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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 NTRS2009

Active Learning for Directed Exploration of Complex Systems

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

oracles↗

NASA NTRS2014

MDO and Cross-Disciplinary Practice in R&D: A Portrait of Principles and Current Practice

For several decades, Multidisciplinary Design Optimization (MDO) has served an important role in aerospace engineering by incorporating physics based disciplinary models into integrated system or sub-system models for use in research, development, (R&D) and design. This paper examines MDO's role in facilitating the integration of the researchers from different single disciplines during R&D and early design of large-scale complex engineered systems (LaCES) such as aerospace systems. The findings in this paper are summarized from a larger study on interdisciplinary practices and perspectives that included considerable empirical data from surveys, interviews, and ethnography. The synthesized findings were derived by integrating the data with theories from organization science and engineering. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. Correspondingly, the data showed that MDO is not the primary integrator of researchers working across disciplines during R&D and early design of LaCES. Cognitive focus such as analysis versus design, organizational challenges such as incentives, and social opportunities such as personal networks often drove the human interactive practices among researchers from different disciplines. Facilitation of the inherent confusion, argument, and learning in crossdisciplinary research was identified as one of several needed elements of enabling successful research across disciplines.

Rivas McGowan, Anna-Maria↗

NASA NTRS1962

Saturn: A Giant Thrust into Space

The film provides an introduction and overview of the Saturn launch vehicle. It is designed with stages to drop off as fuel is spent. There may be two, three, or four stages, depending on the payload. The Saturn rocket will be used to send Apollo missions to the Moon and back. Guidance systems and booster engine rockets are based on proven mechanisms. Scale models are used to test the engines. Hardware, airframes, guidance systems, instrumentation, and the rockets are produced at sites throughout the country. The engines go to Marshall Space Flight Center for further tests. After partial assembly, the vehicle is shipped to Cape Canaveral in large pieces where it is assembled using specially built equipment and structures. Further trials are performed to assure successful launches.

Source record↗

DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗

DOE OSTI2024

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.

17 WIND ENERGY↗

NASA NTRS2009

Formal Methods for Automated Diagnosis of Autosub 6000

This is a progress report on applying formal methods in the context of building an automated diagnosis and recovery system for Autosub 6000, an Autonomous Underwater Vehicle (AUV). The diagnosis task involves building abstract models of the control system of the AUV. The diagnosis engine is based on Livingstone 2, a model-based diagnoser originally built for aerospace applications. Large parts of the diagnosis model can be built without concrete knowledge about each mission, but actual mission scripts and configuration parameters that carry important information for diagnosis are changed for every mission. Thus we use formal methods for generating the mission control part of the diagnosis model automatically from the mission script and perform a number of invariant checks to validate the configuration. After the diagnosis model is augmented with the generated mission control component model, it needs to be validated using verification techniques.

Ernits, Juhan↗

NASA NTRSDate not supplied

A Safety-Driven Approach to Exploring and Comparing Air Traffic Management Concepts for Enabling Urban Air Mobility

There is broad recognition that the high tempo and density of Urban Air Mobility (UAM) operations will require identifying new Air Traffic Management (ATM) concepts to safely integrate UAM air traffic into the airspace alongside existing air traffic. However, the simulation models used to compare ATM concepts today are difficult to apply during the early stages of concept development and do not offer enough support in identifying potential new concepts. In addition, they have limited ability to evaluate ATM concepts in terms of safety, security, and other key emergent properties. Instead of using simulation to evaluate ATM concepts, this paper demonstrates how a safety-driven systems engineering approach based on Systems-Theoretic Accident Model and Processes (STAMP) can be used to design properties such as safety into an ATM system from the earliest stages of development. Using a hazard analysis technique called Systems-Theoretic Process Analysis (STPA), system requirements and the desired ATM behavior are derived. As an example, two possible ATM concepts to implement that behavior are compared to identify their safety-related benefits and tradeoffs. This new approach enables (1) systematic exploration of alternative ATM concepts and (2) identification of the safety-related tradeoffs between concepts as early as possible in the development process.

system safety↗

NASA NTRS2003

The Livingstone Model of a Main Propulsion System

Livingstone is a discrete, propositional logic-based inference engine that has been used for diagnosis of physical systems. We present a component-based model of a Main Propulsion System (MPS) and say how it is used with Livingstone (L2) in order to implement a diagnostic system for integrated vehicle health management (IVHM) for the Propulsion IVHM Technology Experiment (PITEX). We start by discussing the process of conceptualizing such a model. We describe graphical tools that facilitated the generation of the model. The model is composed of components (which map onto physical components), connections between components and constraints. A component is specified by variables, with a set of discrete, qualitative values for each variable in its local nominal and failure modes. For each mode, the model specifies the component's behavior and transitions. We describe the MPS components' nominal and fault modes and associated Livingstone variables and data structures. Given this model, and observed external commands and observations from the system, Livingstone tracks the state of the MPS over discrete time-steps by choosing trajectories that are consistent with observations. We briefly discuss how the compiled model fits into the overall PITEX architecture. Finally we summarize our modeling experience, discuss advantages and disadvantages of our approach, and suggest enhancements to the modeling process.

Bajwa, Anupa↗

DOE OSTI2025

Multiscale Modeling of the Mechanical Response of Silicon Carbide Composite Within the Accelerated Fuel Qualification Framework

The accelerated fuel qualification (AFQ) framework has been used for the initial development of multiscale modeling of silicon carbide (SiC) fiber reinforced composite (SiC-SiC). The AFQ framework provides a methodology to leverage physics-informed multiscale modeling along with a reduced set of empirical test data to reduce the time and cost of licensing and qualification of new nuclear fuel systems while maintaining the overall nuclear power plant safety case. SiC-SiC is being proposed for in-core applications, most notably fuel cladding, for current and next-generation nuclear reactors because of its high temperature stability, irradiation tolerance, and ability to withstand many accident conditions. As these composites exhibit multiscale architectures and complex microstructure-based fracture mechanics, it is an appealing use case for the AFQ methodology. While the end goal of this work is a single multiscale model that can be used for predictive in-core performance, current focus is on the individual various length scale models. Four individual models have been initially developed from microscale to engineering system level to capture key physics-based effects across different length scales. These models include a microscale homogenized tow model, a mesoscale fast Fourier transform–based weave model that integrates the homogenized tow model, a mesoscale finite element–based weave model, and a system-level BISON fuel performance model. Results of these models have undergone an initial comparison with separate-effects test data showing a good match to experimental results. By using the AFQ framework during model development, several near-term benefits have been secured including a reduction in development time for the SiC-SiC cladding, more targeted irradiation testing, and a better understanding of uncertainty.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1990

Real-time diagnostics of the reusable rocket engine using on-line system identification

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Guo, T.-H.↗

NASA NTRS1992

Formalization and visualization of domain-specific software architectures

This paper describes a domain-specific software design system based on the concepts of software architectures engineering and domain-specific models and languages. In this system, software architectures are used as high level abstractions to formulate a domain-specific software design. The software architecture serves as a framework for composing architectural fragments (e.g., domain objects, system components, and hardware interfaces) that make up the knowledge (or model) base for solving a problem in a particular application area. A corresponding software design is generated by analyzing and describing a system in the context of the software architecture. While the software architecture serves as the framework for the design, this concept is insufficient by itself for supplying the additional details required for a specific design. Additional domain knowledge is still needed to instantiate components of the architecture and develop optimized algorithms for the problem domain. One possible way to obtain the additional details is through the use of domain-specific languages. Thus, the general concept of a software architecture and the specific design details provided by domain-specific languages are combined to create what can be termed a domain-specific software architecture (DSSA).

Bailor, Paul D.↗

NASA NTRS2003

Lessons Learned from using a Livingstone Model to Diagnose a Main Propulsion System

NASA researchers have demonstrated that qualitative, model-based reasoning can be used for fault detection in a Main Propulsion System (MPS), a complex, continuous system. At the heart of this diagnostic system is Livingstone, a discrete, propositional logic-based inference engine. Livingstone comprises a language for specifying a discrete model of the system and a set of algorithms that use the model to track the system's state. Livingstone uses the model to test assumptions about the state of a component - observations from the system are compared with values predicted by the model. The intent of this paper is to summarize some advantages of Livingstone seen through our modeling experience: for instance, flexibility in modeling, speed and maturity. We also describe some shortcomings we perceived in the implementation of Livingstone, such as modeling continuous dynamics and handling of transients. We list some upcoming enhancements to the next version of Livingstone that may resolve some of the current limitations.

Sweet, Adam↗

NASA NTRS2016

Modeling in the State Flow Environment to Support Launch Vehicle Verification Testing for Mission and Fault Management Algorithms in the NASA Space Launch System

Analysis methods and testing processes are essential activities in the engineering development and verification of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS). Central to mission success is reliable verification of the Mission and Fault Management (M&FM) algorithms for the SLS launch vehicle (LV) flight software. This is particularly difficult because M&FM algorithms integrate and operate LV subsystems, which consist of diverse forms of hardware and software themselves, with equally diverse integration from the engineering disciplines of LV subsystems. M&FM operation of SLS requires a changing mix of LV automation. During pre-launch the LV is primarily operated by the Kennedy Space Center (KSC) Ground Systems Development and Operations (GSDO) organization with some LV automation of time-critical functions, and much more autonomous LV operations during ascent that have crucial interactions with the Orion crew capsule, its astronauts, and with mission controllers at the Johnson Space Center. M&FM algorithms must perform all nominal mission commanding via the flight computer to control LV states from pre-launch through disposal and also address failure conditions by initiating autonomous or commanded aborts (crew capsule escape from the failing LV), redundancy management of failing subsystems and components, and safing actions to reduce or prevent threats to ground systems and crew. To address the criticality of the verification testing of these algorithms, the NASA M&FM team has utilized the State Flow environment6 (SFE) with its existing Vehicle Management End-to-End Testbed (VMET) platform which also hosts vendor-supplied physics-based LV subsystem models. The human-derived M&FM algorithms are designed and vetted in Integrated Development Teams composed of design and development disciplines such as Systems Engineering, Flight Software (FSW), Safety and Mission Assurance (S&MA) and major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GN&C), Thrust Vector Control (TVC), liquid engines, and the astronaut crew office. Since the algorithms are realized using model-based engineering (MBE) methods from a hybrid of the Unified Modeling Language (UML) and Systems Modeling Language (SysML), SFE methods are a natural fit to provide an in depth analysis of the interactive behavior of these algorithms with the SLS LV subsystem models. For this, the M&FM algorithms and the SLS LV subsystem models are modeled using constructs provided by Matlab which also enables modeling of the accompanying interfaces providing greater flexibility for integrated testing and analysis, which helps forecast expected behavior in forward VMET integrated testing activities. In VMET, the M&FM algorithms are prototyped and implemented using the same C++ programming language and similar state machine architectural concepts used by the FSW group. Due to the interactive complexity of the algorithms, VMET testing thus far has verified all the individual M&FM subsystem algorithms with select subsystem vendor models but is steadily progressing to assessing the interactive behavior of these algorithms with LV subsystems, as represented by subsystem models. The novel SFE applications has proven to be useful for quick look analysis into early integrated system behavior and assessment of the M&FM algorithms with the modeled LV subsystems. This early MBE analysis generates vital insight into the integrated system behaviors, algorithm sensitivities, design issues, and has aided in the debugging of the M&FM algorithms well before full testing can begin in more expensive, higher fidelity but more arduous environments such as VMET, FSW testing, and the Systems Integration Lab7 (SIL). SFE has exhibited both expected and unexpected behaviors in nominal and off nominal test cases prior to full VMET testing. In many findings, these behavioral characteristics were used to correct the M&FM algorithms, enable better test coverage, and develop more effective test cases for each of the LV subsystems. This has improved the fidelity of testing and planning for the next generation of M&FM algorithms as the SLS program evolves from non-crewed to crewed flight, impacting subsystem configurations and the M&FM algorithms that control them. SFE analysis has improved robustness and reliability of the M&FM algorithms by revealing implementation errors and documentation inconsistencies. It is also improving planning efficiency for future VMET testing of the M&FM algorithms hosted in the LV flight computers, further reducing risk for the SLS launch infrastructure, the SLS LV, and most importantly the crew.

Trevino, Luis↗

NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗

NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Verification and Validation↗

NASA NTRS1984

SPIRE Data-Base Management System

Spacelab Payload Integration and Rocket Experiment (SPIRE) data-base management system (DBMS) based on relational model of data bases. Data bases typically used for engineering and mission analysis tasks and, unlike most commercially available systems, allow data items and data structures stored in forms suitable for direct analytical computation. SPIRE DBMS designed to support data requests from interactive users as well as applications programs.

Fuechsel, C. F.↗

DOE OSTI2020

What Questions Would a Systems Engineer Ask to Assess Systems Engineering Models as Credible

Digital Systems Engineering strategies typically call for digital Systems Engineering models to be retained in repositories and certified as an authoritative source of truth (enabling model reuse, qualification, and collaboration). In order for digital Systems Engineering models to be certified as authoritative (credible), they need to be assessed - verified and validated - and with the amount of uncertainty in the model quantified (consider reusing someone else's model without knowing the author). Due to this increasing model complexity, the authors assert that traditional human-based methods for validating, verifying, and uncertainty quantification - such as human-based peer-review sessions - cannot sufficiently establish that a digital Systems Engineering model of a complex system is credible. Digital Systems Engineering models of complex systems can contain millions of nodes and edges. The authors assert that this level of detail is beyond the ability of any group of humans - even working for weeks at a time - to discern and catch every minor model infraction. In contrast, computers are highly effective at discerning infractions with massive amounts of information. The authors suggest that a better approach might be to focus the humans at what model patterns should be assessed and enable the computer to assess the massive details in accordance with those patterns - by running through perhaps 100,000 test loops. In anticipation of future projects to implement and automate the assessment of models at Sandia National Laboratories, a study was initiated to elicit input from a group of 25 Systems Engineering experts. The authors positioning query began with - 'What questions would a Systems Engineer ask to assess Systems Engineering models for credibility?" This report documents the results of that survey.

42 ENGINEERING↗

NASA NTRS1993

Qualitative model-based diagnostics for rocket systems

A diagnostic software package is currently being developed at NASA LeRC that utilizes qualitative model-based reasoning techniques. These techniques can provide diagnostic information about the operational condition of the modeled rocket engine system or subsystem. The diagnostic package combines a qualitative model solver with a constraint suspension algorithm. The constraint suspension algorithm directs the solver's operation to provide valuable fault isolation information about the modeled system. A qualitative model of the Space Shuttle Main Engine's oxidizer supply components was generated. A diagnostic application based on this qualitative model was constructed to process four test cases: three numerical simulations and one actual test firing. The diagnostic tool's fault isolation output compared favorably with the input fault condition.

Maul, William↗