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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A Method to Test Model Calibration Techniques

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then the calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A Method to Test Model Calibration Techniques: Preprint

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then the calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

ReNew100: Reliable Power System Operation with 100% Renewable Generation

The ReNew100 project has developed and demonstrated an operator support system (OSS) to operate power systems with 100% renewable power generation from inverter-based resources (IBRs) like wind and solar that significantly reduces the risk of widespread power outages in a simulated operational environment at Technology Readiness Level (TRL) 6. The OSS achieves resilient operation for power systems under changing operation modes including varying combinations of conventional and renewable generation, including cases with 100% renewable generation from wind and solar IBRs. The OSS continuously monitors the N-1 security of the power system, which refers to the ability of the power systems to survive single outages of power system elements, like the loss of a generator or a power line, without causing widespread power outages beyond those expected and planned for. If the OSS identifies that the system is not N-1 secure, an automatic Controller Parameter Optimization (CoPO) developed within Renew100 is activated to optimize the N-1 security of the system by tuning controller parameters within different assets like batteries, wind, and solar plants. The core innovation of ReNew100 is the development of this CoPO as well as the demonstration of the OSS for a real-time simulation of the Hawai'i Island power system. Moreover, Renew100 has developed new model calibration techniques for dynamic power system models and demonstrated them for Hawai'i Island.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2019

On the Use of Optimization Techniques for Turbulence Model Calibration

This article discusses the use of numerical optimization procedures to aid in the calibration of turbulence model coefficients. Such methods would increase the rigor and repeatability of the calibration procedure by requiring clearly defined and objective optimization metrics, and could be used to identify unique combinations of coefficient values for specific flow problems. The approach is applied to the re-calibration of an explicit algebraic Reynolds stress model for the incompressible planar mixing layer using the Nelder-Mead simplex algorithm and a micro-genetic algorithm with minimally imposed constraints. Three composite fitness functions, each based upon the error in the mixing layer growth rate and the normal and shear components of the Reynolds stresses, are investigated. The results demonstrate a significant improvement in the target objectives through the adjustment of three pressure-strain coefficients. Adjustments of additional coefficients provide little further benefit. Issues regarding the effectiveness of the fitness functions and the efficiency of the optimization algorithms are also discussed.

Yoder, Dennis A.↗
DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Earth Radiation Budget Experiment scanner radiometric calibration results

The Earth Radiation Budget Experiment (ERBE) scanning radiometers are producing measurements of the incoming solar, earth/atmosphere-reflected solar, and earth/atmosphere-emitted radiation fields with measurement precisions and absolute accuracies, approaching 1 percent. ERBE uses thermistor bolometers as the detection elements in the narrow-field-of-view scanning radiometers. The scanning radiometers can sense radiation in the shortwave, longwave, and total broadband spectral regions of 0.2 to 5.0, 5.0 to 50.0, and 0.2 to 50.0 micrometers, respectively. Detailed models of the radiometers' response functions were developed in order to design the most suitable calibration techniques. These models guided the design of in-flight calibration procedures as well as the development and characterization of a vacuum-calibration chamber and the blackbody source which provided the absolute basis upon which the total and longwave radiometers were characterized. The flight calibration instrumentation for the narror-field-of-view scanning radiometers is presented and evaluated.

Lee, Robert B., III↗
NASA NTRS2014

An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model

Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by push broom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite and the RapidEye constellation, further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of "linear" arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented.

Side slither↗
DOE OSTI2019

Development of Upscaling Techniques and Construction of Calibrated Models for Fractured Rocks Using Discrete Fracture Network Approaches

The Pahute Mesa Corrective Action Unit on the Nevada National Security Site (NNSS) contains several fractured aquifers that can potentially provide high-permeability pathways for radionuclides to migrate away from underground nuclear testing locations. Although the geometric properties of fractures, such as length and orientation, can be found using geophysical methods and borehole image analyses, the hydraulic property of fractures (which are primarily influenced by apertures) are often unknown or have a high degree of uncertainty attached to their values. The aperture values have a profound impact on groundwater velocity and are critical to understanding transport scales at Pahute Mesa. Fracture aperture values and their distributions also have dominant control over radionuclide transport with potential for long-range correlations or scale-invariant dynamics. Understanding of the small-scale transport variability in fractures could lead to identification of distinct characteristics on larger scales. Furthermore, upscaling techniques need to be developed to adequately predict transport behavior at field scales by capturing the transport trends of smaller scale networks. This report explores these vital modeling needs to understand the role of fractures in Pahute Mesa. Discrete fracture network (DFN) models are the basis for constructing a well-calibrated model to ascertain aperture values of fractures in lava-flow aquifers. These models are also used to develop and compare the usefulness of several random-walk-based models to perform transport upscaling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

An improved calibration technique for wind tunnel model attitude sensors

Aerodynamic wind tunnel tests at NASA Langley Research Center (LaRC) require accurate measurement of model attitude. Inertial accelerometer packages have been the primary sensor used to measure model attitude to an accuracy of +/- 0.01 deg as required for aerodynamic research. The calibration parameters of the accelerometer package are currently obtained from a seven-point tumble test using a simplified empirical approximation. The inaccuracy due to the approximation exceeds the accuracy requirement as the misalignment angle between the package axis and the model body axis increases beyond 1.4 deg. This paper presents the exact solution derived from the coordinate transformation to eliminate inaccuracy caused by the approximation. In addition, a new calibration procedure is developed in which the data taken from the seven-point tumble test is fit to the exact solution by means of a least-squares estimation procedure. Validation tests indicate that the new calibration procedure provides +/- 0.005-deg accuracy over large package misalignments, which is not possible with the current procedure.

Tripp, John S.↗
NASA NTRS2019

Modeling of a Modern Aircraft Through Calibration Techniques

NASA is seeking a new baseline aircraft model to assess the state-of-the-art technology for aircraft noise, emissions, and fuel/energy consumption as an update to a 2005 baseline. The process of modeling engine and airframe models as a system has historically required many iterations at NASA between the airframe and engine models. A new internal process presented in this paper contains a method that simultaneously calibrates an airframe and engine model to known data to create an aircraft system model. The work presented in this paper proposes a new framework in creating new aircraft models for future NASA research. This approach is presented as a general outline applicable to any chosen commercial aircraft. As an applied example, the B737 MAX 8 aircraft is chosen as the integrated engine and airframe model subjected to calibration. Initial results show a close match to available data but further refinement in the process is necessary for this ongoing work.

Thacker, Robert↗
NASA NTRS2009

A New Blind Pointing Model Improves Large Reflector Antennas Precision Pointing at Ka-Band (32 GHz)

The National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL)-Deep Space Network (DSN) subnet of 34-m Beam Waveguide (BWG) Antennas was recently upgraded with Ka-Band (32-GHz) frequency feeds for space research and communication. For normal telemetry tracking a Ka-Band monopulse system is used, which typically yields 1.6-mdeg mean radial error (MRE) pointing accuracy on the 34-m diameter antennas. However, for the monopulse to be able to acquire and lock, for special radio science applications where monopulse cannot be used, or as a back-up for the monopulse, high-precision open-loop blind pointing is required. This paper describes a new 4th order pointing model and calibration technique, which was developed and applied to the DSN 34-m BWG antennas yielding 1.8 to 3.0-mdeg MRE pointing accuracy and amplitude stability of 0.2 dB, at Ka-Band, and successfully used for the CASSINI spacecraft occultation experiment at Saturn and Titan. In addition, the new 4th order pointing model was used during a telemetry experiment at Ka-Band (32 GHz) utilizing the Mars Reconnaissance Orbiter (MRO) spacecraft while at a distance of 0.225 astronomical units (AU) from Earth and communicating with a DSN 34-m BWG antenna at a record high rate of 6-megabits per second (Mb/s).

DSN↗
DOE OSTI2018

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results show that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.

42 ENGINEERING↗