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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Development of the Architectural Simulation Model for Future Launch Systems and its Application to an Existing Launch Fleet

A significant portion of lifecycle costs for launch vehicles are generated during the operations phase. Research indicates that operations costs can account for a large percentage of the total life-cycle costs of reusable space transportation systems. These costs are largely determined by decisions made early during conceptual design. Therefore, operational considerations are an important part of vehicle design and concept analysis process that needs to be modeled and studied early in the design phase. However, this is a difficult and challenging task due to uncertainties of operations definitions, the dynamic and combinatorial nature of the processes, and lack of analytical models and the scarcity of historical data during the conceptual design phase. Ultimately, NASA would like to know the best mix of launch vehicle concepts that would meet the missions launch dates at the minimum cost. To answer this question, we first need to develop a model to estimate the total cost, including the operational cost, to accomplish this set of missions. In this project, we have developed and implemented a discrete-event simulation model using ARENA (a simulation modeling environment) to determine this cost assessment. Discrete-event simulation is widely used in modeling complex systems, including transportation systems, due to its flexibility, and ability to capture the dynamics of the system. The simulation model accepts manifest inputs including the set of missions that need to be accomplished over a period of time, the clients (e.g., NASA or DoD) who wish to transport the payload to space, the payload weights, and their destinations (e.g., International Space Station, LEO, or GEO). A user of the simulation model can define an architecture of reusable or expendable launch vehicles to achieve these missions. Launch vehicles may belong to different families where each family may have it own set of resources, processing times, and cost factors. The goal is to capture the required resource levels of the major launch elements and their required facilities. The model s output can show whether or not a certain architecture of vehicles can meet the launch dates, and if not, how much the delay cost would be. It will also produce aggregate figures of missions cost based on element procurement cost, processing cost, cargo integration cost, delay cost, and mission support cost. One of the most useful features of this model is that it is stochastic where it accepts statistical distributions to represent the processing times mimicking the stochastic nature of real systems.

Rabadi, Ghaith↗
NASA NTRS2005

Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

Hojnicki, Jeffrey S.↗
NASA NTRS1994

Evaluating modeling tools for the EDOS

The Earth Observing System (EOS) Data and Operations System (EDOS) Project is developing a functional, system performance model to support the system implementation phase of the EDOS which is being designed and built by the Goddard Space Flight Center (GSFC). The EDOS Project will use modeling to meet two key objectives: (1) manage system design impacts introduced by unplanned changed in mission requirements; and (2) evaluate evolutionary technology insertions throughout the development of the EDOS. To select a suitable modeling tool, the EDOS modeling team developed an approach for evaluating modeling tools and languages by deriving evaluation criteria from both the EDOS modeling requirements and the development plan. Essential and optional features for an appropriate modeling tool were identified and compared with known capabilities of several modeling tools. Vendors were also provided the opportunity to model a representative EDOS processing function to demonstrate the applicability of their modeling tool to the EDOS modeling requirements. This paper emphasizes the importance of using a well defined approach for evaluating tools to model complex systems like the EDOS. The results of this evaluation study do not in any way signify the superiority of any one modeling tool since the results will vary with the specific modeling requirements of each project.

Knoble, Gordon↗
NASA NTRS1993

A programming environment for distributed complex computing. An overview of the Framework for Interdisciplinary Design Optimization (FIDO) project. NASA Langley TOPS exhibit H120b

The Framework for Interdisciplinary Design Optimization (FIDO) is a general programming environment for automating the distribution of complex computing tasks over a networked system of heterogeneous computers. For example, instead of manually passing a complex design problem between its diverse specialty disciplines, the FIDO system provides for automatic interactions between the discipline tasks and facilitates their communications. The FIDO system networks all the computers involved into a distributed heterogeneous computing system, so they have access to centralized data and can work on their parts of the total computation simultaneously in parallel whenever possible. Thus, each computational task can be done by the most appropriate computer. Results can be viewed as they are produced and variables changed manually for steering the process. The software is modular in order to ease migration to new problems: different codes can be substituted for each of the current code modules with little or no effect on the others. The potential for commercial use of FIDO rests in the capability it provides for automatically coordinating diverse computations on a networked system of workstations and computers. For example, FIDO could provide the coordination required for the design of vehicles or electronics or for modeling complex systems.

Townsend, James C.↗
NASA NTRS2014

T-MATS Toolbox for the Modeling and Analysis of Thermodynamic Systems

The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) is a MATLABSimulink (The MathWorks Inc.) plug-in for creating and simulating thermodynamic systems and controls. The package contains generic parameterized components that can be combined with a variable input iterative solver and optimization algorithm to create complex system models, such as gas turbines.

system modeling↗
NASA NTRS1995

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Harrington, Peter DEB.↗
DOE OSTI2024

Basic Energy Sciences Roundtable: Foundational Science to Accelerate Nuclear Energy Innovation

Energy security, availability, and reliability are among the greatest challenges facing the nation and the planet. An abundant potential source of energy resides in the fundamental atomic building blocks of the universe in the form of nuclear fission and fusion reactions. In fact, energy from nuclear fission currently provides the majority of the world’s zero-carbon electricity, and future fusion energy systems offer great promise; carbon-free nuclear energy technologies can be key to the world’s decarbonized energy future. Although contemporary fission systems use well-established technologies to supply safe and efficient baseload power, they could be more fuel efficient and less costly. Moving beyond massive light-water fission reactors to a variety of advanced nuclear systems—which will vary in size and operate in extremes of temperature, corrosivity, and other parameters—will place stringent conditions on materials and chemical systems. New demands will be placed on the coolants and solvents, the materials, and the monitoring tools used in these reactors. Fusion-based nuclear energy will require superior materials to withstand extremely high temperatures, plasma exposure, radiation damage, and implanted gases. The advantages associated with these new fission and fusion technologies will be realized only through continued advancements in the fundamental science underpinning our knowledge of the physics and chemistry of nuclear systems gained via improved experimental and computational methods. In July 2022, the U.S. Department of Energy’s Office of Basic Energy Sciences—in coordination with the Offices of Nuclear Energy, Fusion Energy Sciences, and Advanced Scientific Computing Research—held a virtual roundtable titled “Foundational Science to Accelerate Nuclear Energy Innovation” to discuss the scientific and technical barriers for advanced nuclear energy systems. Five priority research opportunities were identified to address these scientific and technical challenges and to accelerate progress toward the realization of next-generation fusion and fission energy systems. The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous Basic Energy Sciences workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2013

Modeling Complex Cross-Systems Software Interfaces Using SysML

The complex flight and ground systems for NASA human space exploration are designed, built, operated and managed as separate programs and projects. However, each system relies on one or more of the other systems in order to accomplish specific mission objectives, creating a complex, tightly coupled architecture. Thus, there is a fundamental need to understand how each system interacts with the other. To determine if a model-based system engineering approach could be utilized to assist with understanding the complex system interactions, the NASA Engineering and Safety Center (NESC) sponsored a task to develop an approach for performing cross-system behavior modeling. This paper presents the results of applying Model Based Systems Engineering (MBSE) principles using the System Modeling Language (SysML) to define cross-system behaviors and how they map to crosssystem software interfaces documented in system-level Interface Control Documents (ICDs).

Earth orbit↗
NASA NTRS2010

The Importance of HRA in Human Space Flight: Understanding the Risks

Human performance is critical to crew safety during space missions. Humans interact with hardware and software during ground processing, normal flight, and in response to events. Human interactions with hardware and software can cause Loss of Crew and/or Vehicle (LOCV) through improper actions, or may prevent LOCV through recovery and control actions. Humans have the ability to deal with complex situations and system interactions beyond the capability of machines. Human Reliability Analysis (HRA) is a method used to qualitatively and quantitatively assess the occurrence of human failures that affect availability and reliability of complex systems. Modeling human actions with their corresponding failure probabilities in a Probabilistic Risk Assessment (PRA) provides a more complete picture of system risks and risk contributions. A high-quality HRA can provide valuable information on potential areas for improvement, including training, procedures, human interfaces design, and the need for automation. Modeling human error has always been a challenge in part because performance data is not always readily available. For spaceflight, the challenge is amplified not only because of the small number of participants and limited amount of performance data available, but also due to the lack of definition of the unique factors influencing human performance in space. These factors, called performance shaping factors in HRA terminology, are used in HRA techniques to modify basic human error probabilities in order to capture the context of an analyzed task. Many of the human error modeling techniques were developed within the context of nuclear power plants and therefore the methodologies do not address spaceflight factors such as the effects of microgravity and longer duration missions. This presentation will describe the types of human error risks which have shown up as risk drivers in the Shuttle PRA which may be applicable to commercial space flight. As with other large PRAs of complex machines, human error in the Shuttle PRA proved to be an important contributor (~12 percent) to LOCV. An existing HRA technique was adapted for use in the Shuttle PRA, but additional guidance and improvements are needed to make the HRA task in space-related PRAs easier and more accurate. Therefore, this presentation will also outline plans for expanding current HRA methodology to more explicitly cover spaceflight performance shaping factors.

Hamlin, Teri↗
DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Modular Manufacturing Simulator: Users Manual

The Modular Manufacturing Simulator (MMS) has been developed for the beginning user of computer simulations. Consequently, the MMS cannot model complex systems that require branching and convergence logic. Once a user becomes more proficient in computer simulation and wants to add more complexity, the user is encouraged to use one of the many available commercial simulation systems. The (MMS) is based on the SSE5 that was developed in the early 1990's by the University of Alabama in Huntsville (UAH). A recent survey by MSFC indicated that the simulator has been a major contributor to the economic impact of the MSFC technology transfer program. Many manufacturers have requested additional features for the SSE5. Consequently, the following features have been added to the MMS that are not available in the SSE5: runs under Windows, print option for both input parameters and output statistics, operator can be fixed at a station or assigned to a group of stations, operator movement based on time limit, part limit, or work-in-process (WIP) limit at next station. The movement options for a moveable operators are: go to station with largest WIP, rabbit chase where operator moves in circular sequence between stations, and push/pull where operator moves back and forth between stations. This user's manual contains the necessary information for installing the MMS on a PC, a description of the various MMS commands, and the solutions to a number of sample problems using the MMS. Also included in the beginning of this report is a brief discussion of technology transfer.

Source record↗
NASA NTRS2001

Toward Failure Modeling In Complex Dynamic Systems: Impact of Design and Manufacturing Variations

When designing vehicle vibration monitoring systems for aerospace devices, it is common to use well-established models of vibration features to determine whether failures or defects exist. Most of the algorithms used for failure detection rely on these models to detect significant changes during a flight environment. In actual practice, however, most vehicle vibration monitoring systems are corrupted by high rates of false alarms and missed detections. Research conducted at the NASA Ames Research Center has determined that a major reason for the high rates of false alarms and missed detections is the numerous sources of statistical variations that are not taken into account in the. modeling assumptions. In this paper, we address one such source of variations, namely, those caused during the design and manufacturing of rotating machinery components that make up aerospace systems. We present a novel way of modeling the vibration response by including design variations via probabilistic methods. The results demonstrate initial feasibility of the method, showing great promise in developing a general methodology for designing more accurate aerospace vehicle vibration monitoring systems.

Tumer, Irem Y.↗
NASA NTRS1986

Reliability models applicable to space telescope solar array assembly system

A complex system may consist of a number of subsystems with several components in series, parallel, or combination of both series and parallel. In order to predict how well the system will perform, it is necessary to know the reliabilities of the subsystems and the reliability of the whole system. The objective of the present study is to develop mathematical models of the reliability which are applicable to complex systems. The models are determined by assuming k failures out of n components in a subsystem. By taking k = 1 and k = n, these models reduce to parallel and series models; hence, the models can be specialized to parallel, series combination systems. The models are developed by assuming the failure rates of the components as functions of time and as such, can be applied to processes with or without aging effects. The reliability models are further specialized to Space Telescope Solar Arrray (STSA) System. The STSA consists of 20 identical solar panel assemblies (SPA's). The reliabilities of the SPA's are determined by the reliabilities of solar cell strings, interconnects, and diodes. The estimates of the reliability of the system for one to five years are calculated by using the reliability estimates of solar cells and interconnects given n ESA documents. Aging effects in relation to breaks in interconnects are discussed.

Patil, S. A.↗
NASA NTRSDate not supplied

Resilience Modeling in Complex Engineered Systems with Human-Machine Interactions

In recent times, there has been a growing interest in resilience-based design. Resilience-based design operates on the concept that failures and unexpected events will happen, and when they occur, complex engineered systems should be able to operate within acceptable bounds and recover reasonably. Humans can contribute to the resilience of a system by quickly detecting unforeseen events and taking corrective measures. To this effect, researchers have proposed guidelines and design approaches that can help promote human-system resilience. However, there is no early design stage tool to validate if a system is indeed resilient after applying these guidelines and design methods. In this research, we integrate the Human Error and Functional Failure Reasoning (HEFFR) framework into the fmdtools toolkit to enable designers to model the combined (machine, human, and joint) failures, including their propagation and dynamic effects, during early design stages. This integrated tool also allows designers to model the effects of performance shaping factors, team dynamics, and human-machine interactions in systems of systems. A demonstrative example of a remotely operated rover is explored to demonstrate how this approach can be applied to understand resilience in complex engineered systems with human interactions.

Lukman Irshad↗
NASA NTRSDate not supplied

Model Based Approaches for Fault Detection, Prognostics, Decision Making in Complex Systems

The presentation discusses application of model based approaches to complex systems. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗
NASA NTRS1984

A geometric modeler based on a dual-geometry representation polyhedra and rational b-splines

For speed and data base reasons, solid geometric modeling of large complex practical systems is usually approximated by a polyhedra representation. Precise parametric surface and implicit algebraic modelers are available but it is not yet practical to model the same level of system complexity with these precise modelers. In response to this contrast the GEOMOD geometric modeling system was built so that a polyhedra abstraction of the geometry would be available for interactive modeling without losing the precise definition of the geometry. Part of the reason that polyhedra modelers are effective is that all bounded surfaces can be represented in a single canonical format (i.e., sets of planar polygons). This permits a very simple and compact data structure. Nonuniform rational B-splines are currently the best representation to describe a very large class of geometry precisely with one canonical format. The specific capabilities of the modeler are described.

Klosterman, A. L.↗