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At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Frankenstein’s ROMster: Avoiding pitfalls of reduced-order model development

Reduced-order models (ROMs) are a widely used and powerful approach to reducing the complexity of predictive physics-based numerical simulations for a wide range of applications, including electronics and fluid mechanics such as geologic CO 2 sequestration (GCS). ROMs are critical for optimization, sensitivity analysis, model calibration and uncertainty quantification where full-order models cannot be feasibly executed many times. Traditional approaches generate a single ROM for each simulated response (e.g., CO 2 injection rates, pH changes) based on a set of training simulations. Here, we demonstrate that a single ROM can display excellent overall predictive statistics, but have predictions that dramatically and unacceptably deviate from simulator responses especially when the response variable has a large range (i.e., vary over multiple orders of magnitude). For example, we show that a traditional statistically-high-performing GCS ROM (coefficient of determination R 2 of 0.99) can have average absolute relative errors of over 200%. To address this, we propose a new and novel approach where a set of sub-ROMs are generated to overcome the potential pitfalls in traditional single ROM development. The effectiveness of the proposed approach—the ROMster framework—is demonstrated using a case study of predicted CO 2 injection rates for GCS. We find our approach is a robust and general framework for ROM development, reducing the average “error” from 200% to only 4% in our case study.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Performance optimization of CO 2 heat pump water heater

A preliminary analysis was conducted to analyze the performance of a heat pump water heater (HPWH) that uses CO 2 as the refrigerant. A model to predict the performance was developed and calibrated based on the experimental data for an existing HPWH using a CO 2 refrigerant. The calibrated model was then used to run a parametric analysis in which factors such as water supply temperature, water circulation rate, tank stratification, and condenser configuration were considered. The performance of a commercial CO 2 system was compared with that of a similar system using R-134a as the refrigerant. It was found that CO 2 HPWH performance was comparable to that of an R-134a HPWH, more so for a separated gas cooler configuration. For comparable performance, the compressor size and the tube-in-tube heat exchanger (condenser/gas cooler) size were compared for CO 2 - and R-134a-based systems. Finally, the impact of the water circulation rate on the water temperature stratification in the tank, an essential requirement for higher performance for CO 2 HPWH systems was also investigated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

How data science methods can improve the quality and efficiency of ICF and HEDP research

Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A Framework for Model Validation and Calibration of Microgrid Components Using PMU Data

This paper presents a framework for phasor measurement-based component model validation and calibration islanded microgrids, with emphasis on grid-forming inverters. Real measurements from the Plum Island microgrid facility are used to calibrate the model of a commercial inverter in the open-source phasor-based distribution system simulator GridLAB-D.Limitations of current distribution system simulators in replicating the behavior of commercial inverters are also identified.

microgrid, Synchrophasor data, model validation↗

DOE OSTI2020

Improved Models of Long-Term Creep Behavior of High Performance Structural Alloys

In this program, QuesTek Innovations LLC, a leader in the field of computational materials design, proposes to develop a robust creep-modeling toolkit that expands its computational Materials by Design® technology, in order to predict the long-term creep performance of materials for base alloys and weldments in fossil energy systems under wide thermal and mechanical conditions. The Material Database Group of NIMS (National Institute for Material Science, JAPAN) provides reliable public data for long-term creep behavior over 300,000 hours (> 34 years) for a variety of materials including high Cr steels along with seminal work performed by EPRI (Electric Power Research Institute) on creep failure of these steels. QuesTek would rely on these premier institutions for experimental data needed to calibrate models. The developed tool has wider applications than the state-of-the-art modeling tools. It includes the effects of chemistry variations, microstructure variation (weldments) etc .which is lacking in many available models requiring the model to be calibrated to large number of datasets for accurate predictions. A model based on fundamental mechanism has promises in application to different materials, other than the demonstrated material (Grade 91 steels) in the current program. As a result of such an effort, we envision greatly advancing the state-of-the-art modeling tools for creep life assessments of different materials.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2016

Economic analysis of model validation for a challenge problem

It is now commonplace for engineers to build mathematical models of the systems they are designing, building, or testing. And, it is nearly universally accepted that phenomenological models of physical systems must be validated prior to use for prediction in consequential scenarios. Yet, there are certain situations in which testing only or no testing and no modeling may be economically viable alternatives to modeling and its associated testing. This paper develops an economic framework within which benefit–cost can be evaluated for modeling and model validation relative to other options. The development is presented in terms of a challenge problem. As a result, we provide a numerical example that quantifies when modeling, calibration, and validation yield higher benefit–cost than a testing only or no modeling and no testing option.

42 ENGINEERING↗

DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Surrogate models for linear response

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of 180 Yb and the 𝛽-decay half-life of 80 Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

Beta decay↗

DOE OSTI2015

A Bayesian, multivariate calibration for Globigerinoides ruberMg/Ca

The use of Mg/Ca in marine carbonates as a paleothermometer has been challenged by observations that implicate salinity as a contributing influence on Mg incorporation into biotic calcite and that dissolution at the sea-floor alters the original Mg/Ca. Yet, these factors have not yet been incorporated into a single calibration model. In this paper, we introduce a new Bayesian calibration for Globigerinoides ruber Mg/Ca based on 186 globally distributed core top samples, which explicitly takes into account the effect of temperature, salinity, and dissolution on this proxy. Our reported temperature, salinity, and dissolution (here expressed as deep-water ΔCO 2- 3 ) sensitivities are (±2σ) 8.7±0.9%/°C, 3.9±1.2%/psu, and 3.3±1.3%/μmol.kg -1 below a critical threshold of 21 μmol/kg in good agreement with previous culturing and core-top studies. We then perform a sensitivity experiment on a published record from the western tropical Pacific to investigate the bias introduced by these secondary influences on the interpretation of past temperature variability. This experiment highlights the potential for misinterpretations of past oceanographic changes when the secondary influences of salinity and dissolution are not accounted for. Finally, multiproxy approaches could potentially help deconvolve the contributing influences but this awaits better characterization of the spatio-temporal relationship between salinity and δ 18 O sw over millennial and orbital timescales.

58 GEOSCIENCES↗

DOE OSTI2024

Platform Of Optimal Experiment Management

The platform of optimal experiment management, POEM, powered with automated machine learning to accelerate the discovery of optimal solutions, and automatically guide the design of experiments to be evaluated. POEM currently supports 1) random model explorations for experiment design, 2) sparse grid model explorations with Gaussian Polynomial Chaos surrogate model to accelerate experiment design ,3) time-dependent model sensitivity and uncertainty analysis to identify the importance features for experiment design, 4) model calibrations via Bayesian inference to integrate experiments to improve model performance, and 5) Bayesian optimization for optimal experimental design. In addition, POEM aims to simplify the process of experimental design for users, enabling them to analyze the data with minimal human intervention, and improving the technological output from research activities.

Wang, Congjian [Idaho National Laboratory (INL), I↗

DOE OSTI2013

Geochemical Impacts of Leaking CO 2 from Subsurface Storage Reservoirs to Unconfined and Confined Aquifers

Experimental research work has been conducted and is undergoing at Pacific Northwest National Laboratory (PNNL) to address a variety of scientific issues related with the potential leaks of the carbon dioxide (CO 2 ) gas from deep storage reservoirs. The main objectives of this work are as follows: • Develop a systematic understanding of how CO 2 leakage is likely to influence pertinent geochemical processes (e.g., dissolution/precipitation, sorption/desorption and redox reactions) in the aquifer sediments. • Identify prevailing environmental conditions that would dictate one geochemical outcome over another. • Gather useful information to support site selection, risk assessment, policy-making, and public education efforts associated with geological carbon sequestration. In this report, we present results from experiments conducted at PNNL to address research issues related to the main objectives of this effort. A series of batch and column experiments and solid phase characterization studies (quantitative x-ray diffraction and wet chemical extractions with a concentrated acid) were conducted with representative rocks and sediments from an unconfined, oxidizing carbonate aquifer, i.e., Edwards aquifer in Texas, and a confined aquifer, i.e., the High Plains aquifer in Kansas. These materials were exposed to a CO 2 gas stream simulating CO 2 gas leaking scenarios, and changes in aqueous phase pH and chemical composition were measured in liquid and effluent samples collected at pre-determined experimental times. Additional research to be conducted during the current fiscal year will further validate these results and will address other important remaining issues. Results from these experimental efforts will provide valuable insights for the development of site-specific, generation III reduced order models. In addition, results will initially serve as input parameters during model calibration runs and, ultimately, will be used to test model predictive capability and competency. The results from these investigations will provide useful information to support site selection, risk assessment, and public education efforts associated with geological, deep subsurface CO 2 storage and sequestration.

Qafoku, Nikolla P.↗

DOE OSTI2014

Global Simulation of Bioenergy Crop Productivity: Analytical framework and Case Study for Switchgrass

Contemporary global assessments of the deployment potential and sustainability aspects of biofuel crops lack quantitative details. This paper describes an analytical framework capable of meeting the challenges associated with global scale agro-ecosystem modeling. We designed a modeling platform for bioenergy crops, consisting of five major components: (i) standardized global natural resources and management data sets, (ii) global simulation unit and management scenarios, (iii) model calibration and validation, (iv) high-performance computing (HPC) modeling, and (v) simulation output processing and analysis. A case study with the HPC- Environmental Policy Integrated Climate model (HPC-EPIC) to simulate a perennial bioenergy crop, switchgrass (Panicum virgatum L.) and global biomass feedstock analysis on grassland demonstrates the application of this platform. The results illustrate biomass feedstock variability of switchgrass and provide insights on how the modeling platform can be expanded to better assess sustainable production criteria and other biomass crops. Feedstock potentials on global grasslands and within different countries are also shown. Future efforts involve developing databases of productivity, implementing global simulations for other bioenergy crops (e.g. miscanthus, energycane and agave), and assessing environmental impacts under various management regimes. We anticipated this platform will provide an exemplary tool and assessment data for international communities to conduct global analysis of biofuel biomass feedstocks and sustainability.

Nair, S. Surendran↗

NASA NTRS2016

Optical Modeling and Polarization Calibration for CMB Measurements with Actpol and Advanced Actpol

The Atacama Cosmology Telescope Polarimeter (ACTPol) is a polarization sensitive upgrade to the Atacama Cosmology Telescope, located at an elevation of 5190 m on Cerro Toco in Chile. ACTPol uses transition edge sensor bolometers coupled to orthomode transducers to measure both the temperature and polarization of the Cosmic Microwave Background (CMB). Calibration of the detector angles is a critical step in producing polarization maps of the CMB. Polarization angle offsets in the detector calibration can cause leakage in polarization from E to B modes and induce a spurious signal in the EB and TB cross correlations, which eliminates our ability to measure potential cosmological sources of EB and TB signals, such as cosmic birefringence. We calibrate the ACTPol detector angles by ray tracing the designed detector angle through the entire optical chain to determine the projection of each detector angle on the sky. The distribution of calibrated detector polarization angles are consistent with a global offset angle from zero when compared to the EB-nulling offset angle, the angle required to null the EB cross-correlation power spectrum. We present the optical modeling process. The detector angles can be cross checked through observations of known polarized sources, whether this be a galactic source or a laboratory reference standard. To cross check the ACTPol detector angles, we use a thin film polarization grid placed in front of the receiver of the telescope, between the receiver and the secondary reflector. Making use of a rapidly rotating half-wave plate (HWP) mount we spin the polarizing grid at a constant speed, polarizing and rotating the incoming atmospheric signal. The resulting sinusoidal signal is used to determine the detector angles. The optical modeling calibration was shown to be consistent with a global offset angle of zero when compared to EB nulling in the first ACTPol results and will continue to be a part of our calibration implementation. The first array of detectors for Advanced ACTPol, the next generation upgrade to ACTPol, will be deployed in 2016.We plan to continue using both techniques and compare them to astrophysical source measurements for the Advanced ACTPol polarization calibration.

Koopman, Brian↗

NASA NTRS2024

Modeling Dynamic Crush Behavior of Carbon Fiber Reinforced Polymer Composite Structures Using MAT213

Modeling crushing of carbon fiber reinforced polymer (CFRP) composites is challenging, and current simulation methodologies involve tuning of non-physical parameters. MAT213, a next-generation material model, has advanced functionality to better simulate dynamic impact loading. The objective of the present investigation is to evaluate the potential for using MAT213 to simulate dynamic crushing of CFRPs. Two sets of simulations were performed: one for calibration based on a set of coupon-level experiments and another for prediction of the response of structural elements. Simulations involving dynamic crushing of flat specimens were iteratively run to calibrate model parameters. The calibration demonstrated that MAT213 could produce a simulated force-displacement response within experimental scatter. The simulated failure morphology was also comparable to the experiments. After successful calibration, predictive simulations of dynamic crushing of C-channel shaped specimens were completed using a simulated crash sled test rig and two pairs of impactor mass/velocity conditions. The simulated force-displacement curve in the crash sled simulations for the lower-velocity condition fell within the experimental scatter, but the stable crush force was underpredicted by 27% in the higher-velocity simulations. Better correlation in the lower-velocity test condition likely results from the calibration condition being a similar velocity to the lower-velocity crash sled condition.

Dynamic Crush↗

DOE OSTI2019

Strength of porous α-SiO 2 in a shock loaded environment

The strength of brittle porous media is of concern in numerous applications, for example, earth penetration, crater formation, and blast loading; thus it is of importance to possess techniques that allow for constitutive model calibration within the laboratory setting. It is the goal of the immediate work to demonstrate an experimental technique allowing for strength assessment, which can be implemented into pressure dependent yield surfaces within numerical simulation schemes. As a case study, the deviatoric strength of distended α-SiO 2 has been captured in a tamped Richtmyer- Meshkov instability environment at a pressure regime of 4-10 GPa. In contrast to traditional RMI studies used to infer strength in solids, the described approach herein is implemented to probe the behavior of the porous tamp media backing the corrugated solid surface. Hydrocode simulation has been used to interpret the experiment, and a resulting pressure-dependent yield surface akin to the often employed Modified Drucker-Prager model has been calibrated via the coupled experiment and simulation. The simulations indicate that the resulting jet length generated by the RMI is highly sensitive to the porous media strength, thereby providing a feasible experimental platform capable of capturing pressurized granular deviatoric response. Additionally, a Mach lens loading environment has also been implemented as a validation case study, demonstrating good agreement between experiment and simulation within an alternative loading environment. Calibration and validation of the pressure-dependent yield surface gives confidence to the model form, thereby providing a framework for future porous media strength studies.

36 MATERIALS SCIENCE↗

NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗

DOE OSTI2018

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

We report that carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. Finally, the predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

SWOT Applications for WRF-Hydro Modeling in Alaska

The Surface Water Ocean Topography (SWOT) mission, launching next year, will provide high-spatial resolution measurements of terrestrial surface water, including global rivers with widths greater than 50-100 m. SWOT measurements are naturally suited for stream hydrology, and many previous studies have worked to quantify the impact of SWOT observations on the modeling of channel flow. This work highlights the application of SWOT for WRF-Hydro modeling in Alaska for data assimilation and model calibration to support ongoing National Oceanic and Atmospheric Administration (NOAA) National Water Model development. Results demonstrate the effectiveness of using SWOT discharge estimates to calibrate WRF-Hydro in ungauged basins, and quantifies the impact of SWOT data assimilation on WRF-Hydro performance.

Nicholas Elmer↗