Analysis of sprays from rocket engine injectors
Cold flow analysis of liquid propellant sprays from rocket engine injectors, relating propellant mixing and combustion performance
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Cold flow analysis of liquid propellant sprays from rocket engine injectors, relating propellant mixing and combustion performance
This viewgraph presentation gives a general overview of the design and analysis division of NASA John C. Stennis Space Center. This division develops and maintains propulsion test systems and facilities for engineering competencies.
The history of computational structural analysis is reviewed and anticipated requirements for the design, development, and support of advanced commercial engines is discussed. The present commercial engine environment is comprised primarily of turbofan engines containing monolithic titanium and nickel alloys. Future commercial engines will make extensive use of composite materials to meet demanding high temperature requirement and aggressive weight goals, since only modestly higher capabilities can be anticipated in monolithic materials. Engine configurations will include ducted and/or unducted propfans and super turbojet in addition to turbofans.
The computer program CARES/LIFE calculates the time-dependent reliability of monolithic ceramic components subjected to thermomechanical and/or proof test loading. This program is an extension of the CARES (Ceramics Analysis and Reliability Evaluation of Structures) computer program. CARES/LIFE accounts for the phenomenon of subcritical crack growth (SCG) by utilizing either the power or Paris law relations. The two-parameter Weibull cumulative distribution function is used to characterize the variation in component strength. The effects of multiaxial stresses are modeled using either the principle of independent action (PIA), the Weibull normal stress averaging method (NSA), or the Batdorf theory. Inert strength and fatigue parameters are estimated from rupture strength data of naturally flawed specimens loaded in static, dynamic, or cyclic fatigue. Two example problems demonstrating proof testing and fatigue parameter estimation are given.
The expert system described in this paper analyzes spectral emissions of rocket engine exhaust plumes and shows major promise for use in engine health diagnostics. Plume emission spectroscopy is an important tool for diagnosing engine anomalies, but it is time-consuming and requires highly skilled personnel. The expert system was created to alleviate such problems. The system accepts a spectral plot in the form of wavelength vs intensity pairs and finds the emission peaks in the spectrum, lists the elemental emitters present in the data and deduces the emitter that produced each peak. The system consists of a conventional language component and a commercially available inference engine that runs on an Apple Macintosh computer. The expert system has undergone limited preliminary testing. It detects elements well and significantly decreases analysis time.
This report presents the performance of a steady-state, dual-spool, separate-exhaust turbofan engine, with an interstage turbine burner (ITB) serving as a secondary combustor. The ITB, which is located in the transition duct between the high- and the low-pressure turbines, is a relatively new concept for increasing specific thrust and lowering pollutant emissions in modern jet-engine propulsion. A detailed off-design performance analysis of ITB engines is written in Microsoft(Registered Trademark) Excel (Redmond, Washington) macrocode with Visual Basic Application to calculate engine performances over the entire operating envelope. Several design-point engine cases are pre-selected using a parametric cycle-analysis code developed previously in Microsoft(Registered Trademark) Excel, for off-design analysis. The off-design code calculates engine performances (i.e. thrust and thrust-specific-fuel-consumption) at various flight conditions and throttle settings.
All subsurface stress measurement methods available for deep subsurface applications are indirect and involve a significant amount of uncertainty. Quantifying these uncertainties is critical for applications where the state of stress is a component of a risk analysis with significant safety, environmental, and economic considerations. A publicly available and open source tool called the State of Stress Analysis Tool (SOSAT) has been developed to aid in performing a Bayesian uncertainty quantification and geomechanical risk analysis using many types of commonly available data. A new feature of this tool is using data from the FutureGen 2.0 site. The new feature of the tool was uses information about the absence or presence of borehole breakouts to constrain the state of stress. This new feature allows the user to specify probability distributions for the relevant parameters such as the drilling mud pressure, temperature, and the formation strength properties. The tool computes the posterior joint probability distribution of the horizontal principal stresses. Using random samples from this posterior distribution the tool then computes the probability of activating a critically oriented fault at a range of pore pressures. This latter feature can be used to aid in the assessment of the risk of induced seismicity and leakage in geologic carbon sequestration and enhanced geothermal systems.
ABCC engine combines air breathing and rocket engine into a single engine to increase the specific impulse over an entire flight trajectory. Except for the heat source, the basic operation of the ABCC is similar to the basic operation of the RBCC engine. The ABCC is intended to have a higher specific impulse than the RBCC for single stage Earth to orbit vehicle. Computational fluid dynamics (CFD) is a useful tool for the analysis of complex transport processes in various components in ABCC propulsion system. The objective of the present research was to develop a transient 1-D numerical model using conservation of mass, linear momentum, and energy equations that could be used to predict flow behavior throughout a generic ABCC engine following a flight path. At specific points during the development of the 1-D numerical model a myriad of tests were performed to prove the program produced consistent, realistic numbers that follow compressible flow theory for various inlet conditions.
An RBCC engine combines air breathing and rocket engines into a single engine to increase the specific impulse over an entire flight trajectory. Considerable research pertaining to RBCC propulsion was performed during the 1960's and these engines were revisited recently as a candidate propulsion system for either a single-stage-to-orbit (SSTO) or two-stage-to-orbit (TSTO) launch vehicle. There are a variety of RBCC configurations that had been evaluated and new designs are currently under development. However, the basic configuration of all RBCC systems is built around the ejector scramjet engine originally developed for the hypersonic airplane. In this configuration, a rocket engine plays as an ejector in the air-augmented initial acceleration mode, as a fuel injector in scramjet mode and the rocket in all rocket mode for orbital insertion. Computational fluid dynamics (CFD) is a useful tool for the analysis of complex transport processes in various components in RBCC propulsion systems. The objective of the present research was to develop a transient 1-D numerical model that could be used to predict flow behavior throughout a generic RBCC engine following a flight path.
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The Space Shuttle Main Engine (SSME) is a large thrust class, reusable, staged combustion cycle rocket engine employing liquid hydrogen and liquid oxygen propellants. A cluster of three SSMEs is used on every space shuttle mission to propel the space shuttle orbiter vehicle into low earth orbit. Development of the SSME began in the early 70 s and the first flight of the space shuttle occurred in 1981. Today, the SSME has accrued over one million seconds of ground test and flight operational time, launching 129 space shuttle missions. Given that the SSME is used to launch a manned vehicle, its reliability must be commensurate for the task. At the same time, the SSME is a high performance, high power density engine which traditionally does not lend itself towards high reliability. Furthermore, throughout its history, the SSME operational envelope has been explored and expanded leading to several major test failures. Hence, assessing the reliability of the SSME throughout its history has been a challenging undertaking. This paper provides a review and discussion of SSME reliability assessment techniques and results over its history. Basic reliability drivers such as engine design, test program, major failures, redesigns and upgrades will also be discussed.
ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.
ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.
Numerical and Analytical methods developed to determine damage accumulation in specific engine components when speed variation included. Dither Life Ratio shown to be well over factor of 2 for specific example. Steady‐State assumption shown to be accurate for most turbopump cases, allowing rapid calculation of DLR. If hot‐fire speed data unknown, Monte Carlo method developed that uses speed statistics for similar engines. Application of techniques allow analyst to reduce both uncertainty and excess conservatism. High values of DLR could allow previously unacceptable part to pass HCF criteria without redesign. Given benefit and ease of implementation, recommend that any finite life turbomachine component analysis adopt these techniques. Probability Values calculated, compared, and evaluated for several industry‐proposed methods for combining random and harmonic loads. Two new excel macros written to calculate combined load for any specific probability level. Closed form Curve fits generated for widely used 3(sigma) and 2(sigma) probability levels. For design of lightweight aerospace components, obtaining accurate, reproducible, statistically meaningful answer critical.
Analysis of nuclear light bulb startup and engine dynamics including transient responses to perturbations at full power operating level
A comparison of two different concepts for the guidance, navigation and control test set signal ground system is presented. The first is a concept utilizing a ground plate to which crew station, avionics racks, electrical power distribution system, master electrical common connection assembly and marshall mated elements system grounds are connected by 4/0 welding cable. An alternate approach has an aluminum sheet interconnecting the signal ground reference points between the crew station and avionics racks. The comparison analysis quantifies the differences between the two concepts in terms of dc resistance, ac resistance and inductive reactance. These parameters are figures of merit for ground system conductors in that the system with the lowest impedance is the most effective in minimizing noise voltage. Although the welding cable system is probably adequate, the aluminum sheet system provides a higher probability of a successful system design.
Oilseeds are of great economic importance for food and animal feed and their contribution to renewable energy production. Soybean seeds (Glycine max (L.) Merr.) contain c. 40% protein, 20% oil, and 30% carbohydrate (Song et al., 2023). Due to the massive scale of soybean production worldwide, even small improvements in seed protein and oil content make economic sense (Song et al., 2023). Successful manipulation of seed composition largely depends on a thorough understanding of the processes and pathways involved in the biosynthesis of fatty acids and amino acids, which are the building blocks of lipids and proteins. Rational engineering of the synthesis of storage reserves, that is, the rerouting of metabolic flux in central metabolism, is difficult to accomplish due to the complexity of the central metabolic network, the intricate regulation of its enzymes at multiple levels, and the often-unpredictable effects of genetic manipulation (Sweetlove et al., 2017). Therefore, the advancement of our understanding of central metabolism and its control of carbon partitioning requires following an iterative ‘design–build–test–learn’ (DBTL) cycle (Lin & Eudes, 2020) where metabolic flux analysis and hypothesis testing by transgenic approaches are important components. Previous metabolic studies on soybeans using isotopic tracers and metabolic flux analysis have provided insight into how lipid and protein biosynthesis occurs simultaneously during seed development (Allen et al., 2009; Allen & Young, 2013; Kambhampati et al., 2021). In an article published in this issue of New Phytologist, Morley et al. (2023; 1834–1851) put the insights they have gained into the delivery of metabolic precursors and energy cofactors to oil synthesis to the test and arrive at a successful metabolic engineering design. They show that an increase in seed oil content in soybeans can be achieved by overexpression of malic enzyme (ME) during seed development. Malic enzyme refers to a class of decarboxylating malate dehydrogenase enzymes that oxidize malate with NAD + or NADP + as redox cofactor while generating pyruvate and CO 2 . Like higher plants in general, soybean has distinct NADH- or NADPH-producing ME isoforms localized to the cytosol, plastid, or mitochondria (Gerrard Wheeler et al., 2016). As Morley et al. show, an increase in seed oil can be achieved in particular when a NADP+-dependent enzyme isoform (EC 1.1.1.40) is overexpressed in the plastid. Given the complex compartmentalization of pyruvate, malate, and redox metabolism (Fig. 1), increased oil production appears to depend on additional pyruvate and reducing equivalents being produced in the same compartment where de novo fatty acid biosynthesis occurs: the plastid.
Cyber-informed engineering and security-by-design frameworks are important in promoting the need to identify cybersecurity concerns early in the systems engineering lifecycle so risks from adversarial cyber-attacks can be eliminated or reduced through engineering design practices. In addition to adversarial risk, risk in operational technology systems also includes non-adversarial and unintentional risk from other factors such as human performance errors, environmental conditions, design flaws, and device degradation or failure. This paper introduces a new concept for characterizing digital risk, both adversarial and non-adversarial, and provides the basis for initial research into a novel digital risk analysis approach focused on incorporating attack difficulty into a multi-attribute analysis technique using robust decision-making. This digital risk characterization is also used to frame a discussion on the challenges of competing objectives and competing stakeholder requirements in an integrated energy system project that incorporates a small modular reactor and industrial facility.