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At least 883 records · Page 49

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NASA NTRSDate not supplied

Artemis I Space Launch System Base Heat Shield Thermal Protection System Performance

The Space Launch System (SLS) Core Stage base heat shield experienced the highest external heating environments on the entire launch vehicle during Artemis I ascent flight. This result was consistent with design predictions. The base heat shield experiences P50 cork combustion dynamics at low altitudes, plume-induced recirculation at moderate altitudes and then in-space base flow physics out to Main Engine Cut-Off (MECO). The base heat shield thermal protection system (TPS) is composed of a P50 cork ablator which is bonded to a substrate. The heat shield protects the gimbal actuation system, RS-25 turbomachinery systems and other aft section sensitive components during ascent. This paper estimates the base heat shield TPS performance from Artemis I using flight data from the NASA Langley Research Center’s Scientifically Calibrated In-Flight Imagery (SCIFLI) Airborne Multispectral Imager (SAMI), development flight instrumentation (DFI) and other TPS recession flight measurements. Predictions from computational and ground test-derived engineering ablation models and observations are also applied. Since no base heat shield substrate thermocouple data were obtained for Artemis I, an estimate of the TPS performance data is derived here. This data assesses thermal margin of the SLS Core Stage base heat shield and best informs the Artemis II Crewed mission to the moon.

aerothermodynamics↗

DOE OSTI2020

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

42 ENGINEERING↗

NASA NTRS2003

Lessons Learned from using a Livingstone Model to Diagnose a Main Propulsion System

NASA researchers have demonstrated that qualitative, model-based reasoning can be used for fault detection in a Main Propulsion System (MPS), a complex, continuous system. At the heart of this diagnostic system is Livingstone, a discrete, propositional logic-based inference engine. Livingstone comprises a language for specifying a discrete model of the system and a set of algorithms that use the model to track the system's state. Livingstone uses the model to test assumptions about the state of a component - observations from the system are compared with values predicted by the model. The intent of this paper is to summarize some advantages of Livingstone seen through our modeling experience: for instance, flexibility in modeling, speed and maturity. We also describe some shortcomings we perceived in the implementation of Livingstone, such as modeling continuous dynamics and handling of transients. We list some upcoming enhancements to the next version of Livingstone that may resolve some of the current limitations.

Sweet, Adam↗

DOE OSTI2017

Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 s to execute. Additionally, well-posedness and finite element error analyses of the state system and optimization problem are provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1991

User modeling techniques for enhanced usability of OPSMODEL operations simulation software

The PC based OPSMODEL operations software for modeling and simulation of space station crew activities supports engineering and cost analyses and operations planning. Using top-down modeling, the level of detail required in the data base can be limited to being commensurate with the results required of any particular analysis. To perform a simulation, a resource environment consisting of locations, crew definition, equipment, and consumables is first defined. Activities to be simulated are then defined as operations and scheduled as desired. These operations are defined within a 1000 level priority structure. The simulation on OPSMODEL, then, consists of the following: user defined, user scheduled operations executing within an environment of user defined resource and priority constraints. Techniques for prioritizing operations to realistically model a representative daily scenario of on-orbit space station crew activities are discussed. The large number of priority levels allows priorities to be assigned commensurate with the detail necessary for a given simulation. Several techniques for realistic modeling of day-to-day work carryover are also addressed.

Davis, William T.↗

DOE OSTI2016

Development of building energy asset rating using stock modelling in the USA

The US Building Energy Asset Score helps building stakeholders quickly gain insight into the efficiency of building systems (envelope, electrical and mechanical systems). A robust, easy-to-understand 10-point scoring system was developed to facilitate an unbiased comparison of similar building types across the country. The Asset Score does not rely on a database or specific building baselines to establish a rating. Rather, distributions of energy use intensity (EUI) for various building use types were constructed using Latin hypercube sampling and converted to a series of stepped linear scales to score buildings. A score is calculated based on the modelled source EUI after adjusting for climate. A web-based scoring tool, which incorporates an analytical engine and a simulation engine, was developed to standardize energy modelling and reduce implementation cost. This paper discusses the methodology used to perform several hundred thousand building simulation runs and develop the scoring scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRS2019

Heat Analysis Manager (HAM), a Thermal Desktop API Based Heat Map Generation Software

Thermal engineers often create custom heat maps to analyze their thermal model. However, generating a heat map is difficult because thermal simulation only readily provide attributes of simulated nodes such as temperature, capacitance, heat generation, and a network of conductances. Heat flow values are a derived quantity from the nodal attributes, and the data processing and management of heat flow between nodes quickly become difficult for large models. Deriving a network of heat flow values requires vast amount of calculations and data handling, heat map generation process generally suffers from processing speed, loss of accuracy, and/or presentation of data in a useful format. Heat Analysis Manager (HAM) is a Thermal Desktop (TD) based free multi-purpose tool developed to aid thermal engineers in analyzing their thermal model, including a heat map generation functionality. HAM’s heat map generator retains accuracy and fast processing speed by utilizing TD’s application programming interface (API) and built-in TD’s “Qflow from Results.” Furthermore, HAM’s heat map output is presented in an easily customizable format in Excel, allowing users to create various custom visual heat maps. A full description of how HAM utilizes TD’s API to create a customizable heat map is provided. A simple model demonstration is included along with step-by-step procedures on creating custom heat maps. HAM’s heat map result has been verified against TD’s and other heat map generation software, and verification methods are also included.

Thermal desktop↗

NASA NTRSDate not supplied

NASA’s Safety, Reliability, and Mission Assurance Digital Future

The evolution from “document-centric” to “data-centric” and “model-centric” information leveraging structured data and model-based approaches is at the heart of digital engineering transformational efforts underway across industry and government. It is these approaches that pave the way for data lakes, Authoritative Sources of Truth (ASOTs), and systems- of-systems interoperability and the corresponding transformational benefits thereof. Such benefits include increased data availability, data access equity, data traceability, real-time analytics, batch analytics, and (most importantly) acceleration of the time-to-value and time-to-insights associated with engineering products and analyses. The longer-term benefits of reusability, customization and traceability are even more promising. For Safety and Mission Assurance (SMA), and Mission Success (SMS) activities; realization of such benefits is essential to provide engineers and analysts alike vital information when needed to support critical decision making throughout the entire life cycle. The SMA community often operate in parallel with engineering activities, for which information exchange with relevant context is paramount. Far too often, such information lags key decision points and/or is absent of the robust, integrated, knowledge needed, given inherent barriers associated with traditional document-centric means to data sharing, analysis, and reporting. This paper provides an overview of how NASA’s Office of Safety and Mission Assurance (OSMA) is evolving its policies, standards, guidance, and training to transform to eliminate such barriers, thus realizing the benefits emerging in this new digital era. A roadmap for achieving this digital future is presented along with key building blocks involving use and implementation of concepts such as: Objectives-Hierarchies, Objective-Driven Requirements, Accepted Standards, Safety and Assurance Cases, data digitization (i.e., ontologies, structured data, and model-centric data), FAIR (Findable, Accessible, Interoperable, & Reusable) and/or FAIRUST (Findable, Accessible, Interoperable, Reusable, Understandable, Secure, and Trusted) principles [1]. This paper also describes how OSMA, leveraging the Agency’s overall commitment to Digital Transformation (DT), is using the power of Policy, “Digital” Domain representation, Product Evolution, and Community Outreach and Engagement as part of a strategic vision and roadmap to evolve and transform its SMA organizations to become better able to serve its stakeholders and customers. Future publications will elaborate on these building blocks and deeper concepts.

Authoritative Source of Truth (ASOT),↗

NASA NTRS1982

Computing the Power-Density Spectrum for an Engineering Model

Computer program for calculating of power-density spectrum (PDS) from data base generated by Advanced Continuous Simulation Language (ACSL) uses algorithm that employs fast Fourier transform (FFT) to calculate PDS of variable. Accomplished by first estimating autocovariance function of variable and then taking FFT of smoothed autocovariance function to obtain PDS. Fast-Fourier-transform technique conserves computer resources.

Dunn, H. J.↗

NASA NTRS2013

Improved Cell Culture Method for Growing Contracting Skeletal Muscle Models

An improved method for culturing immature muscle cells (myoblasts) into a mature skeletal muscle overcomes some of the notable limitations of prior culture methods. The development of the method is a major advance in tissue engineering in that, for the first time, a cell-based model spontaneously fuses and differentiates into masses of highly aligned, contracting myotubes. This method enables (1) the construction of improved two-dimensional (monolayer) skeletal muscle test beds; (2) development of contracting three-dimensional tissue models; and (3) improved transplantable tissues for biomedical and regenerative medicine applications. With adaptation, this method also offers potential application for production of other tissue types (i.e., bone and cardiac) from corresponding precursor cells.

Marquette, Michele L.↗

NASA NTRS1988

Research Study: Space Shuttle Main Engine Plume Flowfield Model

The initial research effort was an in-depth analysis of the shuttle main engine plumes in an effort to improve the flowfield model and to enhance shuttle base heating equipment predictions during ascent. A prediction methodology code was developed incorporating the improved plume model into a predictive tool which could consider different trajectoreis and engine perfromance variables. Various plume flow model improvement studies were ongoing at the time of the 51-L accident. Since that time, base heating and plume methodology improvements have continued as part of the overall emphasis on Shuttle design assurance before resuming flight schedule.

Bender, Robert L.↗

DOE OSTI2018

Model-Based Characterization of Elevated Temperature and High Pressure Oxy-Coal Combustion Systems

Reaction Engineering International is leading a DOE-funded program focused on characterization of the impacts of high temperatures and pressures in oxy-coal combustion systems. A primary objective is to evaluate flame behavior, heat transfer, and ash chemistry at high pressure to facilitate the application of pressurized oxy-coal in full-scale systems. The University of Utah’s 300 kW pressurized entrained flow gasifier has been successfully modified and operated as a high pressure, slurry-fed, oxy-coal combustor at firing rates ranging from nearly 100 kW to over 180 kW with reactor pressures as high as 200 psi. This presentation will describe the modifications to the gasifier to allow it to operate as a high pressure, oxy-coal combustor; detail experimental data including measurements of radiant flux, wall surface temperatures, and ash aerosol characterization; and CFD modeling of the experimental conditions. In addition, an update will be provided for accomplishments achieved to date for the component of the program focused on modeling of high temperature oxy-coal combustion using undiluted directed oxygen with minimal flue gas recirculation at atmospheric pressure.

CFD modeling↗

DOE OSTI2019

Process Modeling of U-10wt% Mo Alloys Using Integrated Computational Materials Engineering

Low-enriched uranium alloyed with 10 wt% molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium as fuel for the nation’s high-performance research reactors. Manufacturing the U-10Mo alloy consists of multiple complex thermomechanical processes, which is highly challenging for computational modeling. The integrated computational materials engineering (ICME) concept supports building a microstructure-based framework to investigate the effect of manufacturing processes on the material microstructure. In this report, an ICME model that combines homogenization, hot rolling, annealing, and cold rolling is presented as an example of implementing the ICME concept for modeling the manufacturing processes of the U-10Mo alloy. The integrated model enables information to be passed between the different model components, leading to an improved prediction and a better understanding of microstructure across multiple processes. The integrated model was also implemented to investigate the variation of chemical composition, thickness of the zirconium interlayer, grain growth, and carbide redistribution and fracture of U-10Mo during the thermomechanical processes. With the homogenization model, a molybdenum concentration map can be reconstructed from any given microstructure, and the homogenization time needed to reach a desired homogenization level can also be approximated. The microstructure-based finite-element rolling model, shows that the homogenization process reduces the nonuniformity in the thickness of the zirconium interlayers on the U-10Mo. With the output from the rolling model, a statistical analysis of second-phase hard particles was conducted to quantify the particle redistribution and interparticle spacing between carbides during the deformation. Also, new stringer identification criteria were proposed to predict the correlation between stringer volume fractions and hot-rolling reduction. A macro-finite-element method hot-rolling model showed that higher reduction passes can be achieved within the mill separation force range, and a new “aggressive” rolling schedule was proposed. The model also showed the importance of can material on the quality of rolled foils, with stronger can materials resulting in hot-rolled U-10Mo free of defects (waviness, dog-boning). The deformation-induced recrystallization model demonstrated capability to use inputs from deformation simulations of strain accumulation in U-10Mo samples to predict grain growth behavior of the fuel during thermal treatment stages. Finally, a plane-strain compression mode simulated particle fracture and calculates the void volume fraction generated during cold rolling. The macro-finite-element method cold-rolling model showed that smaller diameter rolls are necessary to achieve the desired foil thickness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2019

Analysis of Experimental Ice Accretion Data and Assessment of a Thermodynamic Model During Ice Crystal Icing

This paper evaluates a thermodynamic ice crystal icing model that has been previously presented to describe the possible mechanisms of icing within the core of a turbofan jet engine. The model functions between two distinct ice accretions based on a surface energy balance: freeze-dominated icing and melt-dominated icing. Freeze-dominated icing occurs when liquid water (from melted ice crystals) freezes and accretes on a surface along with the existing ice of the impinging water and ice mass. This freeze-dominated icing is characterized as having strong adhesion to the surface. The amount of ice accretion is partially dictated by a freeze fraction, which is the fraction of impinging liquid water that freezes. Melt-dominated icing occurs as unmelted ice on a surface accumulates. This melt-dominated icing is characterized by weakly bonded surface adhesion. The amount of ice accumulation is partially dictated by a melt fraction, which is the fraction of impinging ice crystals that melts. Experimentally observed ice growth rates suggest that only a small fraction of the impinging ice remains on the surface, implying a mass loss mechanism such as splash, runback, bounce, or erosion. The fraction of mass loss must be determined in conjunction with the fraction of freezing liquid water or fraction of melting ice on an icing surface for a given ice growth rate. This mass loss parameter, however, along with the freeze fraction and melt fraction, are the only experimental parameters that are currently not measured directly. Using icing growth rates from ice crystal icing experiments, a methodology that has been previously proposed is used to determine these unknown parameters. This work takes ice accretion data from tests conducted by the National Aeronautics and Space Administration (NASA) at the Glenn Research Center in 2018 that examined the fundamental physics of ice crystal icing. This paper continues evaluation of the thermodynamic model from a previous effort, with additions to the model that account for sub-freezing temperatures that have been observed at the leading edge of the airfoil during icing. The predicted temperatures were generally in good agreement with measured temperatures. Other key findings include the total wet-bulb temperature being a good first order indicator of whether icing is freeze-dominated (sub-freezing values) or melt-dominated (above freezing). Maximum sticking efficiency values, the fraction of impinging mass that adheres to a surface, was calculated to be about 0.2, and retained this maximum value for a range of melt ratios (0.3 to 0.65 and possibly higher), which is defined as the ratio of liquid water content to total water content. Higher air velocities reduced the maximum sticking efficiency and shifted the icing regime to higher melt ratio values. Finally, the leading edge ice accretion angle was found to be related to ice growth (lower growth rates for smaller angles) and melt ratio (smaller melt ratios resulted in smaller angles, likely due to erosion effects).

Ice-Crystal Icing↗