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Engine-demand airflow used as primary control parameter. Optimum inlet angle depends on such other variables as mach number, altitude, and ambient temperature. Function of these variables approximated by schedule ("inlet ramp schedule") of digitized values implemented by digital control system.
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
Nuclear power continues to have an imperative role for the U.S.’s electricity generation. However, for these nuclear power plants (NPPs) to remain economically viable, new strategies for reducing operations and maintenance costs must be explored. The U.S. Department of Energy Light Water Reactor Sustainability Program is researching how to digitally transform existing analog and hybrid main control rooms into a fully integrated control room that addresses this challenge. The transformation will fundamentally change the conduct of operations for these U.S. NPPs. Human factors engineering has a vital role in this effort, where traditional task analysis methods are important in informing the design of advanced human-system interface displays. This work describes a preliminary tool to support task analysis for the development of these advanced displays. Details on the use of this tool, including the specific task analysis methods offered, are presented in this paper.
Within the United States (U.S.), the Fusion Innovation Research Engine (FIRE) Collaboratives seek to accelerate fusion technology development through wide-ranging community-driven research activities that bring together industry, laboratories, research institutes, and academia. Increasing the maturity of key components and systems for future fusion power plants (FPPs) toward commercialization, will, by necessity, increase the need for knowledgeable engineers, designers, and researchers in industry capable of integrating and further improving these technologies within industry FPP concepts. Further, various modeling and simulation packages support these programs and are under-development within them to drive design iteration and the development of FPP digital twins. To derive the greatest benefit from model output and capabilities, training these same specialists will be vital. Thus, the development of effective and accessible software onboarding and professional development opportunities focused on fusion will be key to keeping the pace of growth high in the coming decade and beyond. A similar need exists within the advanced fission reactor community, driven by an ever-growing need for carbon-free baseload power for industrial and data center applications. Within the U.S. and around the world, several open-source packages and frameworks exist to support this endeavor, and two we will highlight here are the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and OpenMC. These packages, notably, are also being utilized in the FIRE Collaboratives program. In this presentation, we will highlight the lessons learned from over 30 years of combined software development and training experience, focused on developing strong technical software foundations within the nuclear workforce, from students to professional engineers & scientists. We will connect this experience to present and emerging needs in the fusion energy community, and, finally, will outline possible paths forward to develop a large, robust, global fusion workforce.
The flexibility provided by High Voltage Direct Current (HVDC) systems can be further extended by Wide Area Monitoring, Protection, and Control (WAMAPC) systems. WAMPAC systems enable many HVDC applications and, on the other hand, inevitably introduce cybersecurity concerns that need to be addressed. In this work, a security domain layer and decision framework is reported to detect and mitigate the impact of false data injection (FDI) attacks targeting HVDC stations. Specifically, a rule-based cyber-attack detection method is introduced and implemented on Raspberry Pi and tested on the real-time HVDC simulation facility on the real-time digital simulator (RTDS) platform at ABB U.S. Corporate Research Center.
A technique of monitoring digital closed-loop feedback systems has been conceived. The basic idea is to obtain information on the performances of closed-loop feedback circuits in such systems to aid in the determination of the functionality and integrity of the circuits and of performance margins. The need for this technique arises as follows: Some modern digital systems include feedback circuits that enable other circuits to perform with precision and are tolerant of changes in environment and the device s parameters. For example, in a precision timing circuit, it is desirable to make the circuit insensitive to variability as a result of the manufacture of circuit components and to the effects of temperature, voltage, radiation, and aging. However, such a design can also result in masking the indications of damaged and/or deteriorating components. The present technique incorporates test circuitry and associated engineering-telemetry circuitry into an embedded system to monitor the closed-loop feedback circuits, using spare gates that are often available in field programmable gate arrays (FPGAs). This technique enables a test engineer to determine the amount of performance margin in the system, detect out of family circuit performance, and determine one or more trend(s) in the performance of the system. In one system to which the technique has been applied, an ultra-stable oscillator is used as a reference for internal adjustment of 12 time-to-digital converters (TDCs). The feedback circuit produces a pulse-width-modulated signal that is fed as a control input into an amplifier, which controls the circuit s operating voltage. If the circuit s gates are determined to be operating too slowly or rapidly when their timing is compared with that of the reference signal, then the pulse width increases or decreases, respectively, thereby commanding the amplifier to increase or reduce, respectively, its output level, and "adjust" the speed of the circuits. The nominal frequency of the TDC s pulse width modulated outputs is approximately 40 kHz. In this system, the technique is implemented by means of a monitoring circuit that includes a 20-MHz sampling circuit and a 24-bit accumulator with a gate time of 10 ms. The monitoring circuit measures the duty cycle of each of the 12 TDCs at a repetition rate of 28 Hz. The accumulator content is reset to all zeroes at the beginning of each measurement period and is then incremented or decremented based of the value of the state of the pulse width modulated signal. Positive or negative values in the accumulator correspond to duty cycles greater or less, respectively, than 50 percent.
Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.
The results of ice adhesion testing efforts are typically quantified using the maximum shear stress sustained at the interface before the ice detaches from the underlying substrate. While this singular value can be useful for understanding the role icing wind tunnel conditions and substrate surface properties have on the strength of the ice-substrate interface, it does not capture the mechanical behavior of the ice during an experiment. To better understand the deformation of the ice sample and ice-substrate interface during testing, digital image correlation was applied to the lap-joint shear test methodology utilized at NASA Glenn Research Center. Preparation of the sample surface for digital image correlation, initial parsing of the correlation data, and the time synchronization of data from multiple sources are discussed. Using the directional components of deformation measured via digital image correlation, a novel approach to calculating the interfacial shear strain is presented. Results from analyzing several impact ice samples consistently reveals a non-linear response in the ice during mechanical loading which occurs prior to sample delamination, a behavior not observable in the force and displacement data gathered from the testing apparatus. Both the full and small angle forms of the engineering shear strain are evaluated, with the full shear strain form providing considerably lower noise levels. The transition of sample response from near the interface to the bulk behavior of the ice under shear loading is discussed with results from several samples being presented.
A small, portable, relatively inexpensive computer system was developed for on-line use in clinical or laboratory situations. The system features an integrated hardware-software package that permits use of all peripherals, such as analog-to-digital converter, oscilloscope, plotter, digital bus, with an interpreter constructed around the BASIC programming language. The system is conceptually similar to the LINC system developed in 1962, but is more compact and powerful due to intervening advances in integrated circuit technology. A description of the hardware of the system was given. A reference manual, user manual, and programming guides were also presented. Finally, a stereo display system for vectorcardiograms was described.
Methods are presented for estimating some of the more fundamental design variables of single-rotor helicopters (tip speed, blade area, disk loading, and installed power) based on design requirements (speed, weight, fuselage drag, and design hover ceiling). The well-known constraints of advancing-blade compressibility and retreating-blade stall are incorporated into the estimation process, based on an empirical interpretation of rotor performance data from large-scale wind-tunnel tests. Engine performance data are presented and correlated with a simple model usable for preliminary design. When approximate results are required quickly, these methods may be more convenient to use and provide more insight than large digital computer programs.
The Microwave Anisotropy Probe (MAP) was launched June 30, 2001 to create an all-sky map of the Cosmic Microwave Background. The mission's hardware suite included two Lockheed Martin AST-201 star trackers, two Kearfott Two-Axis Rate Assemblies (TARAs) mounted to provide X, Y and redundant Z-axis rates, two Adcole Digital Sun Sensor (DSS) heads sharing one set of electronics, twelve Adcole Coarse Sun Sensor (CSS) eyes, three Ithaco E-sized Reaction Wheel Assemblies (RWAs), and a Propulsion Subsystem that employed eight PRIMEX Rocket Engine Modules (REMs). This hardware has allowed MAP to meet its various Orbit and Attitude Control Requirements, including performing a complex zero-momentum scan, meeting its attitude determination requirements, and maintaining a trajectory that places MAP in a lissajous orbit around the second Sun-Earth Lagrange point (L2) via phasing loops and a lunar gravity assist. Details of MAP's attitude determination, attitude control, and trajectory design are presented separately. This paper will focus on the performance of the hardware components mentioned above, as well as the significant lessons learned through the use of these components. An emphasis will be placed on spacecraft design modifications that were needed to accommodate existing hardware designs into the MAP Observatory design.
Climate induced sea-level rise poses a risk to coastal areas on the Island of Hawai’i, and many of the island’s historic cultural lands are in danger of becoming overtaken by wetlands or inundation. In partnership with the County of Hawai’i, State of Hawai’i Department of Land and Natural Resources, and Arizona State University, NASA DEVELOP mapped wetland extent and short-term sea-level rise inundation risk. We utilized Earth observations over a 10-year span (2013 – 2022) that included the NASA MEaSUREs Gridded Sea Surface Height Anomalies and MEaSUREs Group for High Resolution Sea Surface Temperature datasets, United States Geological Survey (USGS) Hawaii Digital Elevation Models (DEM), and in situ tidal gauge data. Flood risk index values were acquired for 5 known Hawai’i flood events between 2019 – 2021 from the Global Flood Mapper tool on Google Earth Engine. We used a random forest model to predict short-term sea-level rise inundation risk along the entire coast of Hawai’i. Current wetland extents and probabilistic locations of new wetlands were modeled with the most recently available data from PlanetScope Surface Reflectance optical imagery (2022), USGS 3D Elevation Program (3DEP) 10m DEM (2020), temperature and precipitation data from the Hawai’i Climate Atlas, and soils data from the Hawai’i Soil Atlas (2014) using the Wetland Intrinsic Potential tool. Results indicated locations that had the highest probability of wetland creation. The end products aimed to help the partners prioritize efforts to meeting regulation requirements for wetlands protection, evaluate the inundation risk to historical features, and support decision-making for their Shoreline Setback and Climate Adaption plans.
The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.
The results of ice adhesion testing efforts are typically quantified using the maximum shear stress sustained at the interface before the ice detaches from the underlying substrate. While this singular value can be useful for understanding the role icing wind tunnel conditions and substrate surface properties have on the strength of the ice-substrate interface, it does not capture the mechanical behavior of the ice during an experiment. To better understand the deformation of the ice sample and ice-substrate interface during testing, digital image correlation was applied to the lap-joint shear test methodology utilized at NASA Glenn Research Center. Preparation of the sample surface for digital image correlation, initial parsing of the correlation data, and the time synchronization of data from multiple sources are discussed. Using the directional components of deformation measured via digital image correlation, a novel approach to calculating the interfacial shear strain is presented. Results from analyzing several impact ice samples consistently reveals a non-linear response in the ice during mechanical loading which occurs prior to sample delamination, a behavior not observable in the force and displacement data gathered from the testing apparatus. Both the full and small angle forms of the engineering shear strain are evaluated, with the full shear strain form providing considerably lower noise levels. The transition of sample response from near the interface to the bulk behavior of the ice under shear loading is discussed with results from several samples being presented.
During the year of 1994, the engineering problems in aircraft modeling were studied. The initial concern was to obtain a surface model with desirable geometric characteristics. Much of the effort during the first half of the year was to find an efficient way of solving a computationally difficult optimization model. Since the smoothing technique in the proposal 'Surface Modeling and Optimization Studies of Aerodynamic Configurations' requires solutions of a sequence of large-scale quadratic programming problems, it is important to design algorithms that can solve each quadratic program in a few interactions. This research led to three papers by Dr. W. Li, which were submitted to SIAM Journal on Optimization and Mathematical Programming. Two of these papers have been accepted for publication. Even though significant progress has been made during this phase of research and computation times was reduced from 30 min. to 2 min. for a sample problem, it was not good enough for on-line processing of digitized data points. After discussion with Dr. Robert E. Smith Jr., it was decided not to enforce shape constraints in order in order to simplify the model. As a consequence, P. Dierckx's nonparametric spline fitting approach was adopted, where one has only one control parameter for the fitting process - the error tolerance. At the same time the surface modeling software developed by Imageware was tested. Research indicated a substantially improved fitting of digitalized data points can be achieved if a proper parameterization of the spline surface is chosen. A winning strategy is to incorporate Dierckx's surface fitting with a natural parameterization for aircraft parts. The report consists of 4 chapters. Chapter 1 provides an overview of reverse engineering related to aircraft modeling and some preliminary findings of the effort in the second half of the year. Chapters 2-4 are the research results by Dr. W. Li on penalty functions and conjugate gradient methods for quadratic programming problems.
One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.