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At least 793 records · Page 44

Model Verification and Validation Concepts for a Probabilistic Fracture Assessment Model to Predict Cracking of Knife Edge Seals in the Space Shuttle Main Engine High Pressure Oxidizer

Physics-based models are routinely used to predict the performance of engineered systems to make decisions such as when to retire system components, how to extend the life of an aging system, or if a new design will be safe or available. Model verification and validation (V&V) is a process to establish credibility in model predictions. Ideally, carefully controlled validation experiments will be designed and performed to validate models or submodels. In reality, time and cost constraints limit experiments and even model development. This paper describes elements of model V&V during the development and application of a probabilistic fracture assessment model to predict cracking in space shuttle main engine high-pressure oxidizer turbopump knife-edge seals. The objective of this effort was to assess the probability of initiating and growing a crack to a specified failure length in specific flight units for different usage and inspection scenarios. The probabilistic fracture assessment model developed in this investigation combined a series of submodels describing the usage, temperature history, flutter tendencies, tooth stresses and numbers of cycles, fatigue cracking, nondestructive inspection, and finally the probability of failure. The analysis accounted for unit-to-unit variations in temperature, flutter limit state, flutter stress magnitude, and fatigue life properties. The investigation focused on the calculation of relative risk rather than absolute risk between the usage scenarios. Verification predictions were first performed for three units with known usage and cracking histories to establish credibility in the model predictions. Then, numerous predictions were performed for an assortment of operating units that had flown recently or that were projected for future flights. Calculations were performed using two NASA-developed software tools: NESSUS(Registered Trademark) for the probabilistic analysis, and NASGRO(Registered Trademark) for the fracture mechanics analysis. The goal of these predictions was to provide additional information to guide decisions on the potential of reusing existing and installed units prior to the new design certification.

Pai, Shantaram S.↗

Modelling the development of capillary pressure in freshly 3D-printed concrete elements

3D concrete printing is a promising technology recently developed to automate construction. Since no formwork is used in this technology to support and protect fresh concrete, there are two aspects which considerably accelerate the development of capillary pressure in 3D-printed concrete in comparison to conventionally placed concrete: i) high stiffness of 3D-printed needed to provide sufficient buildability, and ii) very early and fast evaporation of pore water. Accelerated development of capillary pressure may lead to severe plastic shrinkage cracking in 3D-printed elements and, hence, need to be mitigated. This investigation aims at providing a poromechanical model for capillary pressure development in 3D-printed elements. To simulate the development of capillary pressure and plastic shrinkage, environmental factors, material properties, and element geometry need to be considered as a whole. The model inputs – coefficient of permeability, static bulk modulus, air entry pressure and chemical shrinkage rate – were determined experimentally. The model was validated for two fine-grained concretes. Both 3D-printed materials yielded faster capillary pressure increase in comparison to cast concrete, while partial substitution of cement with silica fume further accelerated the capillary pressure development. Furthermore, due to the lower permeability of the mixture containing silica fume, the gradient of capillary pressure between 3D-printed layers increased, as did the gradient of plastic shrinkage.

36 MATERIALS SCIENCE↗

Development, Demonstration and Validation of Data-Driven Compact Diode Models for Circuit Simulation and Analysis

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (e.g., radiation effects) into an existing model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2) Generalized Moving Least-Squares, and (3) feedforward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.

1N4148 switching diode↗

Overview of NASA MSFC and UAH Space Weather Modeling and Data Efforts

Marshall Space Flight Center, along with its industry and academia neighbors, has a long history of space environment model development and testing. Space weather efforts include research, testing, model development, environment definition, anomaly investigation, and operational support. This presentation will highlight a few of the current space weather activities being performed at Marshall and through collaborative efforts with University of Alabama in Huntsville scientists.

Parker, Linda Neergaard↗

Development of the INL Thermal Energy Distribution System (TEDS) in the Modelica Eco-System for Validation and Verification

This report provides an overview on the development of the Thermal Energy Distribution System (TEDS) in the Modelica process model eco-system at Idaho National Laboratory as part of the integrated energy system initiative. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Engineering Science Laboratory (ESL) at INL. The model was then used during the pre-construction phase of the experimental effort to inform experimental design (insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit including: a 200kW Chromalox heater, a single-tank packed bed thermal energy storage system filled with 0.125 inch Alumina (Al 2 O 3 ), an ethylene-glycol to Therminol-66 heat exchanger, system piping, six control valves, and all associated temperature, pressure, and mass flow sensors. The model does not include nitrogen fill gas tanks, or associated overfill tanks, as these are not part of standard system control. Two simulation sets were run. The first is a five-hour test that operates as a shakedown test for the facility. This simulation puts the facility through all five potential operating modes and showcases the ability of valving, control sensors, and component controllers to meet the system demands. The second case imposes a typical summer day demand on the system from a region with mixed commercial and residential electrical needs where the generator alone cannot meet peak demand but instead requires the thermal storage unit to act as a peaking unit. Over the course of the fifteen-hour discharge cycle heat losses account for a heat loss of approximately 15°C in outlet temperature. Such heat losses would lead to a decrease in overall system efficiency and may lead to additional design changes for such designs in the future that may include a topping heater. Through commencement of this work, a systems level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model has been utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Once TEDS begins operation, this model will be refined, tuned, and used for validation and verification purposes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Developing Soft Skill in the Geosciences: NASA DEVELOP’s Approach to Strengthening Soft Skills in the Future Geospatial Workforce

Part of NASA’s Applied Sciences, the DEVELOP Program bridges the gap between NASA Earth Science and potential users through rapid feasibility studies that apply Earth observations to environmental decision making needs. The program conducts 50-60 studies a year that engage participants – students, recent graduates, early career individuals, and transitioning career professionals – in autonomous teamwork with an emphasis on professional development and communication. The experience offers participants a robust opportunity to build their teaming skills (facilitated through programmatic personality assessments and discussions), interpersonal communication, and critical thinking skills which are central to project work in the DEVELOP model. The program also offers leadership opportunities where participants lead their peers within a supportive environment. This presentation will introduce the DEVELOP model for soft skill development, lessons learned, and highlight testimonials of past participants.

Amanda Clayton↗

Statistical Analysis of CO 2 Exposed Wells to Predict Long Term Leakage through the Development of an Integrated Neural-Genetic Algorithm

The objective of this project is to develop a computerized statistical model with the Integrated Neural-Genetic Algorithm (INGA) for predicting the probability of long-term leak of wells in CO 2 sequestration operations. This object has been accomplished by conducting research in three phases: 1) data mining of CO 2 -explosed wells, 2) INGA computer model development, and 3) evaluation of the predictive performance of the computer model with data from field tests. Data mining was conducted for 510 wells in two CO 2 sequestration projects in the Texas Gulf Coast region. They are the Hasting West field and Oyster Bayou field in the Southern Texas. Missing wellbore integrity data were estimated using an analytical and Finite Element Method (FEM) model. The INGA was first tested for performances of convergence and computing efficiency with the obtained data set of high dimension. It was concluded that the INGA can handle the gathered data set with good accuracy and reasonable computing time after a reduction of dimension with a grouping mechanism. A computerized statistical model with the INGA was then developed based on data pre-processing and grouping. Comprehensive training and testing of the model were carried out to ensure that the model is accurate and efficient enough for predicting the probability of long-term leak of wells in CO 2 sequestration operations. The Cranfield in the southern Mississippi was select as the test site. Observation wells CFU31F2 and CFU31F3 were used for pressure-testing, formation-logging, and cement-sampling. Tools run in the wells include Isolation Scanner, Slim Cement Mapping Tool (SCMT), Cased Hole Formation Dynamics Tester (CHDT), and Mechanical Sidewall Coring Tool (MSCT). Analyses of the obtained data indicate no leak of CO 2 cross the cap zone while it is evident that the well cement sheath was invaded by the CO 2 from the storage zone. This observation is consistent with the result predicted by the INGA model which indicates the well has a CO 2 leak-safe probability of 72%. This comparison implies that the developed INGA model is valid for future use in predicting well leak probability.

97 MATHEMATICS AND COMPUTING↗

Development of mathematical models for solid state switching devices

Models are developed for two types of remote power controllers (RPC). The models give the equations for the currents and voltages for all elements of passive loads as a function of time for both turn-on and turn-off. It is shown that the RPC can be considered as a combination of current and voltage sources. Equations are given for these sources which are essentially independent of the load being turned on and off. Experimental results are given for several types of loads and comparisons are made with the results obtained using the models.

Raburn, W. D.↗

Advanced development of atmospheric models

A set of atmospheric analysis and prediction models was developed in support of the SEASAT Program existing objective analysis models which utilize a 125x125 polar stereographic grid of the Northern Hemisphere, which were modified in order to incorporate and assess the impact of (real or simulated) satellite data in the analysis of a two-day meteorological scenario in January 1979. Program/procedural changes included: (1) a provision to utilize winds in the sea level pressure and multi-level height analyses (1000-100 MBS); (2) The capability to perform a pre-analysis at two control levels (1000 MBS and 250 MBS); (3) a greater degree of wind- and mass-field coupling, especially at these controls levels; (4) an improved facility to bogus the analyses based on results of the preanalysis; and (5) a provision to utilize (SIRS) satellite thickness values and cloud motion vectors in the multi-level height analysis.

Kesel, P. G.↗

A prototype DSN X/S-band feed: Model 3 development

The development of a prototype X/S-band common aperture Cassegrain feedhorn for Deep Space Network (DSN) use is discussed. A Model 3 combiner has been developed to increase S-band bandwidth to include the Highly Elliptical Earth Orbiter band from 2025 to 2110 MHz, and to provide a 400-kW CW S-band uplink and a possible planetary radar band near 2320 MHz. The combiner uses eight S-band waveguide injection slots arranged in four pairs. The problems of this design geometry associated with rejection filtering and X/S-band matching are discussed.

Bathker, D. A.↗

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

17 WIND ENERGY↗

A Newly Developed Fluorescence Model for C2H6 v5 and Application to Cometary Spectra Acquired with NIRSPEC at Keck II

Accurate rotational temperatures are essential for extracting production rates for parent volatiles in comets. Two strong bands of ethane (v7 at 2985.39/cm and v5 at 2895.67/cm) are seen in infrared cometary spectra, but the Q-branches of v7 are not resolved by current instruments and cannot provide an accurate rotational temperature with current models.We developed a fluorescence model for the C2H6 v5 band that can be used to derive a rotational temperature.We applied our C2H6 5 model to high-resolution infrared spectra of the comets C/2004 Q2 Machholz and C/2000 WM1 (LINEAR), acquired with the Near-infrared Echelle Spectrograph on the Keck II telescope. We demonstrate agreement among the rotational temperatures derived from C2H6 v5 and other species, and between mixing ratios derived from C2H6 v5 and C2H6 v7. As a symmetric hydrocarbon, C2H6 is observed only in the infrared, and it is now the fifth molecule (along with H2O, HCN, CO, and H2CO) for which we can derive a reliable rotational temperature from cometary infrared spectra.

Radeva, Yana L.↗

CMB-S4 Instrument Modeling and Development for CMB-S4

During this thirteen month award, we focused on two main tasks relevant to the development of the CMB-S4 project: (1) The refinement of sensitivity models and use of those to optimize the instrument design, and (2) the final development and testing a cryogenic blackbody calibrator prototype, of which we then produced three copies for CMB-S4's detector testing systems at FNAL, SLAC and UIUC.

47 OTHER INSTRUMENTATION↗

Some Recent Developments in Numerical Modelling at ECMWF

A new atmospheric model was introduced into operational forecasting at the European Center for Medium Range Weather Forecasts, on 21 April 1983. The principal differences between this model and the Center's first operational model were in the adiabatic formulation, which, in the new model, includes use of a spectral representation in the horizontal, a more general vertical coordinate, and a modified, more-efficient, time-stepping scheme. In addition, new programming techniques and standards were adopted to facilitate both the model's use as a research tool and its adaptation to make full use of the features of the recently-acquired CRAY X-MP computer. A number of revisions were also made in detailed aspects of the formulation of the parameterization schemes. The operational change to this new model was accompanied by a second important change, namely the use of a higher envelope orography in the lower boundary conditions of the model. Problems in the operational performance of the model and development preparatory to the future operational implementation of a high resolution version of the model on the CRAY X-MP are discussed.

Simmons, A. J.↗

A Comparison of Two Balance Calibration Model Building Methods

Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.

DeLoach, Richard↗

Microwave remote sensing and radar polarization signatures of natural fields

Theoretical models developed for simulation of microwave remote sensing of the Earth surface from airborne/spaceborne sensors are described. Theoretical model calculations were performed and the results were compared with data of field measurements. Data studied included polarimetric images at the frequencies of P band, L band, and C band, acquired with airborne polarimeters over a agricultural field test site. Radar polarization signatures from bare soil surfaces and from tree covered fields were obtained from the data. The models developed in this report include: (1) Small perturbation model of wave scatterings from randomly rough surfaces, (2) Physical optics model, (3) Geometrical optics model, and (4) Electromagnetic wave scattering from dielectric cylinders of finite lengths, which replace the trees and branches in the modeling of tree covered field. Additionally, a three-layer emissivity model for passive sensing of a vegetation covered soil surface is also developed. The effects of surface roughness, soil moisture contents, and tree parameters on the polarization signatures were investigated.

Mo, Tsan↗

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗