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Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection
This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.
Process_to_Down-Select_a_Counter-Unmanned_Aircraft_System_for_Testing_and_Evaluation.
Abstract not provided.
PRISM: Process Parameter Optimization for Selective Manufacturing
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Roadmap to Iodine and Mercury Abatement Materials Selection in Nuclear Waste Processing Off-Gas Streams
This work provides a guide for candidate mercury (Hg) and iodine-129 (129I) abatement material identification, screening, evaluation, technical gap identification, and bench-scale testing prioritization while also conceptualizing a materials deployment roadmap for implementation of new materials in the Hanford Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility at Hanford in Richland, Washington, and elsewhere in the U.S. Department of Energy (DOE) complex for similar applications. The study was prompted by the need to replace the Kombisorb BAT-37 due to uncertainties in Hg and 129I capture performance and future availability for use in the WTP LAW Facility secondary off-gas system, specifically the Carbon Adsorber units. However, replacement of this material could also mitigate two other issues with Kombisorb BAT-37 (and its successor BAT II 37): (1) fire safety risk due to exothermic heat generated by adsorption reactions between the carbon material and Hg and (2) the risk of low retention of 129I in glass and subsequent downstream impacts on secondary liquid waste treatment at the Effluent Treatment Facility.
Characterization of the Temperature Capabilities of Advanced Disk Alloy ME3
The successful development of an advanced powder metallurgy disk alloy, ME3, was initiated in the NASA High Speed Research/Enabling Propulsion Materials (HSR/EPM) Compressor/Turbine Disk program in cooperation with General Electric Engine Company and Pratt & Whitney Aircraft Engines. This alloy was designed using statistical screening and optimization of composition and processing variables to have extended durability at 1200 F in large disks. Disks of this alloy were produced at the conclusion of the program using a realistic scaled-up disk shape and processing to enable demonstration of these properties. The objective of the Ultra-Efficient Engine Technologies disk program was to assess the mechanical properties of these ME3 disks as functions of temperature in order to estimate the maximum temperature capabilities of this advanced alloy. These disks were sectioned, machined into specimens, and extensively tested. Additional sub-scale disks and blanks were processed and selectively tested to explore the effects of several processing variations on mechanical properties. Results indicate the baseline ME3 alloy and process can produce 1300 to 1350 F temperature capabilities, dependent on detailed disk and engine design property requirements.
Joint Test Protocol for Validation of Alternative Low-Emission Surface Preparation/Depainting Technologies for Structural Steel
Headquarters National Aeronautics and Space Administration (NASA) chartered the Acquisition Pollution Prevention (AP2) Office to coordinate agency activities affecting pollution prevention issues identified during system and component acquisition and sustainment processes. The primary objectives of the AP2 Office are to: (1) Reduce or eliminate the use of hazardous materials (HazMats) or hazardous processes at manufacturing, remanufacturing, and sustainment locations. (2) A void duplication of effort in actions required to reduce or eliminate HazMats through joint center cooperation and technology sharing. This project will identify, evaluate and approve alternative surface preparation technologies for use at NASA and Air Force Space Command (AFSPC) installations. Materials and processes will be evaluated with the goal of selecting those processes that will improve corrosion protection at critical systems, facilitate easier maintenance activity, extend maintenance cycles, eliminate flight hardware contamination and reduce the amount of hazardous waste generated. This Joint Test Protocol (JTP) contains the critical requirements and tests necessary to qualify alternative Low-Emission Surface Preparation/Depainting Technologies for Structural Steel Applications. These tests were derived from engineering, performance, and operational impact (supportability) requirements defined by a consensus of NASA and Air Force Space Command (AFSPC) participants. The Field Test Plan (FTP), entitled Joint Test Protocol for Validation of Alternative Low Emission Surface Preparation/Depainting Technologies for Structural Steel, prepared by ITB, defines the field evaluation and testing requirements for validating alternative surface preparation/depainting technologies and supplements the JTP.
Field Evaluations Test Plan for Validation of Alternative Low-Emission Surface Preparation/Depainting Technologies for Structural Steel
Headquarters National Aeronautics and Space Administration (NASA) chartered the Acquisition Pollution Prevention (AP2) Office to coordinate agency activities affecting pollution prevention issues identified during system and component acquisition and sustainment processes. The primary objectives of the AP2 Office are to: (1) Reduce or eliminate the use of hazardous materials (HazMats) or hazardous processes at manufacturing, remanufacturing, and sustainment locations. (2) Avoid duplication of effort in actions required to reduce or eliminate HazMats through joint center cooperation and technology sharing. To achieve a substrate condition suitable for the application of a coating system, both new and old (in-situ) substrates must undergo some type of surface preparation and/or depainting operation to ensure adhesion of the new coating system. The level of cleanliness or anchor profile desired is typically a function of the type of coating to be applied and the specification being adhered to. In high performance environments, cleanliness and surface profile requirements for carbon steel (the dominant substrate for facilities, structures and equipment) dictates the use of abrasive media. Many of the abrasive media currently used across NASA and Air Force Space Command (AFSPC) installations generate large quantities of fugitive particulate emissions and waste. The high quantities of airborne dust and waste generated from these operations pose significant environmental concern. Efforts to contain emissions and the reduce quantity of waste generated have significant implications on project cost; this is often a deterrent to engaging in maintenance activities. In response to recent technological developments and NASA's and AFSPC's need to undertake environmentally conscious corrosion prevention projects, a review of the industry needs to be undertaken to evaluate surface preparation technologies (materials and processes) for embrace. This project will identify, evaluate and approve alternative surface preparation technologies for use at NASA and AFSPC installations. Materials and processes will be evaluated with the goal of selecting those processes that will improve corrosion protection at critical systems, facilitate easier maintenance activity, extend maintenance cycles, eliminate flight hardware contamination and reduce the amount of hazardous waste generated. This Field Evaluations Test Plan defines the field evaluation and testing requirements for validating alternative surface preparation/depainting technologies and supplements the JTP. The field evaluations will be performed at Stennis Space Center, Mississippi, under the oversight of the Project Engineer. Additional field evaluations may be performed at other NASA centers or AFSPC facilities.
Oxygen production System Models for Lunar ISRU
In-Situ Resource Utilization (ISRU) seeks to make human space exploration feasible; by using available resources from a planet or the moon to produce consumables, parts, and structures that otherwise would be brought from Earth. Producing these in situ reduces the mass of such that must be launched and doing so allows more payload mass' for each mission. The production of oxygen from lunar regolith, for life support and propellant, is one of the tasks being studied under ISRU. NASA is currently funding three processes that have shown technical merit for the production of oxygen from regolith: Molten Salt Electrolysis, Hydrogen Reduction of Ilmenite, and Carbothermal Reduction. The ISRU program is currently developing system models of, the , abovementioned processes to: (1) help NASA in the evaluation process to select the most cost-effective and efficient process for further prototype development, (2) identify key parameters, (3) optimize the oxygen production process, (4) provide estimates on energy and power requirements, mass and volume.of the system, oxygen production rate, mass of regolith required, mass of consumables, and other important parameters, and (5) integrate into the overall end-to-end ISRU system model, which could be integrated with mission architecture models. The oxygen production system model is divided into modules that represent unit operations (e.g., reactor, water electrolyzer, heat exchanger). Each module is modeled theoretically using Excel and Visual Basic for Applications (VBA), and will be validated using experimental data from on-going laboratory work. This modularity (plug-n-play) feature of each unit operation allows the use of the same model on different oxygen production systems simulations resulting in comparable results. In this presentation, preliminary results for mass, power, volume will be presented along with brief description of the oxygen production system model.
Process to Down-Select a Counter-Unmanned Aircraft System for Testing and Evaluation.
Abstract not provided.
Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
The Electrochemical Behavior of As-Prepared Aluminum Alloy A360 Produced by Selective Laser Melting Fabrication with and without a Trivalent Chromium Process Conversion Coating
In this paper, we report on the electrochemical behavior and corrosion resistance of as-prepared aluminum alloy, A360 (AlSi10Mg), fabricated by additive manufacturing, with and without a trivalent chromium process conversion coating. Selective laser melting was the 3D printing process used for the alloy build. The corrosion suppression provided by the coating system was assessed through electrochemical measurements and accelerated degradation testing during a 14-day continuous neutral salt-spray exposure. The results indicate that the conversion coating can be formed by solution pretreating (degreasing and deoxidation) the alloy (X-Z plane perpendicular to the build plane) with its native surface roughness (as-prepared) followed by immersion for 10, 15 or 20 min in the coating bath. All three immersion times provide an equivalent level of corrosion protection to the alloy. The conversion coating suppresses both anodic and cathodic currents, increases the polarization resistance, and provides both anodic and cathodic corrosion protection to the as-prepared alloy. The conversion-coated specimens exhibit good stand-alone corrosion resistance during a 14-day neutral salt-spray exposure with corrosion intensity values (g/m 2 -year) ~10x lower than values for the uncoated, as-prepared alloy specimens.
Plating Processes Utilizing High Intensity Acoustic Beams
A system and a method for selective plating processes are disclosed which use directed beams of high intensity acoustic waves to create non-linear effects that alter and improve the plating process. The directed beams are focused on the surface of an object, which in one embodiment is immersed in a plating solution, and in another embodiment is suspended above a plating solution. The plating processes provide precise control of the thickness of the layers of the plating, while at the same time, in at least some incidents, eliminates the need for masking.
Selecting optimum algorithms for image processing
Collection of registration, compression, and classification algorithms allows users to evaluate approaches and select best one for particular application. Program includes six registration algorithms, six compression algorithms, and two classification algorithms. Package also includes routines for evaluating effects of processing on image data. Collection is written in FORTRAN IV for batch execution.
Sulfur-Donor Solvents Strongly Coordinate Pb 2+ in Hybrid Organic–Inorganic Perovskite Precursor Solutions
Strong coordination between Lewis-basic processing additives and the Lewis-acidic lead halide in hybrid organic–inorganic perovskite (HOIP) precursor solutions is required to solubilize the lead halide, and subsequently access the appropriate crystallization kinetics and attain the desired morphology of perovskite active layers. While oxygen-donor solvents and additives, such as dimethylformamide and dimethyl sulfoxide, are widely used for perovskite processing, we demonstrate here that “soft” sulfur-donor solvents exhibit stronger coordination to the “borderline soft” Lewis acid Pb 2+ center of PbI 2 relative to “hard” O-donor solvents in the precursor solution. The stronger coordination of S-donor solvents compared to O-donor solvents to Pb 2+ implies that such compounds can be useful additives to HOIP precursor solutions. Density-functional calculations of the enthalpy change resulting from the coordination of solvents to Pb 2+ provide direct numerical comparison of the strength of O-donor and S-donor coordination with Pb 2+ and expands the library of candidate S-donor compounds. Our results provide a roadmap for processing additive selection and expand the previously limited choice of perovskite processing additives to include strongly coordinating S-donor compounds.
Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement
This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.