Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “knowledge engineering”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantized Electronic Doping towards Atomically Controlled “Charge-Engineered” Semiconductor Nanocrystals

“Charge engineering” of semiconductor nanocrystals (NCs) through so-called electronic impurity doping is a long-standing challenge in colloidal chemistry and holds promise for ground-breaking advancements in many optoelectronic, photonic, and spin-based nanotechnologies. To date, our knowledge is limited to a few paradigmatic studies on a small number of model compounds and doping conditions, with important electronic dopants still unexplored in nanoscale systems. Equally importantly, fine-tuning of charge engineered NCs is hampered by the statistical limitations of traditional approaches. The resulting intrinsic doping inhomogeneity restricts fundamental studies to statistically averaged behaviors and complicates the realization of advanced device concepts based on their advantageous functionalities. We aim to address these issues by realizing the first example of II–VI NCs electronically doped with an exact number of heterovalent gold atoms, a known p-type acceptor impurity in bulk chalcogenides. Single-dopant accuracy across entire NC ensembles is obtained through a novel non-injection synthesis employing ligand-exchanged gold clusters as “quantized” dopant sources to seed the nucleation of CdSe NCs in organic media. Structural, spectroscopic, and magneto-optical investigations trace a comprehensive picture of the physical processes resulting from the exact doping level of the NCs. Gold atoms, doped here for the first time into II–VI NCs, are found to incorporate as nonmagnetic Au + species activating intense size-tunable intragap photoluminescence and artificially offsetting the hole occupancy of valence band states. Fundamentally, the transient conversion of Au + to paramagnetic Au 2+ (5d 9 configuration) under optical excitation results in strong photoinduced magnetism and diluted magnetic semiconductor behavior revealing the contribution of individual paramagnetic impurities to the macroscopic magnetism of the NCs. Altogether, our results demonstrate a new chemical approach toward NCs with physical functionalities tailored to the single impurity level and offer a versatile platform for future investigations and device exploitation of individual and collective impurity processes in quantum confined structures.

36 MATERIALS SCIENCE↗
DOE OSTI2013

National Geothermal Data System: Transforming the Discovery, Access, and Analytics of Data for Geothermal Exploration

Compendium of Papers from the 38th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California February 11-13, 2013 The National Geothermal Data System (NGDS) is a distributed, interoperable network of data collected from state geological surveys across all fifty states and the nation’s leading academic geothermal centers. The system serves as a platform for sharing consistent, reliable, geothermal-relevant technical data with users of all types, while supplying tools relevant for their work. As aggregated data supports new scientific findings, this content-rich linked data ultimately broadens the pool of knowledge available to promote discovery and development of commercial-scale geothermal energy production. Most of the up-front risks associated with geothermal development stem from exploration and characterization of subsurface resources. Wider access to distributed data will, therefore, result in lower costs for geothermal development. NGDS is on track to become fully operational by 2014 and will provide a platform for custom applications for accessing geothermal relevant data in the U.S. and abroad. It is being built on the U.S. Geoscience Information Network (USGIN) data integration framework to promote interoperability across the Earth sciences community. The basic structure of the NGDS employs state-of-the art informatics to advance geothermal knowledge. The following four papers comprising this Open-File Report are a compendium of presentations, from the 38th Annual Workshop on Geothermal Reservoir Engineering, taking place February 11-13, 2013 at Stanford University, Stanford, California. “NGDS Geothermal Data Domain: Assessment of Geothermal Community Data Needs,” outlines the efforts of a set of nationwide data providers to supply data for the NGDS. In particular, data acquisition, delivery, and methodology are discussed. The paper addresses the various types of data and metadata required and why simple links to existing data are insufficient for promoting geothermal exploration. Authors of this paper are Arlene Anderson, US DOE Geothermal Technologies Office, David Blackwell, Southern Methodist University (SMU), Cathy Chickering (SMU), Toni Boyd, Oregon Institute of Technology’s GeoHeat Center, Roland Horne, Stanford University, Matthew MacKenzie, Uberity, Joe Moore, University of Utah, Duane Nickull, Uberity, Stephen Richard, Arizona Geological Survey, and Lisa Shevenell, University of Nevada, Reno. “NGDS User Centered Design: Meeting the Needs of the Geothermal Community,” discusses the user- centered design approach taken in the development of a user interface solution for the NGDS. The development process is research based, highly collaborative, and incorporates state-of-the-art practices to ensure a quality user interface for the widest and greatest utility. Authors of this paper are Harold Blackman, Boise State University, Suzanne Boyd, Anthro-Tech, Kim Patten, Arizona Geological Survey, and Sam Zheng, Siemens Corporate Research. “Fueling Innovation and Adoption by Sharing Data on the DOE Geothermal Data Repository Node on the National Geothermal Data System,” describes the motivation behind the development of the Geothermal Data Repository (GDR) and its role in the NGDS. This includes the benefits of using the GDR to share geothermal data of all types and DOE’s data submission process. Authors of this paper are Jon Weers, National Renewable Energy Laboratory and Arlene Anderson, US DOE Geothermal Technologies Office. Finally, “Developing the NGDS Adoption of CKAN for Domestic & International Data Deployment,” provides an overview of the “Node-In-A-Box” software package designed to provide data consumers with a highly functional interface to access the system, and to ease the burden on data providers who wish to publish data in the system. It is important to note that this software package constitutes a reference implementation and that the NGDS architecture is based on open standards, which means other server software can make resources available, and other client applications can utilize NGDS data. Authors of this paper are Ryan Clark, Arizona Geological Survey (AZGS), Christoph Kuhmuench, Siemens Corporate Research, and Stephen Richard, AZGS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Tethys Knowledge Management System: Working to Advance the Marine Renewable Energy Industry

Purpose: This paper discusses an ongoing process to assist with the advancement of worldwide understanding of the environmental effects of marine renewable energy (MRE) through a public, online knowledge management system known as Tethys (https://tethys.pnnl.gov). Understanding potential environmental impacts can reduce expensive monitoring requirements and promote growth of this emerging industry. Approach: Tethys acts as the central repository for information in a landscape where literature and knowledge are scattered and difficult to find, providing access for an international audience of stakeholders and the public. An intuitive and flexible design has been developed over 8 years of operation, pairing content curation with synthesis and engagement to enhance the impact of knowledge management. Findings: Tethys has become internationally recognized and trusted as an honest broker of information. A community of participants that use Tethys resources and contributes content has grown to almost 50,000 people around the world, annually. Engineering and siting challenges continue to stall the deployment and establishment of the MRE industry, but progress has been made towards retiring environmental risks and facilitating progression in deploying and operating devices. Practical Implications: The need for knowledge management is particularly important when information is scattered, as is often the case with emerging industries. Tethys serves as an example of intentional design and intuitive organization for knowledge management. Value of Paper: This paper discusses focused knowledge management methods with international cooperation and public engagement to advance an emerging technical industry.

Whiting, Jonathan M.↗
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗
NASA NTRS1990

A flight expert system for on-board fault monitoring and diagnosis

An architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing, and recovering from inflight faults is described. A prototype was implemented and an attempt was made to automate the knowledge acquisition process by employing a learning by being told methodology. The scope of acquired knowledge ranges from domain knowledge, including the information about objects and their relationships, to the procedural knowledge associated with the functionality of the mechanisms. AKAS (automatic knowledge acquisition system) is the constructed prototype for demonstration proof of concept, in which the expert directly interfaces with the knowledge acquisition system to ultimately construct the knowledge base for the particular application. The expert talks directly to the system using a natural language restricted only by the extent of the definitions in an analyzer dictionary, i.e., the interface understands a subset of concepts related to a given domain. In this case, the domain is the electrical system of the Boeing 737. Efforts were made to define and employ heuristics as well as algorithmic rules to conceptualize data produced by normal and faulty jet engine behavior examples. These rules were employed in developing the machine learning system (MLS). The input to MLS is examples which contain data of normal and faulty engine behavior and which are obtained from an engine simulation program. MLS first transforms the data into discrete selectors. Partial descriptions formed by those selectors are then generalized or specialized to generate concept descriptions about faults. The concepts are represented in the form of characteristic and discriminant descriptions, which are stored in the knowledge base and are employed to diagnose faults. MLS was successfully tested on jet engine examples.

Ali, Moonis↗
NASA NTRS1990

A knowledge-based approach to configuration layout, justification, and documentation

The design, development, and implementation is described of a prototype expert system which could aid designers and system engineers in the placement of racks aboard modules on Space Station Freedom. This type of problem is relevant to any program with multiple constraints and requirements demanding solutions which minimize usage of limited resources. This process is generally performed by a single, highly experienced engineer who integrates all the diverse mission requirements and limitations, and develops an overall technical solution which meets program and system requirements with minimal cost, weight, volume, power, etc. This system architect performs an intellectual integration process in which the underlying design rationale is often not fully documented. This is a situation which lends itself to an expert system solution for enhanced consistency, thoroughness, documentation, and change assessment capabilities.

Craig, F. G.↗
DOE OSTI2026

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗
DOE OSTI2023

Decentralized digital twins of complex dynamical systems

Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Abstract Machine Models and Proxy Architectures for Exascale Computing

To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving application codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Machine Learning Techniques in Optimal Design

Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution to the problem, is then obtained by solving in parallel each of the sub-problems in the set and computing the one with the minimum cost. In addition to speeding up the optimization process, our use of learning methods also relieves the expert from the burden of identifying rules that exactly pinpoint optimal candidate sub-problems. In real engineering tasks it is usually too costly to the engineers to derive such rules. Therefore, this paper also contributes to a further step towards the solution of the knowledge acquisition bottleneck [Feigenbaum, 1977] which has somewhat impaired the construction of rulebased expert systems.

Cerbone, Giuseppe↗
NASA NTRS1999

An EVA Suit Fatigue, Strength, and Reach Model

The number of Extra-Vehicular Activities (EVAs) performed will increase dramatically with the upcoming Space Station assembly missions. It is estimated that up to 900 EVA hours may be required to assemble the Space Station with an additional 200 hours per year for maintenance requirements. Efficient modeling tools will be essential to assist in planning these EVAS. Important components include strength and fatigue parameters, multi-body dynamics and kinematics. This project is focused on building a model of the EVA crew member encompassing all these capabilities. Phase 1, which is currently underway, involves collecting EMU suited and unsuited fatigue, strength and range of motion data, for all major joints of the body. Phase 2 involves processing the data for model input, formulating comparisons between the EMU suits and deriving generalized relationships between suited and unsuited data. Phase 3 will be formulation of a multi-body dynamics model of the EMU capable of predicting mass handling properties and integration of empirical data into the model. Phase 4 will be validation of the model with collected EMU data from the Neutral Buoyancy Laboratory at NASA/JSC. Engineers and designers will use tie EVA suit database to better understand the capabilities of the suited individuals. This knowledge will lead to better design of tools and planned operations. Mission planners can use the modeling system and view the animations and the visualizations of the various parameters, such as overall fatigue, motion, timelines, reach, and strength to streamline the timing, duration, task arrangement, personnel and overall efficiency of the EVA tasks. Suit designers can use quantifiable data at common biomechanical structure points to better analyze and compare suit performance.

Maida, James C.↗
NASA NTRS1998

Advanced Methods for Acoustic and Thrust Benefits for Aircraft Engine Nozzle

The purpose of this paper is to address the problems of Aircraft Engine Technology, Airframe Technology, and Rotorcraft Technology. In addition, several applied problems for domestic industry are also studied using knowledge and experience from Aerospace Sciences. The reduction of aircraft noise is a significant driver in the success of the NASA AST and HSR programs as they attempt to meet stringent international environment regulations on noise for commercial aircraft. In accordance with the project fulfillment under this NASA grant the (Fluid Mechanics and Acoustics Laboratory) FM&AL investigates novel and promising concepts for reduction of noise and improvement of propulsion efficiency in jet exhaust nozzles and fans.

Gilinsky, Mikhail M.↗
NASA NTRS2003

Internal Flow Thermal/Fluid Modeling of STS-107 Port Wing in Support of the Columbia Accident Investigation Board

As part of the aero-thermodynamics team supporting the Columbia Accident Investigation Board (CAB), the Marshall Space Flight Center was asked to perform engineering analyses of internal flows in the port wing. The aero-thermodynamics team was split into internal flow and external flow teams with the support being divided between shorter timeframe engineering methods and more complex computational fluid dynamics. In order to gain a rough order of magnitude type of knowledge of the internal flow in the port wing for various breach locations and sizes (as theorized by the CAB to have caused the Columbia re-entry failure), a bulk venting model was required to input boundary flow rates and pressures to the computational fluid dynamics (CFD) analyses. This paper summarizes the modeling that was done by MSFC in Thermal Desktop. A venting model of the entire Orbiter was constructed in FloCAD based on Rockwell International s flight substantiation analyses and the STS-107 reentry trajectory. Chemical equilibrium air thermodynamic properties were generated for SINDA/FLUINT s fluid property routines from a code provided by Langley Research Center. In parallel, a simplified thermal mathematical model of the port wing, including the Thermal Protection System (TPS), was based on more detailed Shuttle re-entry modeling previously done by the Dryden Flight Research Center. Once the venting model was coupled with the thermal model of the wing structure with chemical equilibrium air properties, various breach scenarios were assessed in support of the aero-thermodynamics team. The construction of the coupled model and results are presented herein.

Sharp, John R.↗
NASA NTRS2002

NASA food systems: past, present, and future

The development of space food has been evolving since the Soviet cosmonaut, German Titov, became the first human to eat in space in August 1961. John Glenn was the first American to consume food, applesauce, on the third manned Mercury mission in August 1962. Before these events, there was no knowledge that humans would be able to swallow and, hence, eat in weightlessness. Space food development began with highly engineered foods that met rigid requirements imposed by spacecraft design and short mission durations. Improvements in the habitability of the spacecraft have permitted improvements in the quality of space food. As the missions became longer, the need for better nutrition, more variety, and easily consumable foods also became more important. Currently, the International Space Station astronauts have a wide variety of foods. The goal is to provide acceptable foods that taste similar to foods we eat here on Earth. Extended planetary stays will require even more variety and more technologic advances. Plants will be grown to recycle the air and water and will provide food for the crew. These harvested crops will need to be processed into safe, healthy, and acceptable food ingredients that can then be prepared into menu items.

short duration↗
NASA NTRS2007

Integration and Testing Challenges of Small, Multiple Satellite Missions: Experiences from the Space Technology 5 Project

The ST5 payload, part of NASA s New Millennium Program headquartered at JPL, consisted of three micro satellites (approx. 30 kg each) deployed into orbit from the Pegasus XL launch. ST5 was a technology demonstration mission, intended to test new technologies for potential use for future missions. In order to meet the launch date schedule of ST 5, a different approach was required rather than the standard I&T approach used for single, room-sized satellites. The I&T phase was planned for spacecraft #1 to undergo integration and test first, followed by spacecraft #2 and #3 in tandem. A team of engineers and technicians planned and executed the integration of all three spacecraft emphasizing versatility and commonality. They increased their knowledge and efficiency through spacecraft #1 integration and testing and utilized their experience and knowledge to safely execute I&T for spacecraft #2 and #3. Each integration team member could perform many different roles and functions and thus better support activities on any of the three spacecraft. The I&T campaign was completed with STS s successful launch on March 22,2006

Sauerwein, Timothy A.↗
NASA NTRS2008

Software Analyzes Complex Systems in Real Time

Expert system software programs, also known as knowledge-based systems, are computer programs that emulate the knowledge and analytical skills of one or more human experts, related to a specific subject. SHINE (Spacecraft Health Inference Engine) is one such program, a software inference engine (expert system) designed by NASA for the purpose of monitoring, analyzing, and diagnosing both real-time and non-real-time systems. It was developed to meet many of the Agency s demanding and rigorous artificial intelligence goals for current and future needs. NASA developed the sophisticated and reusable software based on the experience and requirements of its Jet Propulsion Laboratory s (JPL) Artificial Intelligence Research Group in developing expert systems for space flight operations specifically, the diagnosis of spacecraft health. It was designed to be efficient enough to operate in demanding real time and in limited hardware environments, and to be utilized by non-expert systems applications written in conventional programming languages. The technology is currently used in several ongoing NASA applications, including the Mars Exploration Rovers and the Spacecraft Health Automatic Reasoning Pilot (SHARP) program for the diagnosis of telecommunication anomalies during the Neptune Voyager Encounter. It is also finding applications outside of the Space Agency.

Source record↗
NASA NTRS2007

TMS for Instantiating a Knowledge Base With Incomplete Data

A computer program that belongs to the class known among software experts as output truth-maintenance-systems (output TMSs) has been devised as one of a number of software tools for reducing the size of the knowledge base that must be searched during execution of artificial- intelligence software of the rule-based inference-engine type in a case in which data are missing. This program determines whether the consequences of activation of two or more rules can be combined without causing a logical inconsistency. For example, in a case involving hypothetical scenarios that could lead to turning a given device on or off, the program determines whether a scenario involving a given combination of rules could lead to turning the device both on and off at the same time, in which case that combination of rules would not be included in the scenario.

James, Mark↗
NASA NTRS2014

The Enhanced Injection and Mixing Project at NASA Langley

An overview of a research project aimed at enhancing fuel injection and mixing for scramjet engines at flight Mach numbers greater than 8 is presented. The specific objectives of this project are to increase knowledge and understanding of the fundamental physics governing scramjet fuel-air mixing, to develop strategies for improving injector performance and to develop functional relationships between mixing efficiency, losses due to mixing (such as total pressure loss, drag) and combustor/mixing length. The research approach consists of a closely coupled experimental and computational effort with experiments intended for exploring injection concepts and for anchoring computational fluid dynamics (CFD) simulations. The experiments are being conducted in the NASA Langley Arc-Heated Scramjet Test Facility and consist of helium injection into a Mach 6 air stream. Measurements include in-stream helium mole fraction, Pitot pressure, and total temperature as well as wall pressures and temperatures. Additionally, nitric oxide planar laser-induced fluorescence (NO PLIF) imaging will be used for flow visualization of the fuel plume. The numerical simulations, performed with the VULCAN CFD code, provide flowfield details unobtainable from the experiment. The current paper describes the experimental apparatus and diagnostic systems which have been developed and/or tested in preparation for the mixing tests and discusses numerical simulations performed to reduce the risk associated with the experimental design.

K F Cabell↗