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NASA's Role in Gas Turbine Technology Development: Accelerating Technical Progress via Collaboration Between Academia, Industry, and Government Agencies
Given the maturity of the gas turbine engine since its invention and also considering the limited and flattened level of resources expected to be allocated for NASA aeronautics research and development, we ask the question are NASA technology investments still needed to enable future turbine engine-based propulsion systems? If so, what is NASA’s unique role to justify NASA’s investment? To address this topic, we will first review the accomplishments and the impact that NASA Glenn Research Center has made on turbine engine technologies over the last 78 years. Specifically, this paper discusses NASA’s role and contributions to turbine engine development, specific to both 1) NASA’s role in conducting experiments to understand flow physics and provide relevant benchmark validation experiments for Computational Fluid Dynamics (CFD) code development, validation, and assessment; and 2) the impact of technologies resulting from NASA collaborations with industry, academia, and other government agencies. Note that the scope of the discussion is limited to the NASA technology contributions with which the author was intimately associated, and does not represent the entirety of the NASA contributions to turbine engine technology. The specific research, development, and demonstrations discussed herein were selected to both 1) provide a comprehensive review and reference list of the technology and its impact, and 2) identify NASA’s unique role and highlight how NASA’s involvement resulted in additional benefit to the gas turbine engine community. Secondly, we will discuss current NASA collaborations that are in progress and provide a status of the results. Finally, we discuss the challenges anticipated for future turbine engine-based propulsion systems for civil aviation and identify potential opportunities for collaboration where NASA involvement would be beneficial. Ultimately, the gas turbine engine community will decide if NASA involvement is needed to contribute to the development of the design and analysis tools, databases, and technology demonstration programs to meet these challenges for future turbine engine-based propulsion systems.
TPSAS-NF1676L-18674-DND
Objective - Mitigate radiated airframe noise during aircraft landing - Develop effective noise reduction (NR) concepts for current and future generations of civil transport Execution Plan - Utilize flight tests, wind-tunnel experiments, and computational simulations to generate a comprehensive aerodynamic and acoustic database to: - Better understand the principles of airframe noise generation - Improve airframe noise prediction tools - Develop efficient noise reduction concepts - Evaluate the most promising noise reduction concepts in a realistic environment
TopiQAL: Topic-aware Question Answering using Scalable Domain-specific Supercomputers
We all have questions. About today's temperature, scores of our favorite baseball team, the Universe, and about vaccine for COVID-19. Life, physical, and natural scientists have been trying to find answers to various topics using scientific methods and experiments, while computer scientists have built language models as a tiny step towards automatically answering all of these questions across domains given a little bit of context. In this paper, we propose an architecture using state-of-the-art Natural Language Processing language models namely Topic Models and Bidirectional Encoder Representations from Transformers (BERT) that can transparently and automatically retrieve articles of relevance to questions across domains, and fetch answers to topical questions related to COVID-19 current and historical medical research literature. We demonstrate the benefits of using domain-specific supercomputers like Tensor Processing Units (TPUs), residing on cloud-based infrastructure, using which we could achieve significant gains in training and inference times, also with very minimal cost.
Atomic Layer Deposition (ALD) to Extend Catalyst Lifetime for Biobased Adipic Acid Production
Robust heterogeneous catalysts are essential for enabling biomass conversion; however, harsh reaction environments introduce durability challenges for many conventional catalyst materials [1]. The hydrogenation of biobased muconic acid to adipic acid is one such emerging chemistry that faces PGM catalyst stability challenges [2]. Muconic acid is a heavily-investigated biobased platform chemical that can be converted into an array of large-market commodity chemicals [2]. PGM catalysts are exceptionally effective for muconic acid hydrogenation to adipic acid, with Pd the most active to date. [2] However, Pd leaches in an acidic environment and this chemistry has a high propensity for fouling. Atomic layer deposition (ALD) is one such material design strategy that has emerged to stabilize supported metal catalysts [3]. ALD coatings are theorized to stabilize supported metal active sites by i) covering high-energy facets most susceptible to degradation, ii) disrupting the physical mobility of active sites, and iii) reinforcing the structure of the underlying catalyst support [3]. However, ALD coatings for catalyst durability with carboxylic acids remains an underdeveloped area of research and literature reports have yet to consider the techno-economic tradeoffs between the ALD manufacturing cost and catalyst lifetime productivity. This study examines low-cycle Al2O3 ALD coatings to stabilize Pd/TiO2 against deactivation during muconic acid hydrogenation. The unique harshness of muconic acid for Pd leaching was evaluated by both experiment and computation. Based on batch reactor screening results, uncoated and ALD coated catalysts were evaluated in a continuous flow reactor for their productivity, stability, and post-reaction regenerability at 700 degrees C. Characterization was performed to assess the impact of ALD coatings on catalyst morphology, as well as following regeneration. Finally, techno-economic analysis models evaluated the value proposition for ALD-coated catalysts within an nth-generation adipic acid biorefinery.
AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations
Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.
Automated quantum error mitigation based on probabilistic error reduction
Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.
Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)
Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.
Nuclear Science & Technology March 2023 Highlights
Highlights from the Nuclear Science and Technology directorate for the month of March 2023.
Phenomena Identification and Ranking Table (PIRT) for Heat Pipes
This Phenomena Identification and Ranking Table (PIRT) report provides an evaluation of key phenomena affecting the performance and operational regimes of heat pipes, particularly in the context of heat pipe microreactors (HPMRs). Heat pipes are advanced passive thermal management devices that utilize phase change and capillary action to achieve efficient heat transfer. However, due to the complexity of the phenomena coupled in the heat pipe, including phase change, turbulent transition, and compressibility effects, among others, there is high uncertainty in identifying and ranking the important phenomena affecting the operation of heat pipes and the current knowledge for their modeling and simulation and experimental measurements and instrumentation. This PIRT exercise, conducted as a collaborative effort involving the Department of Energy (DOE) Microreactor Program (MRP), the Nuclear Regulatory Commission (NRC), and university partners systematically identifies, reviews, and prioritizes critical phenomena affecting the operation of heat pipes based on their importance and knowledge levels. The report analyzes phenomena with high importance and low knowledge, such as wick de-wetting, critical heat flux, contact angles, and pressure dynamics, discussing challenges and future research directions for improving their modeling and simulation and experimental measurements. Additionally, the report addresses phenomena with low knowledge that could impact heat pipe operation during non-normal or transient operation, including frozen startup, laminar to turbulent transition, geysering, wick priming, underfilling conditions, surface roughness of the wick, NCGs trapped in the wick, and the timescales of startup and shutdown. This comprehensive evaluation serves as a valuable resource for guiding future research and development efforts, supporting the successful integration of heat pipes into critical applications such as nuclear reactors, and contributing to the advancement of heat pipe technologies in safety-critical industries.
Saturn in-flight experimental payload study. Volume II - Design of in-flight experiments Technical report, Jul. 1965 - Feb. 1966
Computer program for evaluating space experiments for Saturn in-flight payloads - experiment design
Projected role of advanced computational aerodynamic methods at the Lockheed-Georgia company
Experience with advanced computational methods being used at the Lockheed-Georgia Company to aid in the evaluation and design of new and modified aircraft indicates that large and specialized computers will be needed to make advanced three-dimensional viscous aerodynamic computations practical. The Numerical Aerodynamic Simulation Facility should be used to provide a tool for designing better aerospace vehicles while at the same time reducing development costs by performing computations using Navier-Stokes equations solution algorithms and permitting less sophisticated but nevertheless complex calculations to be made efficiently. Configuration definition procedures and data output formats can probably best be defined in cooperation with industry, therefore, the computer should handle many remote terminals efficiently. The capability of transferring data to and from other computers needs to be provided. Because of the significant amount of input and output associated with 3-D viscous flow calculations and because of the exceedingly fast computation speed envisioned for the computer, special attention should be paid to providing rapid, diversified, and efficient input and output.
Gravitational experiments with a cylindrical galaxy.
Collisionless computer model performing gravitational experiments with two dimensional galaxy
Automatic computer controlled testing of spacecraft and experiments
Autotest is a flexible, fully automatic, programmable test system. It is externally controlled by a mnemonic test language. Once started, tests can run to completion without further manual intervention. Autotest can be operated manually as well as in automatic mode. Autotest can be adapted to a wide variety of test requirements. Though originally designed to test the Atmosphere Explorer (AE) spacecraft and experiments, autotest can easily be modified to test any electronic device suitable for automated computer testing.
Validation of Two-phase Flow Modeling Techniques in Loci-Stream using Axial Jet Pressure Control and Autogenous Pressurization Experiment
Improvements to computational fluid dynamics modeling of cryogenic fluid management operations are presented with experimental validations. Modeling improvements includes utilizing improved delayed detaches eddy simulation to more effectively model turbulence compared to the Reynolds-averaged Navier Stokes modeling, modeling temperature dependent tank solid properties to capture large variation in thermodynamic properties at cryogenic conditions, modeling real gas equation of state for hydrogen instead of ideal gas behavior, and an on-going effort to improve interface turbulence transport for the volume of fluid method. Two flight-scale ground tests, five K-site tank axial jet pressure control experiments and an Engineering Design Unit tank autogenous pressurization experiment are validated and show the effectiveness of the improved modeling techniques. The successful validation establishes further trust in these models for flight project support and provides a pathway for maturation of in-development interface turbulence damping model for the volume of fluid method for more generalized applications.
Validation of Two-Phase Flow Modeling Techniques in Loci-Stream Using Axial Jet Pressure Control and Autogenous Pressurization Experiments
Improvements to computational fluid dynamics modeling of cryogenic fluid management operations are presented with experimental validations. Modeling improvements includes utilizing improved delayed detaches eddy simulation to more effectively model turbulence compared to the Reynolds-averaged Navier Stokes modeling, modeling temperature dependent tank solid properties to capture large variation in thermodynamic properties at cryogenic conditions, modeling real gas equation of state for hydrogen instead of ideal gas behavior, and an on-going effort to improve interface turbulence transport for the volume of fluid method. Two flight-scale ground tests, five K-site tank axial jet pressure control experiments and an Engineering Design Unit tank autogenous pressurization experiment are validated and show the effectiveness of the improved modeling techniques. The successful validation establishes further trust in these models for flight project support and provides a pathway for maturation of in-development interface turbulence damping model for the volume of fluid method for more generalized applications.
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.
Selected Uses of TSUNAMI in Critical Experiment Design and Analysis
Validation in criticality safety is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 and enable investigation of the parameters at which such a critical condition is achieved. For the critical experiments used in a validation to capture the biases of the materials and neutron energy spectra of interest, those materials must be included in the experiment such that they influence k eff or another observable parameter with statistical significance. This paper discusses the use of sensitivity uncertainty (S/U) methods to develop critical experiments for various purposes. S/U techniques are useful for understanding the underlying components of nuclear data which affect the k eff or another parameter of a given configuration. S/U calculations are most commonly used to compare existing experiments to applications of interest; however, S/U techniques can also be used to identify, optimize, or assess features of proposed experiments so that they can better test specific portions of nuclear data or match an application of interest. The S/U techniques discussed here are from the TSUNAMI code system. The two primary codes discussed in this work are TSUNAMI-3D, which implements the KENO criticality code to calculate the sensitivity of k eff to nuclear data, and TSAR, which calculates the sensitivity of a reactivity difference between two configurations based on their TSUNAMI-3D generated sensitivity profiles. The methods used in these tools are discussed in more detail in the SCALE manual. This paper is one of a series on the development and use of TSUNAMI tools. The other papers address development of TSUNAMI methods and a review of TSUNAMI applications.