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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 NTRS1990

Space Station Freedom propulsion activities

The technical highlights and accomplishments made at NASA LeRC in the development of the Space Station Freedom (SSF) propulsion system are discussed. The objectives are as follows: develop and characterize resistojet-thruster components and assemblies; develop and characterize hydrogen-oxygen thruster components; and conduct system trade studies. The research projects primarily characterize propulsion performance and life. Other tests include environmental impacts, such as exhaust gas profiles and electromagnetic interference. The technical activities that are highlighted are being conducted at LeRC within the Aerospace Technology and Space Station Freedom directorates. These activities include the following: derivation of design analysis models; trade studies of design options; propulsion system impact studies; and component testing for characterization and design verification.

Spera, David A.↗

NASA NTRS1984

Commerce Lab: Mission analysis. Payload integration study

The objectives of the commerce lab mission analysis and payload integration study are discussed. A mission model which accommodates commercial users and provides a basic data base for future mission planning is described. The data bases developed under this study include: (1) user requirements; (2) apparatus capabilities and availabilities; and (3) carrier capabilities. These data bases are synthesized in a trades and analysis phase along with the STS flight opportunities. Optimum missions are identified.

Marvin, G. D.↗

NASA NTRS2022

Economic incentives modify agricultural impacts of nuclear war

A nuclear war using less than 1% of the current global nuclear arsenal, which would inject 5 Tg of soot into the stratosphere, could produce climate change unprecedented in recorded human history and significant impacts on agricultural productivity and the economy. These effects would be most severe for the first five years after the nuclear war and may last for more than a decade. This paper calculates how food availability would change by employing the Environmental Impact and Sustainability Applied General Equilibrium model. Under a robust world trading system, global food availability would drop by a few percentage points. If the war would destabilize trade, it would magnify by several times the negative ramifications of land productivity shocks on food availability. If exporting countries redirect production to domestic consumption at the expense of importing countries, it would lead to the destabilization of international trade. The analysis suggests that economic models aiming to inform policymakers require both economic behavior analysis and biophysical drivers. Policy lessons derived from a crop model can be significantly nuanced when coupled with economic feedback derived from economic models. Through the impact on yield, farmers could shift production among crops and reallocate land use to maximize profits, showing the importance of general equilibrium effects such as product and input substitution and international trade. Although the global impact on corn and soybean production would be significant when just considering crop production, it could be considerably smaller under the economic model. However, this would be at the expense of other sectors, including livestock. In addition, the costs borne from disruptions to climate would vary significantly across regions, with significant adverse effects in high latitude regions. The severity of the shocks in the high-latitude areas would marginalize the farmers' product and input substitution ability.

Nuclear war↗

NASA NTRS2011

The MSFC Collaborative Engineering Process for Preliminary Design and Concept Definition Studies

This paper describes a collaborative engineering process developed by the Marshall Space Flight Center's Advanced Concepts Office for performing rapid preliminary design and mission concept definition studies for potential future NASA missions. The process has been developed and demonstrated for a broad range of mission studies including human space exploration missions, space transportation system studies and in-space science missions. The paper will describe the design team structure and specialized analytical tools that have been developed to enable a unique rapid design process. The collaborative engineering process consists of integrated analysis approach for mission definition, vehicle definition and system engineering. The relevance of the collaborative process elements to the standard NASA NPR 7120.1 system engineering process will be demonstrated. The study definition process flow for each study discipline will be will be outlined beginning with the study planning process, followed by definition of ground rules and assumptions, definition of study trades, mission analysis and subsystem analyses leading to a standardized set of mission concept study products. The flexibility of the collaborative engineering design process to accommodate a wide range of study objectives from technology definition and requirements definition to preliminary design studies will be addressed. The paper will also describe the applicability of the collaborative engineering process to include an integrated systems analysis approach for evaluating the functional requirements of evolving system technologies and capabilities needed to meet the needs of future NASA programs.

Mulqueen, Jack↗

NASA NTRS1993

Life cycle cost modeling of conceptual space vehicles

This paper documents progress to date by the University of Dayton on the development of a life cycle cost model for use during the conceptual design of new launch vehicles and spacecraft. This research is being conducted under NASA Research Grant NAG-1-1327. This research effort changes the focus from that of the first two years in which a reliability and maintainability model was developed to the initial development of a life cycle cost model. Cost categories are initially patterned after NASA's three axis work breakdown structure consisting of a configuration axis (vehicle), a function axis, and a cost axis. The focus will be on operations and maintenance costs and other recurring costs. Secondary tasks performed concurrent with the development of the life cycle costing model include continual support and upgrade of the R&M model. The primary result of the completed research will be a methodology and a computer implementation of the methodology to provide for timely cost analysis in support of the conceptual design activities. The major objectives of this research are: to obtain and to develop improved methods for estimating manpower, spares, software and hardware costs, facilities costs, and other cost categories as identified by NASA personnel; to construct a life cycle cost model of a space transportation system for budget exercises and performance-cost trade-off analysis during the conceptual and development stages; to continue to support modifications and enhancements to the R&M model; and to continue to assist in the development of a simulation model to provide an integrated view of the operations and support of the proposed system.

Ebeling, Charles↗

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRSDate not supplied

Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables

The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.

A. Anderson↗

NASA NTRS2012

Next Generation Heavy-Lift Launch Vehicle: Large Diameter, Hydrocarbon-Fueled Concepts

With the passage of the 2010 NASA Authorization Act, NASA was directed to begin the development of the Space Launch System (SLS) as a follow-on to the Space Shuttle Program. The SLS is envisioned as a heavy lift launch vehicle that will provide the foundation for future large-scale, beyond low Earth orbit (LEO) missions. Supporting the Mission Concept Review (MCR) milestone, several teams were formed to conduct an initial Requirements Analysis Cycle (RAC). These teams identified several vehicle concept candidates capable of meeting the preliminary system requirements. One such team, dubbed RAC Team 2, was tasked with identifying launch vehicles that are based on large stage diameters (up to the Saturn V S-IC and S-II stage diameters of 33 ft) and utilize high-thrust liquid oxygen (LOX)/RP engines as a First Stage propulsion system. While the trade space for this class of LOX/RP vehicles is relatively large, recent NASA activities (namely the Heavy Lift Launch Vehicle Study in late 2009 and the Heavy Lift Propulsion Technology Study of 2010) examined specific families within this trade space. Although the findings from these studies were incorporated in the Team 2 activity, additional branches of the trade space were examined and alternative approaches to vehicle development were considered. Furthermore, Team 2 set out to define a highly functional, flexible, and cost-effective launch vehicle concept. Utilizing this approach, a versatile two-stage launch vehicle concept was chosen as a preferred option. The preferred vehicle option has the capability to fly in several different configurations (e.g. engine arrangements) that gives this concept an inherent operational flexibility which allows the vehicle to meet a wide range of performance requirements without the need for costly block upgrades. Even still, this concept preserves the option for evolvability should the need arise in future mission scenarios. The foundation of this conceptual design is a focus on low cost and effectiveness rather than efficiency or cutting-edge technology. This paper details the approach and process, as well as the trade space analysis, leading to the preferred vehicle concept.

Holliday, Jon↗

NASA NTRS2014

Small Spacecraft System-Level Design and Optimization for Interplanetary Trajectories

The feasibility of an interplanetary mission for a CubeSat, a type of miniaturized spacecraft, that uses an emerging technology, the CubeSat Ambipolar Thruster (CAT) is investigated. CAT is a large delta-V propulsion system that uses a high-density plasma source that has been miniaturized for small spacecraft applications. An initial feasibility assessment that demonstrated escaping Low Earth Orbit (LEO) and achieving Earth-escape trajectories with a 3U CubeSat and this thruster technology was demonstrated in previous work. We examine a mission architecture with a trajectory that begins in Earth orbits such as LEO and Geostationary Earth Orbit (GEO) which escapes Earth orbit and travels to Mars, Jupiter, or Saturn. The goal was to minimize travel time to reach the destinations and considering trade-offs between spacecraft dry mass, fuel mass, and solar power array size. Sensitivities to spacecraft dry mass and available power are considered. CubeSats are extremely size, mass, and power constrained, and their subsystems are tightly coupled, limiting their performance potential. System-level modeling, simulation, and optimization approaches are necessary to find feasible and optimal operational solutions to ensure system-level interactions are modeled. Thus, propulsion, power/energy, attitude, and orbit transfer models are integrated to enable systems-level analysis and trades. The CAT technology broadens the possible missions achievable with small satellites. In particular, this technology enables more sophisticated maneuvers by small spacecraft such as polar orbit insertion from an equatorial orbit, LEO to GEO transfers, Earth-escape trajectories, and transfers to other interplanetary bodies. This work lays the groundwork for upcoming CubeSat launch opportunities and supports future development of interplanetary and constellation CubeSat and small satellite mission concepts.

Spangelo, Sara↗

NASA NTRS2010

Florida Tech CubeSat Experiment Feasibility Study

CubeSats are a relatively new type of satellite. Smaller than long-term (5+ year life expectancy) satellites, these pico-satellites are comparatively cheap, small (10x10x10 cm), and are very versatile. Universities world-wide are using CubeSats to conduct a variety of experiments in space without the need for a large experimental platform. Today CubeSats are considered to be one of the most effective ways to send a small payload into space and has attracted the attention of many educational and non-profit organizations. As this pico-satellite model continues to gain penetration into the satellite build and launch industry, it is expected that more governmental, educational, and commercial interests will emerge. As an example, more of the space-related items of high interest to the National Science Foundation may be tackled with a CubeSat platform resulting in lower life cycle costs than traditional satellite options. NASA LSP, in cooperation with the Florida Institute of Technology, has initiated a feasibility study to investigate the technical aspects of measuring and transferring vibration, acceleration, temperature, and video data from a CubeSat to NASA Hanger AE on Cape Canaveral Air Force Station (CCAFS) a.k.a. Kennedy Space Center (KSC). This report provides a technical feasibility analysis to determine whether-or-not a specific set of NASA/LSP requirements can be accomplished. Our approach has been to provide a "notional" component layout to determine the feasibility of the NASA/LSP stakeholder requirements. The notional layout is used to consider component level technical issues such as size, weight, & power (SWaP), bandwidth, and other critical technical parameters. Even though the notional components may satisfy the stated requirements and thereby demonstrate feasibility, the notional layout is NOT considered a design since no component optimization and design trade-off analysis has taken place. This activity should be accomplished in an appropriate design phase that is outside of the scope of this effort.

Arrasmith, William W.↗

NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center is developing a pre-Phase A tool called "Trade-space Analysis Tool for Constellations" (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper focuses on the tradespace search and how it can be streamlined by combining physical rules, as well as well-designed orbit and coverage computations, thus yielding significant speed-ups. Two use cases are shown as representative examples of the utility of TAT-C generated trades, and results are preliminarily validated against AGI's Systems Tool Kit.

Science Data Processing↗

NASA NTRS2011

Space Missions Trade Space Generation and Assessment Using JPL Rapid Mission Architecture (RMA) Team Approach

The JPL Rapid Mission Architecture (RMA) capability is a novel collaborative team-based approach to generate new mission architectures, explore broad trade space options, and conduct architecture-level analyses. RMA studies address feasibility and identify best candidates to proceed to further detailed design studies. Development of RMA first began at JPL in 2007 and has evolved to address the need for rapid, effective early mission architectural development and trade space exploration as a precursor to traditional point design evaluations. The RMA approach integrates a small team of architecture-level experts (typically 6-10 people) to generate and explore a wide-ranging trade space of mission architectures driven by the mission science (or technology) objectives. Group brainstorming and trade space analyses are conducted at a higher level of assessment across multiple mission architectures and systems to enable rapid assessment of a set of diverse, innovative concepts. This paper describes the overall JPL RMA team, process, and high-level approach. Some illustrative results from previous JPL RMA studies are discussed.

real time systems↗

DOE OSTI2025

A dynamic 2D Borehole Thermal Energy Storage (BTES) model for enhanced computational efficiency

Progressing toward a future increasingly reliant on renewable energy sources, the development of effective, durable energy storage solutions becomes essential to balance supply and demand fluctuations. Borehole Thermal Energy Storage (BTES) is a long-duration thermal energy storage technology that captures excess heat generated from renewable energy sources and stores it underground for later use, enabling the efficient utilization of sustainable energy. This approach is particularly valuable in district energy networks when integrated with Ground Source Heat Pumps (GSHP) to provide stable heating and cooling. However, traditional three-dimensional (3D) numerical models of BTES systems demand extensive computational resources, limiting their practicality for real-time and large-scale applications. This study introduces a novel two-dimensional (2D) modeling approach that reduces computational costs while maintaining high accuracy. By employing a radial ring-based discretization method, the model simulates heat injection, retention, and retrieval dynamics over seasonal cycles. A new thermal-mass weighted-average temperature parameter is introduced to evaluate the performance of BTES systems. Model validation against FEFLOW simulations demonstrates a 17-fold improvement in computational speed compared to traditional Computational Fluid Dynamics (CFD) models while achieving a mean absolute percentage error (MAPE) of 2 % during charging and 4 % during discharging. Additionally, a trade-off analysis between computational efficiency and accuracy is conducted, ensuring the model's applicability for real-world scenarios. The findings of this research contribute to the development of computationally efficient BTES models, facilitating better optimization, control, and integration into renewable energy systems. This work provides a foundation for further studies in techno-economic analysis, multi-year performance evaluation, and real-time operational strategies for BTES applications, supporting a more sustainable energy future.

2D modeling↗

DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A systems engineering framework for the optimization of food supply chains under circular economy considerations

The current linear “take-make-waste-extractive” model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, in this work, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s

Abstract Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade‐study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Dynamic Facade Dashboard v0.1.0

The dashboard is a useful tool for early-stage building design decision-making and communication, as it can help users quickly compare the energy and non-energy related performance of various automated, integrated facade systems using a library of pre-computed data. Users can explore the impacts of various design choices by selecting different facade glazing and shading systems, facade control strategies, and lighting control strategies across multiple climate zones. The dashboard instantly visualizes key metrics, including energy usage in HVAC and lighting, peak cooling and heating load, and daylight availability, allowing immediate trade-off analysis to optimize building efficiency and comfort.

Yu, Tammie [Lawrence Berkeley National Laboratory ↗