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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

An Analysis of the Impact of Extreme Attitude Operation on a Turbofan Engine in a Regional Jet Aircraft

The objective of this work is to characterize the variability in engine performance at extreme attitudes, resulting from the use of different standard control variables, evaluated at different points in the engine’s lifecycle. This paper describes the procedure used to create a dynamic model of an aft-mounted jet engine on a T-tail regional jet aircraft. This model enables simulation of engine operation at extreme attitudes. The model was subsequently evaluated at various altitudes, Mach numbers, and power settings over a range of angles of attack and sideslip, and deterioration levels. Each case was simulated using both fan speed and engine pressure ratio as the engine control variable. The results show that the wing has a very large impact on the engine operation, overwhelming other sources of variation.

loss of control - inflight↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

Development and Implementation of a Small Satellite Systems Engineering Webinar Series: A Collaboration Between United Nations Office for Outer Space Affairs and National Aeronautics and Space Administration

The United Nations Office for Outer Space Affairs (UNOOSA) in collaboration with the National Aeronautics and Space Administration (NASA) established a webinar series on NASA systems engineering standards and practices for the purpose of sharing knowledge in this area through UNOOSA’s “Access to Space for All” which provides free and accessible educational content. Through NASA’s Small Spacecraft Systems Virtual Institute (S3VI), the four-part series was designed to cover basic systems engineering and project management skills that are fundamental for planning, developing, and implementing an experiment or a space project, and that serve as critical knowledge for those engaged in space activities, whether as a designer, builder, or manager of space infrastructure and services. This first series of webinars was convened over the course of November 2023 through February 2024. The webinar series was segmented into four one and a half hour webinars which covered topics related to the Fundamentals of Systems Engineering, Requirements and Systems Engineering, System Assembly, Integration and Test, and Spacecraft Handling, as well as an Introduction to the Small Spacecraft Systems Virtual Institute (S3VI). The agendas for the first three webinars focused on addressing a number of questions relevant to a particular topic. Examples of these questions include: What is systems engineering and what does a systems engineer do? Why does NASA use systems engineering? How do you define requirements for a small spacecraft mission? Why are requirements important to space missions? How are trade studies used to determine parts selection and why are they important? The final webinar of the first series provided interactive discussions and demonstrations of the S3VI tools; shared information on additional webinar opportunities offered by the S3VI; and reviewed databases curated by the S3VI that include the Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool, Mission Design Tools, Small Spacecraft Information Search, and the State-of-the-Art (SoA) Small Spacecraft Report. Future webinar series will be conducted with topics are to be determined. The presenter will discuss details related to the first webinar series and future plans for additional series.

systems engineering↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗
NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗
DOE OSTI2020

Dicarboxylic acid emissions from a GDI engine equipped with a catalytic gasoline particulate filter

Dicarboxylic acids play an important role in atmospheric chemistry, yet their emissions from primary sources, such as internal combustion engines, has not been extensively studied. In this paper, KOH impregnated quartz filters were loaded with exhaust gases from a gasoline direct injection (GDI) engine equipped with a catalytic gasoline particulate filter (GPF). All filters were analyzed for carboxylic and dicarboxylic acids using a derivatized gas chromatography-mass spectroscopy method. Exhaust gas was sampled from pre-GPF and post-GPF locations to determine the performance of the GPF regarding acid conversion. Lean and stoichiometric engine modes were considered with non-oxygenated gasoline and 10% splash blended ethanol in gasoline (E10) to examine the impact of stoichiometry and fuel type. Acid emissions represented as much as 0.51% of total unburned hydrocarbon emissions for total monocarboxylic acids and as much as 0.40% for total dicarboxylic acids. Individual acid concentrations were as high as 38 mg/kg-fuel for monocarboxylic acids and as high as 29 mg/kg-fuel for dicarboxylic acids. Overall, the study found that fuel oxygenates had mixed impact on the acid emissions. Engine-out monocarboxylic acids were reduced when using the E10 fuel by approximately 30–45% for the stoichiometric condition and increased marginally for the lean condition. Dicarboxylic acid emissions were generally insensitive to ethanol content. However, the engine condition significantly affected the acid emissions. Lean operation produced a factor of two to an order of magnitude higher emissions rates of both monocarboxylic and dicarboxylic acids than the stoichiometric condition. The catalytic GPF eliminated between 80 and 92% of the acids emitted from the engine, allowing some acids to be emitted into the environment.

42 ENGINEERING↗
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Gasoline fuels properties for multi-mode operation – Observations in a GDI and the CFR engine

The combustion behavior of five full boiling range RON98 gasoline blends was evaluated for multi-mode operation in a GDI and the CFR octane rating engine. The GDI engine tests were conducted with stoichiometric air-fuel ratio in spark-ignition (SI), and with air-diluted homogeneous charge compression ignition (HCCI) mode. In the CFR engine, the knocking combustion was analyzed under standard RON testing conditions at both peak knocking lambda and stoichiometric air-fuel ratios, whereas compression ignited operation was characterized by utilizing the HCCI number test protocol. Disparate knock limited SI and HCCI combustion behavior was observed for the test fuels, despite four of the fuels having the same RON and octane sensitivity. It was found that knock-limited combustion phasing in the GDI engine did not align well with the RON. However, a detailed comparison of knock-limited SI operation in the GDI and CFR engine revealed that a more appropriate effective RON based on a common knock intensity metric (MAPO) and stoichiometric air-fuel ratio resulted in comparable knock characterization between the two engine platforms. Furthermore, the critical intake air temperature and the critical compression ratio were proposed to characterize knock-limited SI operation, while the minimum intake air heating and compression ratio were used to define a fuel's autoignition propensity for compression ignition operation in the GDI and CFR engine, respectively. Finally, each fuel's characteristic compression ratio needed to obtain knock-limited SI (KLSI) and HCCI operation was used to calculate an effective multi-mode octane number (MM-ON) based on the primary reference fuel blends.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Caution on Using Tetrahydrofuran for Processing Crystalline Silica Samples From Engineered Stone for XRD Analysis

Abstract We conducted laboratory experiments to investigate a suspected effect of tetrahydrofuran (THF) on quantifying crystalline silica in samples collected from working with engineered stone when THF is used to process samples prior to the X-ray diffraction (XRD) analysis. Two groups of samples from grinding either engineered stone or granite were simultaneously taken from a laboratory testing system, with one group of samples using THF for processing and another group using muffle furnace for ashing. For each stone type, we also tested four levels of respirable dust loading on the samples by varying the grinding time from 1 to 8 min. Statistical analysis of the experimental results on crystalline silica contents of the two groups of samples showed that the difference between the two methods was not significant (P ≥ 0.05) for the granite at all four levels of respirable dust loading and for the engineered stone at the two levels of respirable dust loading greater than 0.5 mg. However, the crystalline silica content from using THF processing was significantly lower (P = 0.001) than that from using muffle furnace ashing for engineered stone when the respirable dust loading levels were less than 0.5 mg. For the engineered stone dust samples with grinding times of 1 and 2 min, the average respirable dust loading was about 0.19 and 0.34 mg, respectively; while the crystalline silica content from using THF processing was 30.9 and 21.5% lower than that from using muffle furnace ashing, respectively. Since most full-shift samples from field assessments in this industry are expected to have respirable dust loading less than 0.5 mg, muffle furnace or radio frequency plasma ashing should be specified as the preferred sample processing method instead of the THF processing method for quantification of crystalline silica when engineered stone is expected to present to avoid artificially reduced silica content values, which are likely caused by the reactions between THF and the resins in engineered stone.

Public, Environmental & Occupational Health↗
DOE OSTI2026

Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing

A long-lived central engine embedded in expanding supernova ejecta can alter the dynamics and observational signatures of the event, producing an unusually luminous, energetic, and/or rapidly evolving transient. We use 2D hydrodynamics simulations to study the effect of a central energy source, varying the amount, rate, and isotropy of the energy deposition. We post-process the results with a time-dependent Monte Carlo radiation transport code to extract observational signatures. The engine excavates a bubble at the centre of the ejecta, which becomes Rayleigh–Taylor unstable. Sufficiently powerful engines are able to break through the edge of the bubble and accelerate, shred, and compositionally mix the entire ejecta. The breakout of the engine-driven wind occurs at distinct rupture points, and the outflowing high-velocity gas may eventually give rise to radio emission. The dynamical impact of the engine leads to faster rising optical light curves, with photon escape facilitated by the faster expansion of the ejecta and the opening of low-density channels. For models with strong engines, the spectra are initially hot and featureless, but later evolve to resemble those of broad-line Ic supernovae. Under certain conditions, line emission from ionized, low-velocity material near the centre of the ejecta may be able to escape and produce narrow emission similar to that seen in interacting supernovae. We discuss how variability in the engine energy reservoir and injection rate could give rise to a heterogeneous set of events spanning multiple observational classes, including the fast blue optical transients, broad-line Ic supernovae, and superluminous supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. First, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the in-cylinder turbulent flow field both spatially and temporally. Second, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (2017, "Parallel Methodology to Capture Cyclic Variability in Motored Engines,"Int. J. Engine Res., 18(4), pp. 366-377.) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter time-scale problems. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flow field was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) spark ignition (SI) engine. The predictions from this approach are also shown to be similar to the consecutive LES cycles. It is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn raterelated parameters with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.

Ameen, Muhsin M.↗
DOE OSTI2017

Genome Stability in Engineered Strains of the Extremely Thermophilic Lignocellulose-Degrading Bacterium Caldicellulosiruptor bescii

Caldicellulosiruptor bescii is the most thermophilic cellulose degrader known and is of great interest because of its ability to degrade nonpretreated plant biomass. For biotechnological applications, an efficient genetic system is required to engineer it to convert plant biomass into desired products. To date, two different genetically tractable lineages of C. bescii strains have been generated. The first (JWCB005) is based on a random deletion within the pyrimidine biosynthesis genes pyrFA , and the second (MACB1018) is based on the targeted deletion of pyrE , making use of a kanamycin resistance marker. Importantly, an active insertion element, IS Cbe4 , was discovered in C. bescii when it disrupted the gene for lactate dehydrogenase ( ldh ) in strain JWCB018, constructed in the JWCB005 background. Additional instances of IS Cbe4 movement in other strains of this lineage are presented herein. These observations raise concerns about the genetic stability of such strains and their use as metabolic engineering platforms. In order to investigate genome stability in engineered strains of C. bescii from the two lineages, genome sequencing and Southern blot analyses were performed. The evidence presented shows a dramatic increase in the number of single nucleotide polymorphisms, insertions/deletions, and IS Cbe4 elements within the genome of JWCB005, leading to massive genome rearrangements in its daughter strain, JWCB018. Such dramatic effects were not evident in the newer MACB1018 lineage, indicating that JWCB005 and its daughter strains are not suitable for metabolic engineering purposes in C. bescii . Furthermore, a facile approach for assessing genomic stability in C. bescii has been established. Caldicellulosiruptor bescii is a cellulolytic extremely thermophilic bacterium of great interest for metabolic engineering efforts geared toward lignocellulosic biofuel and bio-based chemical production. Genetic technology in C. bescii has led to the development of two uracil auxotrophic genetic background strains for metabolic engineering. We show that strains derived from the genetic background containing a random deletion in uracil biosynthesis genes ( pyrFA ) have a dramatic increase in the number of single nucleotide polymorphisms, insertions/deletions, and IS Cbe4 insertion elements in their genomes compared to the wild type. At least one daughter strain of this lineage also contains large-scale genome rearrangements that are flanked by these IS Cbe4 elements. Finally, in contrast, strains developed from the second background strain developed using a targeted deletion strategy of the uracil biosynthetic gene pyrE have a stable genome structure, making them preferable for future metabolic engineering studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Engineering Controls Database

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

Source record↗
DOE OSTI2021

NRIC Digital Engineering Ecosystem

The NRIC Digital Engineering Ecosystem is a software suite designed to eliminate procedural failures in the engineering lifecycle of complex projects. It focuses on improving the flow of scheduling data, safety information, engineering requirements, and CAD files among stakeholders of complex engineering projects. The project comprises software adapters that move engineering data from disparate software tools in and out of a central database. The ecosystem is designed to efficiently incorporate data sources from many different engineering toolsets, making it software agnostic, and adaptable to many different engineering applications.

Harris, BrennanK↗