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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Simon, Donald L.↗
NASA NTRS1993

Three-dimensional modeling of diesel engine intake flow, combustion and emissions-2

A three-dimensional computer code, KIVA, is being modified to include state-of-the-art submodels for diesel engine flow and combustion. Improved and/or new submodels which have already been implemented and previously reported are: wall heat transfer with unsteadiness and compressibility, laminar-turbulent characteristic time combustion with unburned HC and Zeldo'vich NO(x), and spray/wall impingement with rebounding and sliding drops. Progress on the implementation of improved spray drop drag and drop breakup models, the formulation and testing of a multistep kinetics ignition model, and preliminary soot modeling results are described. In addition, the use of a block structured version of KIVA to model the intake flow process is described. A grid generation scheme was developed for modeling realistic (complex) engine geometries, and computations were made of intake flow in the ports and combustion chamber of a two-intake-value engine. The research also involves the use of the code to assess the effects of subprocesses on diesel engine performance. The accuracy of the predictions is being tested by comparisons with engine experiments. To date, comparisons were made with measured engine cylinder pressure, temperature and heat flux data, and the model results are in good agreement with the experiments. Work is in progress that will allow validation of in-cylinder flow and soot formation predictions. An engine test facility is described that is being used to provide the needed validation data. Test results were obtained showing the effect of injection rate and split injections on engine performance and emissions.

Reitz, R. D.↗
NASA NTRS2014

Development of a Twin-Spool Turbofan Engine Simulation Using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS)

The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) is a tool that has been developed to allow a user to build custom models of systems governed by thermodynamic principles using a template to model each basic process. Validation of this tool in an engine model application was performed through reconstruction of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (v2) using the building blocks from the T-MATS (v1) library. In order to match the two engine models, it was necessary to address differences in several assumptions made in the two modeling approaches. After these modifications were made, validation of the engine model continued by integrating both a steady-state and dynamic iterative solver with the engine plant and comparing results from steady-state and transient simulation of the T-MATS and C-MAPSS models. The results show that the T-MATS engine model was accurate within 3% of the C-MAPSS model, with inaccuracy attributed to the increased dimension of the iterative solver solution space required by the engine model constructed using the T-MATS library. This demonstrates that, given an understanding of the modeling assumptions made in T-MATS and a baseline model, the T-MATS tool provides a viable option for constructing a computational model of a twin-spool turbofan engine that may be used in simulation studies.

numerical simulation↗
NASA NTRS2002

ISO 9000 and/or Systems Engineering Capability Maturity Model?

For businesses and organizations to remain competitive today they must have processes and systems in place that will allow them to first identify customer needs and then develop products/processes that will meet or exceed the customers needs and expectations. Customer needs, once identified, are normally stated as requirements. Designers can then develop products/processes that will meet these requirements. Several functions, such as quality management and systems engineering management are used to assist product development teams in the development process. Both functions exist in all organizations and both have a similar objective, which is to ensure that developed processes will meet customer requirements. Are efforts in these organizations being duplicated? Are both functions needed by organizations? What are the similarities and differences between the functions listed above? ISO 9000 is an international standard of goods and services. It sets broad requirements for the assurance of quality and for management's involvement. It requires organizations to document the processes and to follow these documented processes. ISO 9000 gives customers assurance that the suppliers have control of the process for product development. Systems engineering can broadly be defined as a discipline that seeks to ensure that all requirements for a system are satisfied throughout the life of the system by preserving their interrelationship. The key activities of systems engineering include requirements analysis, functional analysis/allocation, design synthesis and verification, and system analysis and control. The systems engineering process, when followed properly, will lead to higher quality products, lower cost products, and shorter development cycles. The System Engineering Capability Maturity Model (SE-CMM) will allow companies to measure their system engineering capability and continuously improve those capabilities. ISO 9000 and SE-CMM seem to have a similar objective, which is to document the organization's processes and certify to potential customers the capability of a supplier to control the processes that determine the quality of the product or services being produced. The remaining sections of this report examine the differences and similarities between ISO 9000 and SE-CMM and make recommendations for implementation.

Sampson E. Gholston↗
DOE OSTI2020

PORFLOW Implementation of Special Waste from Models for Slit and Engineered Trenches in the E-Area Low Level Waste Facility Performance Assessment

The implementation of special waste forms (SWFs) in the deterministic PORFLOW Slit Trench (ST) and Engineered Trench (ET) models to be used in the next revision of the E-Area Low Level Waste Facility’s (ELLWFs) Performance Assessment (PA) is outlined in this report. Four SWF implementation methods will be used: effective K d , delayed release, solubility-controlled/diffusion-controlled release, and complex SWF model updates. In addition, the implementation of models that address the presence of tall used equipment storage boxes in ST08-10 is considered a special waste form and will be described.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

MULTI-DIMENSIONAL MODELING OF THE CFR ENGINE FOR THE INVESTIGATION OF SI NATURAL GAS COMBUSTION AND CONTROLLED END-GAS AUTOIGNITION

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emission limits and the wide range of chemical reactivity in pipeline quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its methane number (analogous to the octane number for liquid SI fuels) can span from 65 - 95. In order to realize diesel-like efficiencies, SI natural gas engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding the combustion-engine interactions pertaining to flame propagation and end-gas autoignition (EGAI). However, EGAI, if controlled, provides an opportunity to increase SI natural gas engine efficiency by increasing combustion rate and the total burned fuel, mitigating the effects of the slow flame speeds of natural gas fuels which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, in order to study EGAI phenomenon, the present work highlights multi-dimensional computational fluid dynamics (CFD) models of the Cooperative Fuel Research (CFR) engine. The CFR engine models are used to investigate fuel-engine interactions that lead to EGAI with natural gas, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). A Three-Pressure Analysis, performed with GT-Power, was used to estimate initial and boundary conditions for the three-dimensional CFD model. CONVERGE CFD v2.4 was used for the three-dimensional CFD modeling where the level set G-Equation model and SAGE detailed chemical kinetics solver were used. An assessment of the different modeling approaches is also provided to evaluate their limitations, advantages and disadvantages, and for which situations they are most applicable. Model validation was performed with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction, and IMEP and shows good agreement in Peak Cylinder Pressure (PCP), PCP crank angle, and the location of the 10%, 50%, and 90% mass fraction burned (CA10, CA50, and CA90, respectively). The models can predict the onset crank angle and pressure rise rate for light, medium, and heavy EGAI under a variety of fuel reactivities and engine operating conditions.

Bestel, Diego↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗
NASA NTRSDate not supplied

Overview of Model-Based Systems Engineering Efforts to Evolve the Airspace Research Roadmap

NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution requires a plan to effectively organize, integrate, and communicate NASA’s research and development. The planning tool, called the UAM airspace research roadmap, or just roadmap, is key to the execution of NASA’s UAM airspace research over the next ten years. Implemented through Model-Based Systems Engineering (MBSE) methodology, the roadmap will help to prioritize and coordinate research efforts, and to integrate results that build towards NASA’s research goals of evolving UAM airspace for integration of UAM operations into the National Airspace System (NAS). This paper presents an overview of on-going MBSE efforts to meet these overarching goals.

Model-Based Systems Engineering↗
NASA NTRSDate not supplied

Overview of Model-Based Systems Engineering Efforts to Evolve the Airspace Research Roadmap

NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution requires a plan to effectively organize, integrate, and communicate NASA’s research and development. The planning tool, called the UAM airspace research roadmap, or just roadmap, is key to the execution of NASA’s UAM airspace research over the next ten years. Implemented through Model-Based Systems Engineering (MBSE) methodology, the roadmap will help to prioritize and coordinate research efforts, and to integrate results that build towards NASA’s research goals of evolving UAM airspace for integration of UAM operations into the National Airspace System (NAS). This paper presents an overview of on-going MBSE efforts to meet these overarching goals. Note: Included mp4 video of presentation included in record, runtime 9 mins 57 secs.

Model-Based Systems Engineering↗
NASA NTRS2015

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.

MBSE↗
NASA NTRSDate not supplied

A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System

In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System(IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation functionspresent in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).

In-time Aviation Safety Management System↗
NASA NTRS1992

Three-dimensional modeling of diesel engine intake flow, combustion and emissions

A three-dimensional computer code (KIVA) is being modified to include state-of-the-art submodels for diesel engine flow and combustion: spray atomization, drop breakup/coalescence, multi-component fuel vaporization, spray/wall interaction, ignition and combustion, wall heat transfer, unburned HC and NOx formation, soot and radiation, and the intake flow process. Improved and/or new submodels which were completed are: wall heat transfer with unsteadiness and compressibility, laminar-turbulent characteristic time combustion with unburned HC and Zeldo'vich NOx, and spray/wall impingement with rebounding and sliding drops. Results to date show that adding the effects of unsteadiness and compressibility improves the accuracy of heat transfer predictions; spray drop rebound can occur from walls at low impingement velocities (e.g., in cold-starting); larger spray drops are formed at the nozzle due to the influence of vaporization on the atomization process; a laminar-and-turbulent characteristic time combustion model has the flexibility to match measured engine combustion data over a wide range of operating conditions; and finally, the characteristic time combustion model can also be extended to allow predictions of ignition. The accuracy of the predictions is being assessed by comparisons with available measurements. Additional supporting experiments are also described briefly. To date, comparisons with measured engine cylinder pressure and heat flux data were made for homogeneous charge, spark-ignited and compression-ignited engines. The model results are in good agreement with the experiments.

Reitz, R. D.↗
NASA NTRSDate not supplied

A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System

In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System (IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation function present in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).

IASMS↗
NASA NTRS1975

Aerodynamic characteristics of a large-scale hybrid upper surface blown flap model having four engines

Data are presented from an investigation of the aerodynamic characteristics of large-scale wind tunnel aircraft model that utilized a hybrid-upper surface blown flap to augment lift. The hybrid concept of this investigation used a portion of the turbofan exhaust air for blowing over the trailing edge flap to provide boundary layer control. The model, tested in the Ames 40- by 80-foot Wind Tunnel, had a 27.5 deg swept wing of aspect ratio 8 and 4 turbofan engines mounted on the upper surface of the wing. The lift of the model was augmented by turbofan exhaust impingement on the wind upper-surface and flap system. Results were obtained for three flap deflections, for some variation of engine nozzle configuration and for jet thrust coefficients from 0 to 3.0. Six-component longitudinal and lateral data are presented with four engine operation and with the critical engine out. In addition, a limited number of cross-plots of the data are presented. All of the tests were made with a downwash rake installed instead of a horizontal tail. Some of these downwash data are also presented.

Carros, R. J.↗
NASA NTRS2003

An advanced model-based diagnosis engine

We have developed a new and powerful diagnosis engine that overcomes the limitations of the existing systematic methods of general diagnosis through a two-fold approach. First, we propose a novel and compact reconstruction of the General Diagnosis Engine, one of the most fundamental approaches to model-based diagnoses. We then present a novel algorithmic approach for calculation of minimal diagnosis set.

model-based diagnosis hitting set problem integer ↗