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Results for “Model validation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Ultrasonic power transfer from a spherical acoustic wave source to a free-free piezoelectric receiver: Modeling and experiment

Contactless powering of small electronic components has lately received growing attention for wireless applications in which battery replacement or tethered charging is undesired or simply impossible, and ambient energy harvesting is not a viable solution. As an alternative to well-studied methods of contactless energy transfer, such as the inductive coupling method, the use of ultrasonic waves transmitted and received by piezoelectric devices enables larger power transmission distances, which is critical especially for deep-implanted electronic devices. Moreover, energy transfer by means of acoustic waves is well suited in situations where no electromagnetic fields are allowed. The limited literature of ultrasonic acoustic energy transfer is mainly centered on proof-of-concept experiments demonstrating the feasibility of this method, lacking experimentally validated modeling efforts for the resulting multiphysics problem that couples the source and receiver dynamics with domain acoustics. In this work, we present fully coupled analytical, numerical, and experimental multiphysics investigations for ultrasonic acoustic energy transfer from a spherical wave source to a piezoelectric receiver bar that operates in the 33-mode of piezoelectricity. The fluid-loaded piezoelectric receiver under free-free mechanical boundary conditions is shunted to an electrical load for quantifying the electrical power output for a given acoustic source strength of the transmitter. The analytical acoustic-piezoelectric structure interaction modeling framework is validated experimentally, and the effects of system parameters are reported along with optimal electrical loading and frequency conditions of the receiver.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Predictive Model for Particle Residence Time Distributions in Riser Reactors. Part 1: Model Development and Validation

Here in this computational study, we model the mixing of biomass pyrolysis vapor with solid catalyst in circulating riser reactors with a focus on the determination of solid catalyst residence time distributions (RTDs). A comprehensive set of 2D and 3D simulations were conducted for a pilot-scale riser using the Eulerian-Eulerian two-fluid modeling framework with and without sub-grid-scale models for the gas-solids interaction. A validation test case was also simulated and compared to experiments, showing agreement in the pressure gradient and RTD mean and spread. For simulation cases, it was found that for accurate RTD prediction, the Johnson and Jackson partial slip solids boundary condition was required for all models and a sub-grid model is useful so that ultra high resolutions grids that are very computationally intensive are not required. Finally, we discovered a 2/3 scaling relation for the RTD mean and spread when comparing resolved 2D simulations to validated unresolved 3D sub-grid-scale model simulations.

09 BIOMASS FUELS↗
DOE OSTI2023

PV Performance Modeling - Data and Resources

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. This dataset includes seven MS-Excel sheets with instructions, notes and all necessary data (weather, irradiance, temperature, power) used for the data analysis of the blind modeling comparison. The hourly data represent six different systems from Albuquerque, NM and Roskilde, Denmark over a period of one year. These data are useful for PV performance model validation studies.

14 SOLAR ENERGY↗
DOE OSTI2017

Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY

The dynamics of globular proteins can be described in terms of transitions between a folded native state and less-populated intermediates, or excited states, which can play critical roles in both protein folding and function. Excited states are by definition transient species, and therefore are difficult to characterize using current experimental techniques. We report an atomistic model of the excited state ensemble of a stabilized mutant of an extensively studied flavodoxin fold protein CheY. We employed a hybrid simulation and experimental approach in which an aggregate 42 milliseconds of all-atom molecular dynamics were used as an informative prior for the structure of the excited state ensemble. The resulting prior was then refined against small-angle X-ray scattering (SAXS) data employing an established method (EROS). The most striking feature of the resulting excited state ensemble was an unstructured N-terminus stabilized by non-native contacts in a conformation that is topologically simpler than the native state. We then predict incisive single molecule FRET experiments, using these results, as a means of model validation. Our study demonstrates the paradigm of uniting simulation and experiment in a statistical model to study the structure of protein excited states and rationally design validating experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A High-Pressure Shock Tube Study of Hydrogen and Ammonia Addition to Natural Gas for Reduced Carbon Emissions in Power Generation Gas Turbines

Ignition delay times from undiluted mixtures of natural gas (NG)/H2/Air and NG/NH3/Air were measured using a high-pressure shock tube at the University of Central Florida. The combustion temperatures were experimentally tested between 1000-1500 K near a constant pressure of 25 bar. Mixtures were kept undiluted to replicate the same chemistry pathways seen in gas turbine combustion chambers. Recorded combustion pressures exceeded 200 bar due to the large energy release, hence why these were performed at the high-pressure shock tube facility. The data is compared to the predictions of the NUIGMech 1.1 mechanism for chemical kinetic model validation and refinement. An exceptional agreement was shown for stoichiometric conditions in all cases but strayed at lean and rich equivalence ratios, especially in the lower temperature regime of H2 addition and all temperature ranges of the baseline NG mixture. Hydrogen addition also decreased ignition delay times by nearly 90%, while NH3 fuel addition made no noticeable difference in ignition delay time. NG/NH3 exhibited similar chemistry to pure NG under the same conditions, which is shown in a sensitivity analysis, demonstrating hydrogen chemistry to be dominant in NG/H2 mixtures and hydrocarbon chemistry to be dominant in NG/NH3 mixtures. The reaction CH3 + O2 = CH3O + O is identified and suggested as a possible modification target to improve model performance. Increasing the robustness of chemical kinetic models via experimental validation will directly aid in designing next-generation combustion chambers for use in gas turbines, which in turn will greatly lower global emissions and reduce greenhouse effects.

Pierro, Michael↗
DOE OSTI2017

Calibration of COBRA-TF and STAR-CCM+ with Dakota 6.5 for CASL

High-to-low (Hi2Lo) is utilizing a validated model in a high resolution code to generate synthetic data for improvement of the same model in a lower resolution code. This process is useful in cases where experimental data does not exist or is not sufficient. Data from the high resolution code is treated as calibration data (with appropriate uncertainties and error bounds) which can be used to train parameters that affect solution accuracy in the lower resolution code model and reduce uncertainty. Implementing the Hi2Lo process between COBRA-TF (CTF) and STAR-CCM+ (STAR) within a Dakota 6.5 framework is the goal of this work. High resolution results provided by STAR will eventually be used in addition to experimental data from Westinghouse Electric Company (WEC) to reduce uncertainty in the CTF model. The scope of the work presented herein is a proof of concept for deterministic calibration using Dakota and is not a demonstration of the Hi2Lo process. CTF temperatures will be calibrated to WEC experimental data as a first demonstration of the calibration process. Validation of the CTF model with experimental data was performed previously.

42 ENGINEERING↗
DOE OSTI2018

An Overview of the EGS Collab Project: Field Validation of Coupled Process Modeling of Fracturing and Fluid Flow at the Sanford Underground Research Facility, Lead, SD

Enhanced Geothermal Systems (EGS) development will require an ability to accurately predict the flow rates and temperatures of the production wells. While simple in concept, complex heterogeneous fracture pathways can lead to channeling, short-circuiting, and premature thermal breakthrough complicating EGS. The EGS Collab project will establish a suite of intermediate-scale (~10-20 m) field test beds coupled with stimulation and interwell flow tests to provide a basis to better understand the fracture geometries and processes that control heat transfer between rock and stimulated fractures. These experiments will provide a means of testing tools, codes, and concepts that could later be employed under geothermal reservoir conditions at FORGE and in EGS. We will perform well-controlled, in situ experiments focused on rock fracture behavior and permeability enhancement. Pre- and post-test modeling of each test will allow for model prediction and validation. Comprehensive instrumentation will be used to collect high-quality, high-resolution geophysical and fracture characterization and fluid flow data. ,These data will be analyzed and compared with models and field observations to further elucidate the basic relationships between stress, induced seismicity, and permeability enhancement. To the maximum extent achievable, we will observe and quantify other key governing parameters that impact permeability, and attempt to understand how these parameters might change throughout the development and operation of an EGS project with the goal of enabling commercial viability of EGS. The Sanford Underground Research Facility (SURF) in South Dakota was selected as the EGS Collab project experimental site based on the evaluation of information from several sites. Our team has designed and constructed the first field experiment planned for this project, which is supported by the US Department of Energy’s Geothermal Technologies Office.

EGS, FORGE, field fracture stimulation and flow ex↗
DOE OSTI2021

Effect of Soil Properties and Input Motion on Site Amplification Using Validated Nonlinear Soil Model

Here, three-dimensional (3-D) nonlinear site response analyses are conducted using finite element models of actual soil profiles from ten nuclear power plant (NPP) sites in the United States to investigate the effects of soil properties and input motions on site amplification. The modeling approach developed in this study combines several novel elements, such as 3-D analysis (including vertical motions), nonlinear inelastic behavior of soil (strain-dependent shear modulus reduction and hysteretic damping), formulation of nonreflecting boundary conditions at the base, and generation of realistic outcrop ground motions for specific sites. All these elements of the modeling approach are first validated using actual data from five earthquakes at three downhole array stations recorded in the Kiban-Kyoshin network (KiK-net), Japan. The same approach is then used to develop site models of ten NPP sites in the United States and corresponding ground motions that are spectrally matched to the site hazard spectra. Eight sets of three-component input motions are used in the study and are categorized on the basis of presence or absence of a near-field pulse in the seed ground motions used for spectral matching. It is found that all sites retain a definite site amplification function regardless of the input motion, provided that the seed motion is spectrally matched to the site hazard spectra. The magnitude of site amplification and frequencies at which they occur depend upon soil properties, particularly the shear wave velocity profile and the constitutive relationship (strain-dependent shear modulus reduction and hysteretic damping) of soil. Amplification of spectral acceleration in the vertical direction (up-down motion) is found to be just as much as, if not more than, the amplification in the horizontal direction. Peak shear strain is found to be about 20% larger for near-field motions compared to far-field motions whereas maximum horizontal site amplification for far-field motions is found to be consistently larger than that of near-field motions, even though the differences between the two remain within the scatter resulting from individual ground motions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions

Computational models for simulating physical phenomena during laser-based powder bed fusion additive manufacturing (L-PBF AM) processes are critical for enhancing our understanding of these phenomena, enable process optimization, and accelerate qualification and certification of AM materials and parts. It is a well-known fact that such models typically involve multiple sources of uncertainty that originate from different sources such as model parameters uncertainty, or model/code inadequacy, among many others. Uncertainty quantification (UQ) is a broad field that focuses on characterizing such uncertainties in order to maximize the benefit of these models. Although UQ has been a center theme in computational models associated with diverse fields such as computational fluid dynamics and macro-economics, it has not yet been fully exploited with computational models for advanced manufacturing. The current study introduces one among the first efforts to conduct uncertainty propagation (UP) analysis in the context of L-PBF AM. More specifically, we present a generalized polynomial chaos expansions (gPCE) framework to assess the distributions of melt pool dimensions due to uncertainty in input model parameters. We develop the methodology and then employ it to validate model predictions, both through benchmarking them against Monte Carlo (MC) methods and against experimental data acquired from an experimental testbed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Validation of implosion modeling through direct-drive shock timing experiments at the National Ignition Facility

Precise modeling of shocks in inertial confinement fusion implosions is critical for obtaining the desired compression in experiments. Shock velocities and post-shock conditions are determined by laser-energy deposition, heat conduction, and equations of state. This paper describes experiments at the National Ignition Facility (NIF) where multiple shocks are launched into a cone-in-shell target made of polystyrene, using laser pulse shapes with two or three pickets and varying on-target intensities. Shocks are diagnosed using the Velocity Interferometric System for Any Reflector diagnostic. Simulated and inferred shock velocities agree well for the range of intensities studied in this work. These results are the first shock-velocity measurements using direct drive on the NIF and provide a good measure of early-time, directly driven laser-energy coupling. Furthermore, the validated models add to the credibility of direct-drive-ignition designs at the megajoule scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Dynamic Model of Basic Oxygen Steelmaking Process Based on Multi-zone Reaction Kinetics: Model Derivation and Validation

A multi-zone kinetic model coupled with a dynamic slag generation model was developed for the simulation of hot metal and slag composition during the basic oxygen furnace (BOF) operation. The three reaction zones (i) jet impact zone, (ii) slag–bulk metal zone, (iii) slag–metal–gas emulsion zone were considered for the calculation of overall refining kinetics. In the rate equations, the transient rate parameters were mathematically described as a function of process variables. A micro and macroscopic rate calculation methodology (micro-kinetics and macro-kinetics) were developed to estimate the total refining contributed by the recirculating metal droplets through the slag–metal emulsion zone. The micro-kinetics involves developing the rate equation for individual droplets in the emulsion. The mathematical models for the size distribution of initial droplets, kinetics of simultaneous refining of elements, the residence time in the emulsion, and dynamic interfacial area change were established in the micro-kinetic model. In the macro-kinetics calculation, a droplet generation model was employed and the total amount of refining by emulsion was calculated by summing the refining from the entire population of returning droplets. A dynamic Fe{sub t}O generation model based on oxygen mass balance was developed and coupled with the multi-zone kinetic model. The effect of post-combustion on the evolution of slag and metal composition was investigated. The model was applied to a 200-ton top blowing converter and the simulated value of metal and slag was found to be in good agreement with the measured data. The post-combustion ratio was found to be an important factor in controlling Fe{sub t}O content in the slag and the kinetics of Mn and P in a BOF process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Validation of hydrogen gas stratification and mixing models

Two validation benchmarks confirm that the BMIX++ code is capable of simulating unintended hydrogen release scenarios efficiently. The BMIX++ (UC Berkeley mechanistic MIXing code in C++) code has been developed to accurately and efficiently predict the fluid mixture distribution and heat transfer in large stratified enclosures for accident analyses and design optimizations. The BMIX++ code uses a scaling based one-dimensional method to achieve large reduction in computational effort compared to a 3-D computational fluid dynamics (CFD) simulation. Two BMIX++ benchmark models have been developed. One is for a single buoyant jet in an open space and another is for a large sealed enclosure with both a jet source and a vent near the floor. Both of them have been validated by comparisons with experimental data. Excellent agreements are observed. The entrainment coefficients of 0.09 and 0.08 are found to fit the experimental data for hydrogen leaks with the Froude number of 99 and 268 best, respectively. In addition, the BIX++ simulation results of the average helium concentration for an enclosure with a vent and a single jet agree with the experimental data within a margin of about 10% for jet flow rates ranging from 1.21 × 10⁻⁴ to 3.29 × 10⁻⁴ m³/s. In conclusion, computing time for each BMIX++ model with a normal desktop computer is less than 5 min.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Numerical Model Development and Validation for the WECCOMP Control Competition: Preprint

This paper details the development and validation of a numerical model of the Wavestar wave energy converter (WEC) in WEC-Sim. This numerical model was developed in support of the WEC Control Competition (WECCCOMP), an open competition with the objective of maximizing WEC power production through innovative control strategies. WECCCOMP has two stages: numerical implementation of control strategies, and experimental implementation. The work presented in this paper is for support of the stage one numerical implementation, where contestants are provided a WEC-Sim model of the Wavestar device to develop their control algorithms.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

“Max-tech FHR”: What is the maximum technically achievable water delivery capacity from a single 120 V plug?

Electrifying water heating will require affordable, direct drop-in replacements for existing gas water heaters that meet consumer expectations for hot water delivery. For many replacement scenarios, affordability requires a replacement option with like-for-like physical product dimensions, and existing consumer expectations presume a large delivery capacity from a small water heater. This work explores the technical potential for hot water delivery capacity for a heat pump water heater (HPWH) powered from a common 120 V plug. Delivery capability in this work is quantified by two metrics: the First Hour Rating (FHR) defined in the Code of Federal Regulations, and a real-world draw profile that resulted in hot water runout during field studies of 120 V HPWHs. The work focuses on the common “tall-and-slim” gas water heater product category, with external product dimensions of 20 inch diameter and 60 inch height. Using a simulation model in DOE/ORNL Heat Pump Design Model, validated by experimental baseline testing of a commercially available 120 V HPWH model, several measures were evaluated to maximize the delivery capacity of a 120 V HPWH. The 120 V HPWH was constrained by 1) external product dimensions of 20 in diameter and 60 inch height, 2) electrical power of 1.5 kW (85% of the available power from a dedicated 120 V, 15 A circuit), and 3) commercially-available components. The measures implemented included a large compressor, a pumped loop with plate heat exchanger to maximize tank stratification, enlarged heat exchangers, and relatively thin insulation to maximize the water storage volume.

Gluesenkamp, Kyle↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2022

Redshifts of radio sources in the Million Quasars Catalogue from machine learning

ABSTRACT With the aim of using machine learning techniques to obtain photometric redshifts based upon a source’s radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44 119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties as features, we fail to find a model which can reliably predict the redshifts, although there is the suggestion that the models improve with the size of the training sample. Using the near-infrared–optical–ultraviolet bands magnitudes, we obtain reliable predictions based on the 12 503 radio sources which have all of the required photometry. From the 80:20 training–validation split, this gives only 2501 validation sources, although training the sample upon our previous SDSS model gives comparable results for all 12 503 sources. This makes us confident that SkyMapper, which will survey southern sky in the u, v, g, r, i, z bands, can be used to predict the redshifts of radio sources detected with the Square Kilometre Array. By using machine learning to impute the magnitudes missing from much of the sample, we can predict the redshifts for 32 698 sources, an increase from 28 to 74 per cent of the sample, at the cost of increasing the outlier fraction by a factor of 1.4. While the ‘optical’ band data prove successful, at this stage we cannot rule out the possibility of a radio photometric redshift, given sufficient data which may be necessary to overcome the relatively featureless radio spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Grid Modeling and Hosting Capacity Analysis

An interface with the OpenDSS distribution grid simulator, facilitating users in calibrating and validating models. Additionally, three distinct tools have been developed: PV Hosting Capacity: This tool allows users to determine the additional amount of photovoltaic (PV) generation that can be integrated into the system without breaching operational constraints. EV Hosting Capacity: This tool focuses on identifying the capacity for incorporating extra electric vehicle (EV) load into the system without surpassing operational limitations. Project Impact Analysis Tool: This tool is designed to assess and report all potential grid violations associated with a specific project, providing valuable insights into its impact on the distribution grid.

Poudel, Shiva↗