obtained from Hypersizer runs was translated into a computer aid design (CAD) model to account for the wall thickness tolerance, weld land etc for developing the most probable weight of the components. The flow diram summarizes the analysis steps used in developing these predicted mass.

Aggarwal, Pravin↗