A Refrigerant Charge Prediction Method Based on Pump Down Operation Validated Using Residential and Commercial Heat Pump Systems
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Li, Zhenning↗
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Subsurface Trend Analysis domains for the northern Gulf of Mexico
Geologic domains for the northern Gulf of Mexico derived using the Subsurface Trend Analysis (STA) method. The domains were postulated using geologic province, lithologic, and structural information and validated using statistical methods. Publication detailing the STA method: Rose, K., Bauer, J.R., and Mark-Moser, M. (2020) Subsurface trend analysis, a multi-variate geospatial approach for subsurface evaluation and uncertainty reduction, Interpretation, vol. 8, issue 1 https://library.seg.org/doi/abs/10.1190/int-2019-0019.1 Detailed discussion of domain formation and analysis: Mark-Moser, M., Miller, R., Bauer, J., Rose, K., and C. Disenhof. 2018, Analysis of Subsurface Reservoir Properties Using a Novel Geospatial Approach, Offshore Gulf of Mexico. NETL-TRS-2018 https://edx.netl.doe.gov/dataset/detailed-analysis-of-geospatial-trends-of-hydrocarbon-accumulations-offshore-gulf-of-mexico
geology↗
VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS
This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.
97 MATHEMATICS AND COMPUTING↗
A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter
In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.
14 SOLAR ENERGY↗
Validation of k eff Calculations for Extended BWR Burnup Credit
The US Nuclear Regulatory Commission and consensus standards recommend validation of the numerical methods used in criticality safety analyses. This validation requires the comparison of computational results with measurements of physical systems which are neutronically similar to those used in the safety analysis being performed. To this end, this document examines the methods available to generate sensitivity data to help identify systems similar to spent boiling- water reactor (BWR) fuel in a flooded spent nuclear fuel transportation or storage cask. A large number of critical benchmark experiments are surveyed using sensitivity/uncertainty (S/U) techniques to assess their applicability to BWR burnup credit (BUC) beyond the burnup of peak reactivity. Multiple burnups of BWR assemblies are considered herein, as well as the actinide-only (AO) and actinide-and-major-fission-product (AFP) isotope sets. Sample validations are completed for representative application models to demonstrate that appropriate validation is possible and to indicate the bias and bias uncertainty values expected for related applications.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Quantifying EGS Reservoir Complexity with an Integrated Geophysical Approach-Improved Resolution Ambient Seismic Noise Interferometry
The primary goal of the project was met, as improved resolution (from kilometers to tens/hundreds of meters and to a depth of at least 3 km) was added to the seismic interferometry-estimated parameters relative to the work reported in AltaRock Energy Inc. (2014) for the Dixie Valley Geothermal Wellfield. A procedure for initial fault identification, using seismic interferometry, was developed. The project team further improved, and applied a new, cost-effective, and non-invasive exploration method, developed and validated since 2010, during two previous experiments, at Dixie Valley and Soda Lake, Nevada. The method, initially designed for hydrothermal exploration, was adapted to EGS exploration conditions, which require deeper crustal information. Regions of elevated energy for both the 2-7 Hz and 5-15 Hz frequency bands are located above intersections of multiple faults. Thus, an unprecedented, innovative seismic field deployment and ambient seismic noise and signal processing were successfully used to estimate crustal structure and to identify fault locations at depths up to 3 km, at a cost per kilometer of survey that in some cases may be as much as ten times less than the cost of a controlled-source survey. Based on valid and useful seismic data collected and analyzed during Phase I, in Phase II the team has further quantified statistically the advantage of using seismic analysis, in combination with other geological and geophysical techniques, for EGS target identification. A power spectral density analysis of the passive-seismic empirical Green’s functions reveals significantly higher power recorded over the exploration area at the power plant, and in the drilling area, near the BB station 10201, especially on the horizontal components. In this study, the most important relationship observed during the prior report, namely the one between velocity and temperature was explored. The areas that show anomalously high predicted temperature in the geostatistics occur where multiple faults intersect. Not surprisingly, such areas span between the Dixie Valley range front fault and the basin-ward Piedmont faults, in the vicinity of numerous wells. These locations also fall in the area of high EGS favorability from the prior 2014 study. The anomalies also coincide with the high power spectral density values. The result of altering all favorability criteria for the new data reached a maximum of 1/8th of a point and were not large enough to change any favorability value reported in the 2014 study. The new data reinforce the results obtained from the previous 2014 study. The current high-resolution ambient seismic noise project provided excellent results in defining faults in the wellfield. As such the identification of known faults by the project demonstrated the utility of this method for identifying faults at an exploration stage in geothermal development. Such fault identification, while extremely useful in exploration investigation, has not added sufficient new data for the generation of significantly different DVGW favorability maps in the current project. We believe the cost savings of our passive-seismic technique for EGS, over common industry active-source survey techniques, may have a minimum of 30%, and a maximum of 90%. There are many significant non-cost benefits of passive seismic surveys. Our passive-source method provides an environmentally-friendly seismic survey with minimal to negligible impacts. The economic benefit is greater EGS project feasibility.
15 GEOTHERMAL ENERGY↗
Advanced Reactor Development: Gas-Cooled Reactor First Quarter 2023 Report
ART Program First Quarter report for 2023. Entries for Fuels Development and Qualification, High Temperature Materials Development, Graphite Development and Qualification, and Design Methods Modeling and Validation.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Advanced modeling and simulation of research reactors using dynamic mode decomposition
Full text of publication follows. Due to the ever-increasing safety requirements, the current trend of nuclear reactor analysis is shifting towards high-fidelity multi-physics models, which have a very high computational cost and modelling complexity. As the cost of even a single model run makes it impossible to analyse the behaviour and performance of these models on large-scale commercial plants, it has become even more significant to provide suitable benchmarks to validate and test them extensively. In this sense, research reactors offer a promising solution for the initial validation of high-fidelity models, as they are significantly smaller than commercial reactors and their characteristics are well known. In particular, the reactors of the TRIGA family have been used to assess and validate models and methods for Generation-IV designs, as they have some similar features (such as the dominance of natural convection as cooling mechanism and the difficulties in performing sub-channel analysis using standard codes). Still, the computational requirements of high-fidelity models make them unsuitable for real-time analysis, even following their assessment on research reactors. In this sense, Model Order Reduction (MOR) techniques give an additional strategy to reduce the computational cost of high-fidelity models (whilst preserving sufficient accuracy). In particular, this work focuses on Dynamic Mode Decomposition (DMD), a non-intrusive MOR technique that aims at representing models with explicit temporal dynamics by extracting the time-varying characteristics and the governing structures based only on a set of available data, thus without needing any underlying knowledge of the governing equations. In addition, DMD also computes a low-dimensional surrogate of the dynamic matrix of the system, making it suited for stability analysis and real-time evaluations. This work focuses on the application and validation of the DMD method on the Computational Fluid-Dynamics (CFD) model TRIGA Mark II reactor, also discussing in detail the potentiality of this algorithm as an advanced modelling tool for nuclear reactor analysis. (author)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Validation testing of radioactive Cesium measurement in Decontaminated waste with the Scattering Gamma Equivalent Method - 17238
Canberra Industries, Inc. [CI], Now Mirion Technologies (Canberra) Inc. [MTC], has developed and validated a new Flexible Container Bag or Feed Roll Assay System 'FRAS' for Interim Storage Facilities [ISF] etc. There is an estimated 22 million cubic meters of soil and vegetation from the decontamination efforts around Fukushima. Removed soil and vegetation waste was put into flexible containers called Super Sacks [SS]. These SSs will be transported to ISF by trucks. These SSs will be divided into three categories according to total Cs radioactivity level (RL). This level is 'higher than 100,000 Bq/kg', '100,000>RL>8,000 Bq/kg' and '8,000 > RL >3,000 Bq/kg', respectively. The main uncertainty for SS measuring is internal inhomogeneity and heterogeneous source distribution. Japan Atomic Energy Agency [JAEA] has developed the Scattering Gamma Equivalent Model [SGE model] for decreasing uncertainty derived from these factors. This method was validated by Mirion Technologies (Canberra) KK [MCKK] and JAEA. According to the validation test, the SGE model can reduce the uncertainty of SS measurement from ± 25% to ± less than 15%. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Investigation of Delayed Neutron Sensitivities for Several ICSBEP Benchmarks using MCNP
The effective delayed neutron (β$_{eff}$) is a very important parameter for reactor and criticality applications. This parameter is equal to the difference in reactivity between delayed critical ($k_{eff}$ = 1, which requires both prompt and delayed neutrons to achieve criticality) and prompt critical ($k_p$ = 1, which requires only prompt neutrons to achieve criticality). This is often referred to as the delayed critical "window" (the region of criticality between delayed and prompt critical). β$_{eff}$ is a reactor kinetics parameters and depends on the nuclides in the system that undergo fission as well as the spectral characteristics of the system. Measurements of β$_{eff}$ have been performed for many criticality experiments. The EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) project at Los Alamos National Laboratory (LANL) aims to constrain nuclear data by using a suite of measurement types beyond $k_{eff}$. Our team has recently investigated the use of pulsed spheres for nuclear data validation. Several other measurement methods are also of interest, including β$_{eff}$ (investigated here) and reactivity coefficients (investigated in a separate work at this same meeting). One focus of our work is to determine if other methods are complimentary to the critical experiments already utilized for nuclear data validation. This is important because if a method has similar sensitivities then it will not be particularly useful for nuclear data validation as it will provide the same information as the critical experiments already being used. Here "similar" could refer to several characteristics, one being the shape of a sensitivity profile over energy. In the future, these methods will be utilized (with both existing and new experiments) in machine learning algorithms for nuclear validation, similar to what is currently done for criticality experiments. In order to use a measurement type for nuclear validation, it is necessary to obtain cross-section sensitivities for that parameter. This work looks at one approach to estimate β$_{eff}$ sensitivities by utilizing $k_{eff}$ sensitivities within Monte Carlo N-Particle ® Code Version 6.2. This is applied to several criticality benchmarks. Results are compared and the benefits and limitations of this approach are discussed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The Development of a DC Breakdown Voltage Test for Photovoltaic Insulating Materials
The ability of electrical insulating materials within a module to act as insulators is a key safety requirement for PV technology. Direct current breakdown voltage is therefore now specified for relied-upon insulator materials in the IEC 61730-1 safety standard. To fulfill that requirement, a new test method has been developed within the IEC TS 62788-2 backsheet standard for the measurement of breakdown voltage. The development of the test will be described, including the verification of the most critical parameters relative to factors such as defect population(s), dielectric medium, electrode size, electrode surface roughness, maximum current limit, moisture conditioning, number of replicate specimens, rate of voltage rise, specimen thickness, test polarity, and test temperature. Many of these parameters were specifically explored in discovery experiments as the test method was developed. An interlaboratory round-robin (R-R) experiment was conducted to further validate the test method by quantifying its repeatability and reproducibility. The materials examined in the R-R include the backsheet materials: polyethylene terephthalate (PET, two thicknesses), polyvinyl fluoride (PVF), and laminated PVF/PET/PVF ('TPT') as well as the encapsulants poly(ethylene-co-vinyl acetate) (EVA), and polyvinyl butyral (PVB). The precision of the test method as well as key factors contributing to the measurement will be described.
14 SOLAR ENERGY↗
POLARIZED LINE FORMATION IN NON-MONOTONIC VELOCITY FIELDS
For a correct interpretation of the observed spectro-polarimetric data from astrophysical objects such as the Sun, it is necessary to solve the polarized line transfer problems taking into account a realistic temperature structure, the dynamical state of the atmosphere, a realistic scattering mechanism (namely, the partial frequency redistribution—PRD), and the magnetic fields. In a recent paper, we studied the effects of monotonic vertical velocity fields on linearly polarized line profiles formed in isothermal atmospheres with and without magnetic fields. However, in general the velocity fields that prevail in dynamical atmospheres of astrophysical objects are non-monotonic. Stellar atmospheres with shocks, multi-component supernova atmospheres, and various kinds of wave motions in solar and stellar atmospheres are examples of non-monotonic velocity fields. Here we present studies on the effect of non-relativistic non-monotonic vertical velocity fields on the linearly polarized line profiles formed in semi-empirical atmospheres. We consider a two-level atom model and PRD scattering mechanism. We solve the polarized transfer equation in the comoving frame (CMF) of the fluid using a polarized accelerated lambda iteration method that has been appropriately modified for the problem at hand. We present numerical tests to validate the CMF method and also discuss the accuracy and numerical instabilities associated with it.
79 ASTRONOMY AND ASTROPHYSICS↗
Computationally Efficient Training of Deep Neural Networks via Transfer Learning
Transfer learning is a highly successful approach to train deep neural networks for customized image recognition tasks. Transfer learning leverages deep learning models that are previously trained on massive datasets and re-trains the network for a novel image recognition dataset. Typically, the advantage of transfer learning has been measured in sample efficiency, but instead, we investigate the computational efficiency of transfer learning. A good pre-trained model provides features that can be used as input to a new classifier (usually the top layers of a neural network). We show that if a good pre-trained model is selected, that training a new classifier can be much more computationally efficient than training a deep neural network without transfer learning. The first step in transfer learning is to select a pre-trained model to use as a feed-forward network for generating features. Here this selection process either uses human intuition/convenience or a methodical but computationally intensive validation loop. Instead, we would prefer a method to select the pre-trained model that will produce the best transfer results for the least amount of computation. To this end, we provide a computationally efficient metric for the fit between a pre-trained model and a novel image recognition task. The better the fit, the less computation will be needed to re-train the pre-trained model for the novel task. As machine learning becomes ubiquitous, highly-accurate trained models will proliferate and computationally efficient transfer learning methods will enable rapid development of new image recognition models.
97 MATHEMATICS AND COMPUTING↗
Investigating Fission Reaction Rate Ratio Sensitivities [Abstract]
Reaction rate ratios are a measurable parameter for reactor and criticality applications. A number of foil irradiations and fission chamber measurements have been performed for critical assemblies at Los Alamos National Laboratory starting in the 1950’s including (i) Godiva, a bare HEU spherical assembly; (ii) Flattop-25, a spherical assembly consisting of an HEU core and a natural uranium reflector; (iii) Jezebel, a bare 239 Pu assembly; and (iv) Flattop-Pu, a spherical assembly consisting of a 239Pu core and a natural uranium reflector. Fission ratio data for 238 U(n,f)/ 235 U(n,f), 237 Np(n,f)/ 235 U(n,f), 233 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f) were obtained and reported. The EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) project at Los Alamos National Laboratory (LANL) aims to constrain nuclear data by using a suite of measurement types beyond k-effective. Recent investigations include the use of pulsed spheres for nuclear data validation and other measurement methods of interest. One focus of the work is to determine if other methods are complimentary to the critical experiments utilized for nuclear data validation. It is anticipated the investigations will help inform methods that may be utilized in machine learning algorithms for nuclear validation. In order to use a measurement type for nuclear validation, it is necessary to obtain cross-section sensitivities for parameters. This work looks at reaction rate ratio sensitivities with SENSMG and Monte Carlo N-Particle R Code Version 6.21.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Freeway Loop Detector Data Reconciliation Based on Vehicle Conservation
Loop detector is the most common source of traffic flow measurements on freeways. Though being a mature technology, loop detector is prone to miscounts even when it is properly deployed and adjusted. If a loop detectors systematically over- or under-counts, the estimation based on vehicle conservation leads to results that are not physically plausible such as negative density or density higher than the jam-density. Here we propose an optimization-based method in which plausible values of accumulation is guaranteed through explicit constraints. Compared to similar methods, the proposed method distinct in the following: (i) the optimization problem is convex therefore leads to shorter computational time; (ii) occupancy is also taken into account in order to estimate upper and lower bounds in the accumulation between stations; (iii) no modifications are required when data from one or multiple lanes are missing. The method is first validated using micro-simulation by disturbing the loop-detector from micro-simulation, applying the method and then comparing the results with the ground truth data. The results show the method is able to capture the average counts almost exactly while providing estimate of density with satisfactory accuracy. The method is also successfully applied in a scenario in which field-data is available.
data reconciliation↗
Extension of the ratio method to low energy
The ratio method has been proposed as a means to remove the reaction model dependence in the study of halo nuclei. Originally, it was developed for higher energies but given the potential interest in applying the method at lower energy, in this work we explore its validity at 20 MeV/nucleon. The ratio method takes the ratio of the breakup angular distribution and the summed angular distribution (which includes elastic, inelastic and breakup) and uses this observable to constrain the features of the original halo wave function. In this work we use the Continuum Discretized Coupled Channel method and the Coulomb-corrected Dynamical Eikonal Approximation for the study. We study the reactions of 11 Be on 12 C, 40 Ca and 208 Pb at 20 MeV/nucleon. We compare the various theoretical descriptions and explore the dependence of our result on the core-target interaction. Lastly, our study demonstrates that the ratio method is valid at these lower beam energies.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Dominant balance-based adaptive mesh refinement for incompressible fluid flows
This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗