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At least 775 records · Page 43

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NASA NTRS2024

Uranus Global Reference Atmospheric Model (Uranus-GRAM) 2024: User Guide

Engineers and mission planners designing vehicles that pass through Uranus’ atmosphere require an atmospheric model that calculates the mean values and variations of atmospheric properties. The Uranus Global Reference Atmospheric Model(Uranus-GRAM)is an engineering- oriented model that provides this information based on data from Voyager observations. Uranus- GRAM is designed to offer mission planners the flexibility to select input parameters such as time, latitude, and longitude. Uranus-GRAM outputs atmospheric constituent data and mean values for atmospheric density, temperature, pressure, and zonal wind along a user defined path. Uranus-GRAM also provides dispersions of density and zonal wind. Uranus-GRAM is one option in the GRAM Suite that shares a common software core with the other planetary GRAMs while maintaining Uranus specific models. Additionally, documentation, including this User Guide, a Programmer’s Manual, and trajectory code interfaces has been made available with the software release. This Technical Memorandum summarizes the atmospheric data model in Uranus-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Section 2 describes the input atmospheric data files and how they are used in Uranus-GRAM. Section 3 explains the process to obtain the Uranus-GRAM code, the data files, and how to set up and run the program. Appendices A through E provide additional details regarding the Uranus-GRAM input and output files. Appendix F provides a history of Uranus-GRAM revisions.

atmospheric models↗

DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗

DOE OSTI2017

Model-Based Characterization of High Temperature Oxy-Coal Combustion Systems

Reaction Engineering International (REI) is leading a DOE-funded program focused on characterization of the impacts of high temperatures and pressures in oxy-coal combustion systems. The objective is to produce data and validate mechanisms to describe and predict flame behavior, heat transfer, ash deposition and ash chemistry to enable application of these advanced combustion technologies in full-scale systems. This presentation will provide an overview of accomplishments achieved to date on this project and present modeling results of high temperature oxy-coal combustion using undiluted directed oxygen with minimal flue gas recirculation at atmospheric pressure. Comparisons of model results and experimental data from 100 kW and 1.5 MW test furnaces will be provided for gas and wall temperatures, wall heat flux, carbon on ash, ash deposition rates and ash chemistry.

high temperature, modeling↗

DOE OSTI2015

Genome-scale modeling for metabolic engineering

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Simeonidis, E↗

DOE OSTI2018

Integrated Boosting and Hybridization for Extreme Fuel Economy and Downsizing

The objective of this project was to develop, demonstrate, and evaluate commercialization of a highly efficient downsized engine by electrification of the air delivery and waste heat recovery system and optimizing energy usage to achieve significant fuel economy improvement at a commercially viable cost. The system consists of an Electrically Assisted Variable speed Supercharger (EAVS), driven by an electric motor and the engine through a planetary gear train, that provides engine boost pressure and provides the mild hybrid functions of engine start/stop, torque assist, and brake energy recovery. In the exhaust path of the engine is the electric Waste Heat Recovery (eWHR) system, a roots-based expander driven by another electric motor, that enables exhaust gas energy recovery along with controlled back pressure on the engine. The two electric motors are powered by a 48V rechargeable lithium-ion battery. Further, the EAVS system coupled to an EGR valve enables very precise high exhaust recirculation rates to control the NOx emissions. Through two budget periods, the project team of Eaton, Isuzu, Southwest Research Institute and AVL designed and developed the EAVS and eWHR systems, integrated with an Isuzu diesel engine, and validated the target fuel economy performance and emissions by engine dynamometer testing and vehicle level simulations. GT Power model-based analyses results were used to size the EAVS and eWHR systems for the 1.9L Isuzu Spark engine. The two systems were built and the functional testing was carried out to verify the boosting performance of the superchargers. Following the baseline engine dynamometer testing, the two systems were integrated with the engine and the fuel economy performance and emissions were determined by combined steady state and transient testing over three drive cycles, namely, FTP75, NEDC and HWFET. The EAVS/eWHR system demonstrated 4% improvement in fuel economy for the NEDC cycle in engine dyno testing, correlating well with vehicle simulation results (5.3%). The simulation results indicated that the combination of hybrid functions and drivetrain optimization, fuel economy improvement of more than 20% was feasible with the system. The commercialization study to evaluate the cost of the system and the scalability for different engine/vehicle classes was planned for the final budget period but not completed as the project was closed at the end of budget period two.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1994

Model-based software process improvement

The activities of a field test site for the Software Engineering Institute's software process definition project are discussed. Products tested included the improvement model itself, descriptive modeling techniques, the CMM level 2 framework document, and the use of process definition guidelines and templates. The software process improvement model represents a five stage cyclic approach for organizational process improvement. The cycles consist of the initiating, diagnosing, establishing, acting, and leveraging phases.

Zettervall, Brenda T.↗

NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗

NASA NTRSDate not supplied

Scoping of an Air Supply Configuration for AFC on a Commercial Transport Airplane High Lift System

A scoping study is being conducted on the air supply fora microjet-based active flow control (AFC) system on a twin-turbofan commercial transport airplane. Microjets provide circulation control using small surface-normal pneumatic jets located near the trailing edge of a lifting surface such as a wing or flap. When located on the pressure side of the lifting surface they increase the lift, and when located on the suction side they decrease lift. In this study, microjets are considered for installation in the flaps of the high-lift version of the Common ResearchModel (CRM-HL). Two different architectures to supply the air for the microjets are considered: (1) bleed air from the airplane’s auxiliary power unit (APU) plus ram air, and (2) engine fan bleed air plus ram air. A model based on the 1D compressible flow equations is applied to analyze the air supply system architectures and predict the microjet flow rate with the resulting airplane performance changes based on Reynolds-averaged Navier-Stokes modeling of microjets on the CRM-HL. The results of this scoping study are encouraging in that pressurized air from the APU or the engine fan can be used to entrain ram air and thereby increase the AFC mass flow rate to achieve effective lift control and airplane performance enhancement during takeoff and landing.

AATT↗

NASA NTRS2009

Spreadsheets in Team X: Preserving Order in an Inherently Chaotic Environment

JPL is NASA's prime center for deep space missions. In response to the need to reduce the cost and time to complete early concept studies and proposals JPL created the first concurrent engineering team in the aerospace industry: Team X. Started in 1995, Team X has carried out over 800 studies, dramatically reducing the time and cost involved, and has been the model for other concurrent engineering teams both within NASA and throughout the larger aerospace community. Since its inception, the software backbone of this highly successful design team - engaged in examining some of NASA's cutting edge concepts - has been the unassuming spreadsheet. Over the years the Team X spreadsheet-based tools have evolved from simple standalone engineering models into a networked spreadsheet intensive system with real time parameter updating. Recent new capabilities include stochastic cost estimation and a graphical drag and drop block diagram that automatically populates the related spreadsheet parameters of cost, mass and power. This paper describes how the spreadsheet functions within Team X: its history, architecture, current capabilities, enabling strengths and persistent weaknesses. In addition, the verification methods and institutional oversight that have evolved as the Team X products became increasingly critical to Laboratory success are also discussed.

concurrent engineering↗

DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗

DOE OSTI2018

KiteFAST

KiteFAST is a simulator for airborne wind energy systems based on NREL's OpenFAST whole turbine simulator. KiteFAST is a multi-physics engineering tool for modeling the coupled aero-servo-elastic dynamics of airborne wind energy systems i.e. a wind-energy kite. The engineering model is like NREL's OpenFAST tool for land-based or offshore horizontal-axis wind turbines atop towers, in that KiteFAST models the important physical phenomena and system couplings, including the wind/aerodynamic excitation and full-system dynamics response (fuselage, wings, tail, stabilizers, pylons, nacelles, rotors, tether, ground station, control surfaces, and controller) under both normal (for fatigue) and extreme (for ultimate) loading conditions. The configuration of the wind-energy kite is assumed to have one fuselage; two wings (starboard, port) attached to the fuselage; one vertical stabilizer attached the fuselage; two horizontal stabilizers (starboard, port) attached to the vertical stabilizer; one or more pylons per wing, each with two rotors (top, bottom); one ground-station-fixed tether with a bridal connection to the wings, and nominal circular motion of the energy kite in the port (left) direction. The general configuration of the control surfaces assumes one or more flaps per wing, two rudders on the vertical stabilizer, two elevators per horizontal stabilizer, and rotors with variable speed and rotor-collective blade pitch. KiteFAST was developed from (1) the existing MBDyn open-source multi-body dynamics code; (2) the existing/modified OpenFAST modules NWTC Subroutine Library, AirfoilInfo, InflowWind, MoorDyn, and TurbSim; and (3) new source code for a) generating MBDyn models of the energy kite (KiteMBDyn Preprocessor), b) computing quasi-steady energy-kite aerodynamics (KiteAeroDyn, KiteAeroDyn Driver, KiteVSM, ActuatorDisk), c) interfacing an energy-kite controller, and d) coupling the various KiteFAST modules together (KiteFASTMBD).

Jonkman, Jason↗

DOE OSTI2017

Turbulence radiation coupling in boundary layers of heavy-duty diesel engines

The lack of accurate submodels for in-cylinder radiation and heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop combustion systems for advanced high-efficiency, low-emissions engines. Recent measurements of wall layers in engines show discrepancies of up to 100% with respect to standard CFD boundary-layer models. And recent analysis of in-cylinder radiation based on the most recent spectral property databases and high-fidelity radiative transfer equation (RTE) solvers has shown that at operating pressures and exhaust-gas recirculation levels typical of modern heavy-duty compression-ignition engines, radiative emission can be as high as 40% of the wall heat losses, that molecular gas radiation (mainly CO2 and H2O) can be more important than soot radiation, and that a significant fraction of the emitted radiation can be reabsorbed before reaching the walls. That is, radiation not only contributes to heat losses, but also changes the in-cylinder temperature distribution, which in turn affects combustion and emissions. The goal of this research is to develop models that explicitly account for the potentially strong coupling between radiative and turbulent boundary layer heat transfer. For example, for optically thick conditions, a simple diffusion model might be formulated in terms of an absorption-coefficient-dependent turbulent Prandtl number.

Sircar, Arpan↗

DOE OSTI2025

Multiscale Modeling of the Mechanical Response of Silicon Carbide Composite Within the Accelerated Fuel Qualification Framework

The accelerated fuel qualification (AFQ) framework has been used for the initial development of multiscale modeling of silicon carbide (SiC) fiber reinforced composite (SiC-SiC). The AFQ framework provides a methodology to leverage physics-informed multiscale modeling along with a reduced set of empirical test data to reduce the time and cost of licensing and qualification of new nuclear fuel systems while maintaining the overall nuclear power plant safety case. SiC-SiC is being proposed for in-core applications, most notably fuel cladding, for current and next-generation nuclear reactors because of its high temperature stability, irradiation tolerance, and ability to withstand many accident conditions. As these composites exhibit multiscale architectures and complex microstructure-based fracture mechanics, it is an appealing use case for the AFQ methodology. While the end goal of this work is a single multiscale model that can be used for predictive in-core performance, current focus is on the individual various length scale models. Four individual models have been initially developed from microscale to engineering system level to capture key physics-based effects across different length scales. These models include a microscale homogenized tow model, a mesoscale fast Fourier transform–based weave model that integrates the homogenized tow model, a mesoscale finite element–based weave model, and a system-level BISON fuel performance model. Results of these models have undergone an initial comparison with separate-effects test data showing a good match to experimental results. By using the AFQ framework during model development, several near-term benefits have been secured including a reduction in development time for the SiC-SiC cladding, more targeted irradiation testing, and a better understanding of uncertainty.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

An equivalent dissipation rate model for capturing history effects in non-premixed flames

The effects of strain rate history on turbulent flames have been studied in the. past decades with 1D counter flow diffusion flame (CFDF) configurations subjected to oscillating strain rates. In this work, these unsteady effects are studied for complex hydrocarbon fuel surrogates at engine relevant conditions with unsteady strain rates experienced by flamelets in a typical spray flame. Tabulated combustion models are based on a steady scalar dissipation rate (SDR) assumption and hence cannot capture these unsteady strain effects; even though they can capture the unsteady chemistry. In this work, 1D CFDF with varying strain rates are simulated using two different modeling approaches: steady SDR assumption and unsteady flamelet model. Comparative studies show that the history effects due to unsteady SDR are directly proportional to the temporal gradient of the SDR. A new equivalent SDR model based on the history of a flamelet is proposed. An averaging procedure is constructed such that the most recent histories are given higher weights. This equivalent SDR is then used with the steady SDR assumption in 1D flamelets. Results show a good agreement between tabulated flamelet solution and the unsteady flamelet results. This equivalent SDR concept is further implemented and compared against 3D spray flames (Engine Combustion Network Spray A). Tabulated models based on steady SDR assumption under-predict autoignition and flame lift-off when compared with an unsteady Representative Interactive Flamelet (RIF) model. However, equivalent SDR model coupled with the tabulated model predicted autoignition and flame lift-off very close to those reported by the RIF model. This model is further validated for a range of injection pressures for Spray A flames. The new modeling framework now enables tabulated models with significantly lower computational cost to account for unsteady history effects. (C) 2016 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Kundu, Prithwish↗

DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

A Composition-Based Approach for Predicting Hanford Tank Waste Liquid Viscosity

Liquid viscosity is an important Hanford waste physical property that governs, in part, the performance of key Hanford flowsheet unit operations and processes, including waste filtration, ion exchange, pumping and transport, and mixing. Evaluation and optimization of these processes rely on liquid viscosity correlations subject to substantial error (at least ±60%) that can lead to overly conservative, and sometimes physically unachievable, limits for validation and operation of these unit operations. The current liquid viscosity correlations used for the Hanford flowsheet attempt to capture the effects of specific waste chemistry (namely, the speciation of various salts) on liquid viscosity using liquid density alone. It is postulated that the ±60% or greater error associated with these “density-based” liquid viscosity predictions is driven in part by the inability of waste density to capture the full impact of waste chemistry on liquid viscosity, and that improved prediction accuracy could be achieved by implementing a composition-based approach to evaluating liquid viscosity for Hanford wastes. Recent publications, namely those of Lencka et al. (1998), Hu (2004), and Laliberté (2007a), provide correlations and comprehensive, consolidated databases of single-salt solution viscosity that enable composition-based predictions for Hanford-type liquids (i.e., concentrated multi-component aqueous electrolyte solutions). In the current study, the viscosities of Hanford wastes and waste simulants were predicted using a composition-based model developed by Laliberté (2007a) (hereafter referred to as the “Laliberté viscosity model”) to evaluate its accuracy relative to the current density-based models used to predict waste viscosity for engineering applications at the Hanford Site. A viscosity database that captured the composition and viscosity of wastes and waste simulants was developed to support this evaluation. To facilitate application of the Laliberté viscosity model, waste and waste simulant compositions were converted to an equivalent salt recipe composed of salts with known viscosity coefficients using a number of simplifying assumptions that have the potential to impact the fidelity of the model prediction. Despite these simplifications, the Laliberté viscosity model was able to predict viscosity more accurately than the density-based correlations currently available for Hanford flowsheet use, such as that proposed in RPP-RPT- 51652. The use of composition-based viscosity models, like that proposed by Laliberté (2007a), shows promise for leading to improved prediction of liquid waste viscosity. As implemented in this study, application of the Laliberté viscosity model could lead to a decrease in the uncertainty associated with liquid viscosity prediction by as much as a factor of two, allowing for reduction in unit operation design conservatism and for the more realistic selection of test simulants for flowsheet process evaluation and optimization, all of which could potentially reduce flowsheet design margins, lower costs and reduce duration of the Hanford mission, and reduce Hanford flowsheet risks.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1990

An improved approach for flight readiness assessment

An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft.

Moore, N. R.↗

NASA NTRS1987

A report on NASA software engineering and Ada training requirements

NASA's software engineering and Ada skill base are assessed and information that may result in new models for software engineering, Ada training plans, and curricula are provided. A quantitative assessment which reflects the requirements for software engineering and Ada training across NASA is provided. A recommended implementation plan including a suggested curriculum with associated duration per course and suggested means of delivery is also provided. The distinction between education and training is made. Although it was directed to focus on NASA's need for the latter, the key relationships to software engineering education are also identified. A rationale and strategy for implementing a life cycle education and training program are detailed in support of improved software engineering practices and the transition to Ada.

Legrand, Sue↗