Results for “model validation” — Page 52

DOE OSTI2023

Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions

Abstract To generate material models with fewer limiting assumptions while maintaining closed-form, interpretable solutions, we propose using genetic programming based symbolic regression (GPSR), a machine learning (ML) approach that describes data using free-form symbolic expressions. To maximize interpretability, we start from an analytical, derived material model, the Gurson model for porous ductile metals, and systematically relax inherent assumptions made in its derivation to understand each assumption’s contribution to the GPSR model forms. We incorporate transfer learning methods into the GPSR training process to increase GPSR efficiency and generate models that abide by known mechanics of the system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the generation of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Defect Kinetics and Control for Module Reliability

Potential induced degradation is currently one of the most important module degradation mechanisms. It has been suggested that stacking faults decorated with sodium from the module glass are responsible for this effect and authors have also shown the reversibility of this effect upon reverse biasing of the module. The importance of sodium in the failure mechanism is clear, however, little is known regarding the factors that control its diffusion into the wafer, making it nearly impossible to predict the performance of a given module and engineer it to be better. Sodium migration from module glass into silicon cells and the resulting module degradation is a clear example of how defect kinetics can determine overall module performance and long-term reliability. To the detriment of the industry and its bankability, no quantitative models yet exist to predict defect-assisted module degradation, limiting the progress in improving reliability. In particular, the understanding of defect behavior under high electric fields, under stresses imparted by encapsulation or temperature, and under real operating conditions over long periods of time is a crucial gap in the current state-of-the-art. In this work we developed a Defect-Device-Degradation model to predict defect behavior and its impact on device performance over the module operational lifetime using experimentally-determined defect parameterizations. The validated model will provide a platform for manufacturing process optimization across input materials and architectures to avoid deleterious defects upstream and enable enhanced module robustness.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Demonstration of a 10-m Solar Sail System

The NASA In-Space Propulsion (ISP) program has been sponsoring system design development and hardware demonstration activities of solar sail technology over the past 16 months. Efforts to validate by test a moderate-scale (10-m) 1/4 symmetry ground demonstration sail system are nearly complete. Results of testing and analytical model validation of component and assembly functional, strength, stiffness, shape, and dynamic behavior are discussed.

Murphy, David M.↗

NASA NTRS2008

Estimating the Need for Medical Intervention due to Sleep Disruption on the International Space Station

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

Myers, Jerry G.↗

NASA NTRS2017

Models of Sector Flows Under Local, Regional and Airport Weather Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗

NASA NTRS2017

Models of Sector Aircraft Counts in the Presence of Local, Regional and Airport Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗

NASA NTRS1989

Development and validation of CNS (compressible Navier-Stokes) for hypersonic external flows

CNS, a new computational fluid dynamics procedure, has been developed to aid in hypersonic vehicle design. The code can be used to model the entire external flow around hypersonic vehicle shapes, from the captured shock at the nose to the beginning of the wake. Unlike space-marching codes, the technique allows axially separated flow regions to be modeled. Validation trials using sphere-cone data reveal good solution accuracy for the surface pressure and flowfield temperature.

Flores, Jolen↗

DOE OSTI2019

Development and Validation of Predictive Models for In-Cylinder Radiation and Wall Heat Transfer

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 and design combustion systems for advanced high-efficiency, low-emissions engines. Measurements of wall boundary layers in engines show discrepancies of up to 100% with respect to standard CFD boundary layer models. And analysis of in-cylinder radiation based on the most recent spectral property databases and high-fidelity radiative transfer equation solvers has shown that at operating pressures and exhaust-gas recirculation levels typical of modern compression-ignition truck engines, molecular gas radiation (mainly carbon dioxide and water vapor) can be more important than soot particle radiation, and that a significant fraction of the emitted radiation (50% or more) can be reabsorbed before reaching the walls, thereby acting to redistribute energy in the combustion chamber. In this project, an integrated experimental and simulation approach has been used to generate new physical insight into in-cylinder heat-transfer processes in engines, and to develop computationally practicable CFD-based models that accurately represent the physics. New experimental data have been taken in an optically accessible single-cylinder research engine, and in a multi-cylinder production engine. Those data have provided new insight into the radiative environment in engines. New CFD models have been developed and validated for in-cylinder heat transfer, focusing mainly on radiative heat transfer and coupling between turbulent boundary layer wall heat transfer and radiative heat transfer. The CFD models have been developed and implemented in an open-source software framework that can be shared with other researchers. The models are compatible both with unsteady Reynolds-averaged Navier Stokes (URANS) formulations and with large-eddy simulations (LES) of in-cylinder processes in engines.

02 PETROLEUM↗

NASA NTRS2016

UAS in the NAS Project: Large-Scale Communication Architecture Simulations with NASA GRC Gen5 Radio Model

This report provides a description and performance characterization of the large-scale, Relay architecture, UAS communications simulation capability developed for the NASA GRC, UAS in the NAS Project. The system uses a validated model of the GRC Gen5 CNPC, Flight-Test Radio model. Contained in the report is a description of the simulation system and its model components, recent changes made to the system to improve performance, descriptions and objectives of sample simulations used for test and verification, and a sampling and observations of results and performance data.

avionics↗

DOE OSTI2025

Informed Investments in Clean Energy Technologies

Governments and companies face consequential decisions about allocating resources to the research, development, demonstration and deployment of energy technologies to meet environmental, economic and social goals. Here we discuss how research insights can inform and potentially improve these decisions to make effective use of limited resources and time in shaping the next-generation energy infrastructure. We outline three key research steps: forecasting technological change, relating investments to economic, social and environmental outcomes and informing decision-making processes. We recommend advances to address uncertainty as well as to make methods and results more practicable, emphasizing the importance of model validation, streamlining and interactivity. Progress has been made, yet further work is needed-for example, in the development of reduced-order, testable models and more comprehensive data collection. Overall, this research is beginning to inform decisions but could be adopted more widely by governments and the private sector to help support technological progress for energy affordability, equitable climate change mitigation, health benefits and other objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Baseline process description for simulating plutonium oxide production for precalc project

Savannah River National Laboratory (SRNL) started a multi-year project, the PreCalc Project, to develop a computational simulation of a plutonium oxide (PuO 2 ) production facility with the objective to study the fundamental relationships between morphological and physicochemical properties. This report provides a detailed baseline process description to be used by SRNL personnel and collaborators to facilitate the initial design and construction of the simulation. The PreCalc Project team selected the HB-Line Plutonium Finishing Facility as the basis for a nominal baseline process since the facility is operational and significant model validation data can be obtained. The process boundary as well as process and facility design details necessary for multi-scale, multi-physics models are provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

New Mechanistic Models of Long Term Evolution of Microstructure and Mechanical Properties of Nickel Based Alloys

The report describes the development of a discrete element method (DEM) based modeling approach to quantitatively predict deformation and failure of typical nickel based superalloys. A series of experimental data, including microstructure and mechanical property characterization at 600°C, was collected for a relatively simple, model solid solution Ni-20Cr alloy (Nimonic 75) to determine inputs for the model and provide data for model validation. Nimonic 75 was considered ideal for this study because it is a certified tensile and creep reference material. A series of new DEM modeling approaches were developed to capture the complexity of metal deformation, including cubic elastic anisotropy and plastic deformation both with and without strain hardening. Our model approaches were implemented into a commercially available DEM code, PFC3D, that is commonly used by engineers. It is envisioned that once further developed, this new DEM modeling approach can be adapted to a wide range of engineering applications.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Validation of Wind Power Plant Modeling Approaches in Complex Terrain: Preprint

The effect of terrain on wind-plant aerodynamics is often neglected due to the complex dynamics that make up terrain-atmosphere-turbine interactions and the computational effort required to adequately resolve the relevant physics. In this work, a comprehensive set of measurements (from the Wind Forecast Improvement Project 2) and two distinct modeling approaches are used to investigate the effects of topography on plant performance. The first approach consists of high-fidelity large-eddy simulations conducted for a one- hour period on a terrain-resolving mesh, and producing unsteady, heterogeneous flow that is sensitive to the local topography. The second approach makes use of a steady-state engineering model that has been modified to account for terrain effects. In situ and remote- sensing measurements upstream of the wind plant are used to prescribe initial and boundary conditions to the models, while wind turbine power measurements are used to assess the ability of the different models to capture the effects of terrain on turbine wake dynamics and therefore power production. The full paper will quantify the degree to which our high-fidelity and engineering models are able to capture realistic wind plant aerodynamics in complex terrain, and through improved understanding of the underlying physics, inform our model development efforts.

17 WIND ENERGY↗

NASA NTRS2007

POST2 End-To-End Descent and Landing Simulation for the Autonomous Landing and Hazard Avoidance Technology Project

The Program to Optimize Simulated Trajectories II (POST2) is used as a basis for an end-to-end descent and landing trajectory simulation that is essential in determining the design and performance capability of lunar descent and landing system models and lunar environment models for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. This POST2-based ALHAT simulation provides descent and landing simulation capability by integrating lunar environment and lander system models (including terrain, sensor, guidance, navigation, and control models), along with the data necessary to design and operate a landing system for robotic, human, and cargo lunar-landing success. This paper presents the current and planned development and model validation of the POST2-based end-to-end trajectory simulation used for the testing, performance and evaluation of ALHAT project system and models.

Fisher, Jody l.↗

NASA NTRS2011

System Identification Applied to Dynamic CFD Simulation and Wind Tunnel Data

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed

Murphy, Patrick C.↗

NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Chamber B Thermal Vacuum “Suit 2” Pressure Garment System Test Article Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test results from the “Suit 2” Pressure Garment System (PGS) test article. The primary objective of the “Suit 2” PGS test article was to evaluate system-level suit heat leak and Environmental Protection Garment (EPG) thermal performance. Eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and a heater cage surrounding the test article. This paper will principally focus on system-level thermal results from the “Suit 2” PGS test article. This paper will examine data collected from one-hundred and seventy thermocouples located in critical locations inside and outside of the suit, as well as seven resistance thermometers (RTDs) for calorimetry to determine total heat flux in and out of the suit. The test data will be compared against the system-level PGS thermal models for model validation. To conclude, this paper will address knowledge gaps presented by unmanned xPGS thermal vacuum testing and the current state of lunar xPGS thermal modeling and testing.

xEMU↗

DOE OSTI2023

Energy Model to Evaluate Thermal Energy Storage Integrated with Air Source Heat Pumps: Preprint

Full decarbonization in buildings requires the replacement of combustion appliances with electric ones, and air source heat pumps (ASHP) are a candidate alternative. However, technical limitations, such as the efficiency decrease when operating in cold weather, limit their adoption in the global heating market. Among several options to improve ASHP efficiency operating in colder climates, thermal energy storage (TES) has been considered, as it may provide heating when it is cold and shift ASHP operation to times when the weather is warmer. It may also take advantage of time of use electricity rates and support defrosting when necessary. The evaluation of ASHP-TES systems, however, is still limited because traditional metrics do not capture their full economic and environmental benefits. In this work, a python framework is presented to model the ASHP with and without the presence of TES. Metrics are proposed to analyze the system performance in terms of costs, equivalent CO2 emission, and efficiency metrics to evaluate and compare alternative systems. Model validation against experimental data obtained for a commercial heat pump is provided, as well as an application example using Denver, Colorado, to highlight the model capabilities.

cold climate↗

NASA NTRS2017

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment −PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

forecasting↗