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DOE OSTI2022

Dataset from ORNL Flexible Research Platform (FRP)

The data comes from a two-story light commercial building which can be used to physically simulate light commercial buildings common in the nation's existing building stock. Based on a data collection plan, these measurements were performed for five different building operations. In the data set for each period, 98 data variables were selected and collected, in which 7 variables are weather data and the rest are all building and system operation data.

Cui, Borui↗

DOE OSTI2026

Virtual refrigerant charge sensor for variable-speed heat pumps based on feature selection

The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.

Liang, Chenjiyu↗

DOE OSTI2024

Estimating Sparse Direct Effects in Multivariate Regression With the Spike-and-Slab LASSO

The multivariate regression interpretation of the Gaussian chain graph model simultaneously parametrizes (i) the direct effects of p predictors on q outcomes and (ii) the residual partial covariances between pairs of outcomes. We introduce a new method for fitting sparse versions of these models with spike-and-slab LASSO (SSL) priors. We develop an Expectation Conditional Maximization algorithm to obtain sparse estimates of the p × q matrix of direct effects and the q × q residual precision matrix. Our algorithm iteratively solves a sequence of penalized maximum likelihood problems with self-adaptive penalties that gradually filter out negligible regression coefficients and partial covariances. Because it adaptively penalizes individual model parameters, our method is seen to outperform fixed-penalty competitors on simulated data. We establish the posterior contraction rate for our model, buttressing our method’s excellent empirical performance with strong theoretical guarantees. Using our method, we estimated the direct effects of diet and residence type on the composition of the gut microbiome of elderly adults.

EM algorithm↗

DOE OSTI2020

The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2017/2018 follow-up campaign: discovery of 10 lensed quasars and 10 quasar pairs

Here, we report the results of the STRong lensing Insights into the Dark Energy Survey (STRIDES) follow-up campaign of the late 2017/early 2018 season. We obtained spectra of 65 lensed quasar candidates with ESO Faint Object Spectrograph and Camera 2 on the NTT and Echellette Spectrograph and Imager on Keck, confirming 10 new lensed quasars and 10 quasar pairs. Eight lensed quasars are doubly imaged with source redshifts between 0.99 and 2.90, one is triply imaged (DESJ0345-2545, z = 1.68), and one is quadruply imaged (quad: DESJ0053-2012, z = 3.8). Singular isothermal ellipsoid models for the doubles, based on high-resolution imaging from SAMI on Southern Astrophysical Research Telescope or Near InfraRed Camera 2 on Keck, give total magnifications between 3.2 and 5.6, and Einstein radii between 0.49 and 1.97 arcsec. After spectroscopic follow-up, we extract multi-epoch grizY photometry of confirmed lensed quasars and contaminant quasar + star pairs from DES data using parametric multiband modelling, and compare variability in each system’s components. By measuring the reduced χ 2 associated with fitting all epochs to the same magnitude, we find a simple cut on the less variable component that retains all confirmed lensed quasars, while removing 94 per cent of contaminant systems. Based on our spectroscopic follow-up, this variability information improves selection of lensed quasars and quasar pairs from 34-45 per cent to 51–70 per cent, with most remaining contaminants being star-forming galaxies. Using mock lensed quasar light curves we demonstrate that selection based only on variability will over-represent the quad fraction by 10 per cent over a complete DES magnitude-limited sample, explained by the magnification bias and hence lower luminosity/more variable sources in quads.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗

DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗

DOE OSTI2024

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Engineering↗

DOE OSTI2021

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Wang, Chen↗

DOE OSTI2020

Supramolecular Tuning Enables Selective Oxygen Reduction Catalyzed by Cobalt Porphyrins for Direct Electrosynthesis of Hydrogen Peroxide

Abstract We report a supramolecular strategy for promoting the selective reduction of O 2 for direct electrosynthesis of H 2 O 2 . We utilized cobalt tetraphenylporphyrin (Co‐TPP), an oxygen reduction reaction (ORR) catalyst with highly variable product selectivity, as a building block to assemble the permanently porous supramolecular cage Co‐PB‐1(6) bearing six Co‐TPP subunits connected through twenty‐four imine bonds. Reduction of these imine linkers to amines yields the more flexible cage Co‐rPB‐1(6). Both Co‐PB‐1(6) and Co‐rPB‐1(6) cages produce 90–100 % H 2 O 2 from electrochemical ORR catalysis in neutral pH water, whereas the Co‐TPP monomer gives a 50 % mixture of H 2 O 2 and H 2 O. Bimolecular pathways have been implicated in facilitating H 2 O formation, therefore, we attribute this high H 2 O 2 selectivity to site isolation of the discrete molecular units in each supramolecule. The ability to control reaction selectivity in supramolecular structures beyond traditional host–guest interactions offers new opportunities for designing such architectures for a broader range of catalytic applications.

Smith, Peter T.↗

DOE OSTI2020

Supramolecular Tuning Enables Selective Oxygen Reduction Catalyzed by Cobalt Porphyrins for Direct Electrosynthesis of Hydrogen Peroxide

Smith, Peter T.↗

DOE OSTI2024

High allelic diversity in Arabidopsis NLRs is associated with distinct genomic features

Plants rely on Nucleotide-binding, Leucine-rich repeat Receptors (NLRs) for pathogen recognition. Highly variable NLRs (hvNLRs) show remarkable intraspecies diversity, while their low-variability paralogs (non-hvNLRs) are conserved between ecotypes. At a population level, hvNLRs provide new pathogen-recognition specificities, but the association between allelic diversity and genomic and epigenomic features has not been established. Our investigation of NLRs in Arabidopsis Col-0 has revealed that hvNLRs show higher expression, less gene body cytosine methylation, and closer proximity to transposable elements than non-hvNLRs. hvNLRs show elevated synonymous and nonsynonymous nucleotide diversity and are in chromatin states associated with an increased probability of mutation. Diversifying selection maintains variability at a subset of codons of hvNLRs, while purifying selection maintains conservation at non-hvNLRs. How these features are established and maintained, and whether they contribute to the observed diversity of hvNLRs is key to understanding the evolution of plant innate immune receptors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗

DOE OSTI2021

diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation

diyepw allows for quick and easy generation of a set of EnergyPlus weather (EPW) files for a given location over a given historical period. The user can obtain weather files using an open-source, automated workflow by simply specifying the location of interest using the World Meteorological Organization weather station ID number, and specifying a year or set of years for which to generate EPW files. Building energy modelers can use these auto-generated weather files in building performance simulations to represent the actual observed weather conditions in the location(s) of interest, based on meteorological observations obtained from the National Oceanic and Atmospheric Administration's Integrated Surface Database. Because observed weather data are not available for every meteorological variable specified in the EPW format, diyepw starts with a widely-used set of typical meteorological year (TMY) files, using them as the template to generate new EPW files by substituting in the observed values of selected meteorological variables that are known to affect building energy performance. Its output is a weather file or group of weather files that conform to the data standards associated with the EPW format so they can be used with any building performance simulation software employing EnergyPlus as its simulation engine.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Extreme Variability Quasars in Their Various States. I. The Sample Selection and Composite SDSS Spectra

Extremely variable quasars (EVQs) are a population of sources showing large optical photometric variability revealed by time-domain surveys. The physical origin of such extreme variability is yet unclear. In this first paper of a series, we construct the largest-ever sample of 14,012 EVQs using more than 15 yr of photometric data from Sloan Digital Sky Survey (SDSS) and Pan-STARRS1. We divide the EVQs into five subsamples according to the relative brightness of each EVQ during SDSS spectroscopic observation compared with the mean brightness from photometric observations. Corresponding control samples of normal quasars are built with matched redshift, bolometric luminosity, and supermassive black hole mass. We obtain the composite SDSS spectra of EVQs in various states and their corresponding control samples. We find EVQs exhibit clearly bluer SDSS spectra during bright states and clearly redder spectra during dim states, consistent with the "bluer-when-brighter" trend widely seen in normal quasars. We further find that the line equivalent widths (EWs) of broad Mg II, C IV and [O III] (but not broad Hβ, which is yet puzzling) gradually decreases from the dim state to the bright state, similar to the so-called intrinsic Baldwin effect commonly seen in normal active galactic nuclei. In addition, EVQs have consistently larger line EWs compared with the control samples. We also see that EVQs show slight excess in the very broad line component compared with control samples. Possible explanations for the discoveries are discussed. Our findings support the hypothesis that EVQs are in the tail of a broad distribution of quasar properties but are not a distinct population.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. Here, this paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Selecting the model and influencing variables for DHW heat use prediction in hotels in Norway

Not Available

Ivanko, Dmytro↗

DOE OSTI2021

Effects of multiple simultaneous faults on characteristic fault detection features of a heat pump in cooling mode

Faults in air-cooled vapor compression air-conditioning systems are known to reduce performance, including efficiency, capacity, and lifespan. Their effects have been studied, and fault detection and diagnostic (FDD) methods have been developed as tools for field technicians to install or repair systems, or for monitoring to alert operators to the fault’s presence. Most of this work has focused on faults that occur singly. It is likely that in some systems, multiple faults occur simultaneously, but it is uncertain what effects this may have on diagnostics. Here, this paper describes a laboratory study of a split system air source heat pump in which combinations of two, three, and four simultaneous faults occur. The study includes all combinations of: improper evaporator airflow; overcharge or undercharge of refrigerant; liquid line restrictions; and non-condensable gas in the refrigerant, each at multiple fault intensities. Fault features – those characteristics that can be determined from measurements, for use in diagnostics – are analyzed, and the key fault features are presented. A robust existing method for determining refrigerant charge, the virtual refrigerant charge sensor (VRC) is tested using the multiple fault data, in order to understand how its performance is impacted by the combined faults. The VRC performs well, typically able to correctly determine whether a system is undercharged or overcharged, but the magnitude estimates are impacted. The results suggest that simple subcooling-based methods of charging a system are likely to provide unsatisfactory results when other faults are present.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗