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NASA NTRS2012

Controllable Solid Propulsion Combustion and Acoustic Knowledge Base Improvements

Controllable solid propulsion systems have distinctive combustion and acoustic environments that require enhanced testing and analysis techniques to progress this new technology from development to production. In a hot gas valve actuating system, the movement of the pintle through the hot gas exhibits complex acoustic disturbances and flow characteristics that can amplify induced pressure loads that can damage or detonate the rocket motor. The geometry of a controllable solid propulsion gas chamber can set up unique unsteady flow which can feed acoustic oscillations patterns that require characterization. Research in this area aids in the understanding of how best to design, test, and analyze future controllable solid rocket motors using the lessons learned from past government programs as well as university research and testing. This survey paper will give the reader a better understanding of the potentially amplifying affects propagated by a controllable solid rocket motor system and the knowledge of the tools current available to address these acoustic disturbances in a preliminary design. Finally the paper will supply lessons learned from past experiences which will allow the reader to come away with understanding of what steps need to be taken when developing a controllable solid rocket propulsion system. The focus of this survey will be on testing and analysis work published by solid rocket programs and from combustion and acoustic books, conference papers, journal articles, and additionally from subject matter experts dealing currently with controllable solid rocket acoustic analysis.

McCauley, Rachel↗

NASA NTRS2013

Combined Experimental and Numerical Simulations of Thermal Barrier Coated Turbine Blades Erosion

A combined experimental and computational study was conducted to investigate the erosion of thermal barrier coated (TBC) blade surfaces by alumina particles ingestion in a single stage turbine. In the experimental investigation, tests of particle surface interactions were performed in specially designed tunnels to determine the erosion rates and particle restitution characteristics under different impact conditions. The experimental results show that the erosion rates increase with increased impingement angle, impact velocity and temperature. In the computational simulations, an Euler-Lagrangian two stage approach is used in obtaining numerical solutions to the three-dimensional compressible Reynolds Averaged Navier-Stokes equations and the particles equations of motion in each blade passage reference frame. User defined functions (UDF) were developed to represent experimentally-based correlations for particle surface interaction models which were employed in the three-dimensional particle trajectory simulations to determine the particle rebound characteristics after each surface impact. The experimentally based erosion UDF model was used to predict the TBC erosion rates on the turbine blade surfaces based on the computed statistical data of the particles impact locations, velocities and angles relative to the blade surface. Computational results are presented for the predicted TBC blade erosion in a single stage commercial APU turbine, for a NASA designed automotive turbine, and for the NASA turbine scaled for modern rotorcraft operating conditions. The erosion patterns in the turbines are discussed for uniform particle ingestion and for particle ingestion concentrated in the inner and outer 5 percent of the stator blade span representing the flow cooling the combustor liner.

Hamed, Awate↗

DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Processing Methodology for Double Sensor Probe in a Bubbly Flow

Bubbly flow phenomenon is the most common flow pattern in nuclear plant system. In this paper, according to Hibiki and Bartel, the experiment was designed to observe the fluid conditions surrounding nuclear rod. In recent years, double sensor probe is one of the measuring techniques for the bubbly flow. It can measure local void fraction and bubble velocity. After mathematical statistical calculation, interfacial area concentration is obtained by Kataoka. The double sensor probe method, with the advantage for measuring local interfacial area concentration, has become widely used in many measurement methods. The paper of Liu and Zhao were referred to establish the post-processing for the raw data obtained from measuring instrument. After drawing the signal of the probe, it is important to analyze the raw data for the elimination of unnecessary noise signal and highlight the characteristic signal. Based on the same experimental condition, the different filtering methods, Simple Moving Average (SMA) and Gaussian average, were compared and discussed by the double sensor probe.

42 ENGINEERING↗

DOE OSTI2024

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗

DOE OSTI2016

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. In addition, the dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (N mass ), mass-based carbon concentration (C mass ), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R 2 = 0.6–0.8 for temporal variability; R 2 = 0.3–0.7 for cross-site variability; R 2 = 0.4–0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of N mass , C mass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. In conclusion, our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1988

An interactive testbed for development of expert tools for pattern recognition

The initial implementation of an interactive testbed for development of expert system applications in image processing, i.e., a toolbox of procedures designed to facilitate the capture of expert knowledge for region grouping and analysis is described. The user can elect to interactively enter commands (via a command interpreter) for region manipulation to, in effect, simulate the actions of a hypothetical expert system. The user can then incorporate any rules and procedures as derived from interactive experimentation into customized region processing procedures using the library of utility functions. An iterative technique based on image pyramids is used to compute the initial region segmentation without the use of process parameters. These regions can then be interactively examined and manipulated using the command interpreter.

Wharton, Stephen W.↗

DOE OSTI2021

Designing Anion-Exchange Ionomers with Oriented Nanoscale Phase Separation at a Silver Interface

We report performance of polymer electrolyte-based energy systems is significantly impacted by transport within the electrode catalyst layer, where ionomer thin films coat catalyst particles. Proton exchange ionomer thin films have been thoroughly characterized, but few studies have critically examined anion-exchange ionomer (AEI) thin films. Further, none have reported nanoscale phase separation for hydrocarbon AEIs, which is critical to mitigate transport resistances. In this work, a set of hydrocarbon-based AEIs with nanoscale phase separation are developed from tunable block copolymer systems composed of polyisoprene (PIp) and polychloromethylstyrene (PCMS). The effect of the PIp/PCMS ratio, architecture, and thickness on the thin-film morphology of the neutral block copolymer precursors on silicon and silver substrates is investigated using grazing-incidence small-angle X-ray scattering (GISAXS) and atomic force microscopy (AFM). AEIs are prepared by quaternizing with trimethylamine or methylpiperidine and their cation-dependent morphology is characterized at 60 °C and 95% RH. A perpendicularly aligned morphology is observed on silver, while no phase separation is observed on silicon, indicating that silver-polymer interfacial interactions drive phase separation. After quaternization, dipole-dipole interactions induce some disorder, but nanoscale phase separation is still maintained. GISAXS patterns are modeled using a Unified Fit approach to understand water uptake and swelling, and recommendations for AEI design are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

Investigation of the 2016 Eurasia heat wave as an event of the recent warming

This study investigates the physical mechanisms that contributed to the 2016 Eurasian heat wave during boreal summer season (July–August, JA), characterized by much higher than normal temperatures over eastern Europe, East Asia, and the Kamchatka Peninsula. It is found that the 2016 JA mean surface air temperature, upper-tropospheric height, and soil moisture anomalies are characterized by a tri-pole pattern over the Eurasia continent and a wave train-like structure not dissimilar to recent (1980–2016) trends in those quantities. A series of forecast experiments designed to isolate the impacts of the land, ocean, and sea ice conditions on the development of the heat wave is carried out with the Global Seasonal Forecast System version 5. The results suggest that the tri-pole blocking pattern over Eurasia, which appears to be instrumental in the development of the 2016 summer heat wave, can be viewed as an expression of the recent trends, amplified by record-breaking oceanic warming and internal land-atmosphere interactions.

Eunkyo Seo↗

NASA NTRS2011

Experimental Measurement of Transonic Fan Wake Response to Uniform and Simulated Boundary Layer Ingesting Inlet Flows

BWB Aircraft with embedded engines and BLI inlets offer attractive advantages in terms of reduced noise from engines and increased range and fuel economy. The BLI inlet produces inlet distortion patterns that can reduce fan performance and stall margin, and can produce undesirable forced responses. Knowledge of the dynamic response of fan flow when subjected to flow distortions of the type produced by BLI inlets is important for the design of distortion tolerant fans. This project is investigating fan response to flow distortion by measuring the response of the fan of a JT15D engine to a flow pattern following the results of the NASA Inlet A BLI wind tunnel tests.

O'Brien, Walter F.↗

NASA NTRS1988

Fingertip-shaped optical tactile sensor for robotic applications

Progress is described regarding the development of a high-density, fiber-optic, fingertip-shaped tactile sensor specifically designed for application to dexterous robotics. The sensor operates on optical principles involving the frustration of total internal reflection at a waveguide/elastomer interface and generates a grey-scale tactile image that represents the normal forces of contact. The sensor contains 256 taxels (sensing sites) distributed in a dual-density pattern that includes a tactile fovea near the tip which measures 13 mm x 13 mm and contains 169 taxels. The details regarding the design and construction of this tactile sensor are presented, in addition to photographs of tactile imprints.

Begej, Stefan↗

NASA NTRS2016

Strategic Design of Long-Haul and Oceanic Aircraft Trajectories in Aviation Operations

Long-Haul Aircraft consume most of their fuel during the cruise phase of flight. The inefficiency in cruise flights compared to efficient routes varies is around 3 to 4. The efficiency of oceanic flights is low due to limited navigational and communication equipment, congestion and airspace restrictions. The availability of Automated Dependent Surveillance-Broadcast (ADS-B) and other improvements provides opportunity for better strategic planning of trajectories. Transatlantic flights between US and Europe constitute one of the busiest oceanic airspace regions in the world. This talk examines the benefits of a wind-optimal trajectory concept with a strategic de-confliction component compared to the current flight planning using the North Atlantic Tracks. The analysis is based on air traffic between US and Europe during July 2012. The potential fuel savings are in the range of (420-970) kg per flight for a Boeing 767-300, the most widely used aircraft between the city-pairs in this study. The talk also describes a global simulation of aviation operations combining flight plans and real air traffic data with historical commercial city-pair aircraft type and schedule data and global atmospheric data. The resulting capability extends the simulation and optimization functions of NASAs Future Air Traffic Management Concept Evaluation Tool (FACET) to global scale. This new capability is used to characterize the evolution of global air traffic, analyze fuel savings and seasonal variations in the long-haul wind-optimal traffic patterns in six major regions of the world.

strategic trajectory design↗

NASA NTRS2015

R2U2: Monitoring and Diagnosis of Security Threats for Unmanned Aerial Systems

We present R2U2, a novel framework for runtime monitoring of security properties and diagnosing of security threats on-board Unmanned Aerial Systems (UAS). R2U2, implemented in FPGA hardware, is a real-time, REALIZABLE, RESPONSIVE, UNOBTRUSIVE Unit for security threat detection. R2U2 is designed to continuously monitor inputs from the GPS and the ground control station, sensor readings, actuator outputs, and flight software status. By simultaneously monitoring and performing statistical reasoning, attack patterns and post-attack discrepancies in the UAS behavior can be detected. R2U2 uses runtime observer pairs for linear and metric temporal logics for property monitoring and Bayesian networks for diagnosis of security threats. We discuss the design and implementation that now enables R2U2 to handle security threats and present simulation results of several attack scenarios on the NASA DragonEye UAS.

Formal Methods↗

NASA NTRS1991

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Prasanth, Ravi K.↗

NASA NTRS2011

Infrared-Bolometer Arrays with Reflective Backshorts

Integrated circuits that incorporate square arrays of superconducting-transition- edge bolometers with optically reflective backshorts are being developed for use in image sensors in the spectral range from far infrared to millimeter wavelengths. To maximize the optical efficiency (and, thus, sensitivity) of such a sensor at a specific wavelength, resonant optical structures are created by placing the backshorts at a quarter wavelength behind the bolometer plane. The bolometer and backshort arrays are fabricated separately, then integrated to form a single unit denoted a backshort-under-grid (BUG) bolometer array. In a subsequent fabrication step, the BUG bolometer array is connected, by use of single-sided indium bump bonding, to a readout device that comprises mostly a superconducting quantum interference device (SQUID) multiplexer circuit. The resulting sensor unit comprising the BUG bolometer array and the readout device is operated at a temperature below 1 K. The concept of increasing optical efficiency by use of backshorts at a quarter wavelength behind the bolometers is not new. Instead, the novelty of the present development lies mainly in several features of the design of the BUG bolometer array and the fabrication sequence used to implement the design. Prior to joining with the backshort array, the bolometer array comprises, more specifically, a square grid of free-standing molybdenum/gold superconducting-transition-edge bolometer elements on a 1.4- m-thick top layer of silicon that is part of a silicon support frame made from a silicon-on-insulator wafer. The backshort array is fabricated separately as a frame structure that includes support beams and contains a correspond - ing grid of optically reflective patches on a single-crystal silicon substrate. The process used to fabricate the bolometer array includes standard patterning and etching steps that result in the formation of deep notches in the silicon support frame. These notches are designed to interlock with the support beams on the backshort-array structure to provide structural support and precise relative positioning. The backshort-array structure is inserted in the silicon support frame behind the bolometer array, and the notches in the frame serve to receive the support beams of the backshort-array structure and thus determine the distance between the backshort and bolometer planes. The depth of the notches and, thus, the distance between the backshort and bolometer planes, can be tailored to a value between 25 to 300 m adjusting only a few process steps. The backshort array is designed so as not to interfere with the placement of indium bumps for subsequent indium bump-bonding to the multiplexing readout circuitry

Miller, Timothy M.↗

NASA NTRS2013

Characterization of Swirl-Venturi Lean Direct Injection Designs for Aviation Gas-Turbine Combustion

Injector geometry, physical mixing, chemical processes, and engine cycle conditions together govern performance, operability and emission characteristics of aviation gas-turbine combustion systems. The present investigation explores swirl-venturi lean direct injection combustor fundamentals, characterizing the influence of key geometric injector parameters on reacting flow physics and emission production trends. In this computational study, a design space exploration was performed using a parameterized swirl-venturi lean direct injector model. From the parametric geometry, 20 three-element lean direct injection combustor sectors were produced and simulated using steady-state, Reynolds-averaged Navier-Stokes reacting computations. Species concentrations were solved directly using a reduced 18-step reaction mechanism for Jet-A. Turbulence closure was obtained using a nonlinear κ-ε model. Results demonstrate sensitivities of the geometric perturbations on axially averaged flow field responses. Output variables include axial velocity, turbulent kinetic energy, static temperature, fuel patternation and minor species mass fractions. Significant trends have been reduced to surrogate model approximations, intended to guide future injector design trade studies and advance aviation gas-turbine combustion research.

Combustion Chamber↗

DOE OSTI2022

Harnessing structural stochasticity in the computational discovery and design of microstructures

This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic microstructure design domains has yet to be created. The proposed methodology resolves this issue by learning a unified feature space that embodies diverse structural patterns with continuously varying stochasticity levels. A highly diverse microstructure database is established to incorporate various types of deterministic and stochastic microstructure patterns. A property-aware deep generative model is proposed to learn a unified feature space of the structural characteristics, as well as the relationship between structure features and properties of interest. Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. Microstructural designs with tailorable stochasticity and properties are obtained by searching the unified feature space. Multiple design cases are presented to demonstrate the capability of designing microstructures for structural stochasticity and properties. Furthermore, the proposed method is employed to create stochastically graded structures, which manipulate the mechanical behaviors by varying the local stochasticity of the structure.

36 MATERIALS SCIENCE↗

DOE OSTI2021

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗