Results for “Adaptive Sampling” — Page 77

DOE OSTI2015

Comparative metagenomics reveals impact of contaminants on groundwater microbiomes

To understand patterns of geochemical cycling in pristine versus contaminated groundwater ecosystems, pristine shallow groundwater (FW301) and contaminated groundwater (FW106) samples from the Oak Ridge Integrated Field Research Center (OR-IFRC) were sequenced and compared to each other to determine phylogenetic and metabolic difference between the communities. Proteobacteria (e.g., Burkholderia, Pseudomonas) are the most abundant lineages in the pristine community, though a significant proportion ( >55%) of the community is composed of poorly characterized low abundance (individually <1%) lineages. The phylogenetic diversity of the pristine community contributed to a broader diversity of metabolic networks than the contaminated community. In addition, the pristine community encodes redundant and mostly complete geochemical cycles distributed over multiple lineages and appears capable of a wide range of metabolic activities. In contrast, many geochemical cycles in the contaminated community appear truncated or minimized due to decreased biodiversity and dominance by Rhodanobacter populations capable of surviving the combination of stresses at the site. In conclusion, these results indicate that the pristine site contains more robust and encodes more functional redundancy than the stressed community, which contributes to more efficient nutrient cycling and adaptability than the stressed community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Towards bio-silicon interfaces: Formation of an ultra-thin self-hydrated artificial membrane composed of dipalmitoylphosphatidylcholine (DPPC) and chitosan deposited in high vacuum from the gas-phase

The recent combination of nanoscale developments with biological molecules for biotechnological research has opened a wide field related to the area of biosensors. In the last years, device manufacturing for medical applications adapted the so-called bottom-up approach, from nanostructures to larger devices. Preparation and characterization of artificial biological membranes is a necessary step for the formation of nano-devices or sensors. In this paper, we describe the formation and characterization of a phospholipid bilayer (dipalmitoylphosphatidylcholine, DPPC) on a mattress of a polysaccharide (Chitosan) that keeps the membrane hydrated. The deposition of Chitosan (∼25 Å) and DPPC (∼60 Å) was performed from the gas phase in high vacuum onto a substrate of Si(100) covered with its native oxide layer. The layer thickness was controlled in situ using Very High Resolution Ellipsometry (VHRE). Raman spectroscopy studies show that neither Chitosan nor DPPC molecules decompose during evaporation. With VHRE and Atomic Force Microscopy we have been able to detect phase transitions in the membrane. The presence of the Chitosan interlayer as a water reservoir is essential for both DPPC bilayer formation and stability, favoring the appearance of phase transitions. Our experiments show that the proposed sample preparation from the gas phase is reproducible and provides a natural environment for the DPPC bilayer. In future work, different Chitosan thicknesses should be studied to achieve a complete and homogeneous interlayer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Phenolic compounds and black carbon feedback controls on peat decomposition and carbon accumulation in southeastern peatlands under regimes of seasonal drought, drainage and frequent fire

Peatlands store one-third of soil C in terrestrial ecosystems and have persisted through changing climate over millennia from the arctic to the tropics. Approximately one-third of peat stores are found in subtropical and tropical peatlands (STPs) formed from high-lignin woody biomass. In this project, our questions are: 1) why do these non-sphagnum peatlands (STPs) accumulate C under warmer-drier climates and 2) how might insights coming from studying control mechanisms in STPs improve the management and conservation of the vast C stores in boreal peatlands subject to increasing climate forcing. We hypothesized that a dual control or “latch mechanism” reduces decomposition in shrub/tree communities in STPs due to both (1) higher production of polyphenol and aromatic compounds in STPs than found in northern Sphagnum/Carex communities and (2) the buildup of recalcitrant organic matter produced by light fire-drought-warming-adapted communities, together leading to a reduction in the microbial decay rate of peat. After three-years of intensive biological and chemical analysis in a series of field and microcosm experiments along our north to south bog gradient from Minnesota to Peru, we show how previously unrecognized biotic factors, particularly dynamic interlinked above- and belowground attributes control C sequestration in peatlands. Our key findings include (1) phenolics-bridged plant-microbe symbioses, principally slow-growing microbes dominated in higher phenolic wooded STPs, preserving C in peatlands under climate change, 2) phenolics are the overarching factor controlling the relative abundance of slow-and fast-growing microbes, the slow-growing microbes in STPs metabolize C slowly and are inherently resistant to disturbance, 3) global data analysis shows that soil respiration does not increase exponentially from boreal to tropical peatlands, suggesting that slow-growing microbes may have become dominant in most non-boreal peatlands, 4) peat chemistry analysis from over 2000 samples show that across peatlands both from the arctic to tropics and from high to low elevation peatlands recalcitrance increases as aromatic content increases, and 5) peat affected by low-severity wildfires displays a similar pattern of higher aromatic content. Our findings all demonstrate links between peat recalcitrance and increased content of phenolics and other aromatic compounds in plants. Thus, linked plant–microbe symbiotic traits are a key to understanding ecological resilience and resistance developed in peatlands under disturbance. New trait-based approaches that can better link above-and below ground processes are needed to advance both the accuracy and precision of current abiotic-factor-based Earth system models in predicting future soil C responses to climate-change feedbacks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A resolution independent neural operator

The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.

Deep operator network (DeepONet)↗

DOE OSTI2021

MOA-2009-BLG-319Lb: A Sub-Saturn Planet inside the Predicted Mass Desert

We present an adaptive optics (AO) analysis of images from the Keck II telescope NIRC2 instrument of the planetary microlensing event MOA-2009-BLG-319. The ∼10 yr baseline between the event and the Keck observations allows the planetary host star to be detected at a separation of 66.5 ± 1.7 mas from the source star, consistent with the light-curve model prediction. The combination of the host star brightness and light-curve parameters yields host star and planet masses of M {sub host} = 0.524 ± 0.048M {sub ⊙} and m {sub p} = 67.3 ± 6.2M {sub ⊕} at a distance of D {sub L} = 7.1 ± 0.7 kpc. The star−planet projected separation is 2.03 ± 0.21 au. The planet-to-star mass ratio of this system, q = (3.857 ± 0.029) × 10{sup −4}, places it in the predicted “planet desert” at 10{sup −4} < q < 4 × 10{sup −4} according to the runaway gas accretion scenario of the core accretion theory. Seven of the 30 planets in the Suzuki et al. sample fall in this mass ratio range, and this is the third with a measured host mass. All three of these host stars have masses of 0.5 ≤ M {sub host}/M {sub ⊙} ≤ 0.7, which implies that this predicted mass ratio gap is filled with planets that have host stars within a factor of two of 1M {sub ⊙}. This suggests that runaway gas accretion does not play a major role in determining giant planet masses for stars somewhat less massive than the Sun. Our analysis has been accomplished with a modified DAOPHOT code that has been designed to measure the brightness and positions of closely blended stars. This will aid in the development of the primary method that the Nancy Grace Roman Space Telescope mission will use to determine the masses of microlens planets and their hosts.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Enhanced biofilm formation aids adaptation to extreme warming and environmental instability in the diatom Thalassiosira pseudonana and its associated bacteria

Abstract Recent studies have highlighted the capacity of marine diatoms at the foundation of ocean food webs and biogeochemical cycles to respond to environmental change. These responses are a combination of physiological acclimation and evolutionary responses. The latter occur rapidly within a few hundred generations (translating to a scale of months to years), due to the large standing genetic variation and short generation times of diatom populations. Studies are usually carried out on diatom species in their planktonic state, but this may constitute a stark oversimplification: diatoms readily form biofilms in the water column of shallow coastal areas, where biofilm formation affects carbon export, and in the benthos, where biofilms contribute significantly to sediment stability. Here, I have carried out a 400‐generation selection experiment, where I evolved the diatom Thalassiosira pseudonana and its associated bacteria. Bacteria and diatom were cultured in their planktonic form, and under strong selection for rapid biofilm formation in temperature and nutrient regimes differing in their severity and stability. I find that biofilm formation is enhanced under mild warming and rapid thermal fluctuations but not under extreme warming regardless of nutrient availability. Evolutionary responses are slower in biofilms under benign conditions but speed up under extreme and fluctuating conditions. Taxonomic composition of the associated bacteria is driven by the temperature selection regime in biofilms but not in planktonic samples. I also show that T. pseudonana , when selected for biofilm formation, presents predictable and sequential morphologies that are not usually observed in planktonic T. pseudonana.

Schaum, C. ‐E.↗

DOE OSTI2020

Quantification of CO 2 Mineralization at the Wallula Basalt Pilot Project

In 2013, the Pacific Northwest National Laboratory led a geologic carbon sequestration field demonstration where ~1000 tonnes of CO 2 was injected into several deep Columbia River Basalt zones near Wallula, Washington. Rock core samples extracted from the injection zone two years after CO 2 injection revealed nascent carbonate mineralization that was qualitatively consistent with expectations from laboratory experiments and reactive transport modeling. In this study, we report on a new detailed analysis of the 2012 pre-injection and 2015 post-injection hydrologic tests that capitalizes on the difference in fluid properties between scCO 2 and water to assess changes in near-field, wellbore, and reservoir conditions that are apparent approximately two years following the end of injection. This comparative hydrologic test analysis method provides a new way to quantify the amount of injected CO 2 that was mineralized in the field test. Modeling results indicate that approximately 60% of the injected CO 2 was sequestered via mineralization within two years, with the resulting carbonates occupying ~4% of the available reservoir pore space. The method presented here provides a new monitoring tool to assess the fate of CO 2 injected into chemically reactive basalt formations but could also be adapted for long-term monitoring and verification within more traditional subsurface carbon storage reservoirs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Effects of Substrate Addition on Soil Respiratory Carbon Release Under Long-Term Warming and Clipping in a Tallgrass Prairie

Regulatory mechanisms of soil respiratory carbon (C) release induced by substrates (i.e., plant derived substrates) are critical for predicting ecosystem responses to climate change, but the mechanisms are not well understood. In this study, we sampled soils from a long-term field manipulative experiment and conducted a laboratory incubation to explore the role of substrate supply in regulating the differences in soil C release among the experimental treatments, including control, warming, clipping, and warming plus clipping. Three types of substrates (glucose, C 3 and C 4 plant materials) were added with an amount equal to 1% of soil dry weight under the four treatments. We found that the addition of all three substrates significantly stimulated soil respiratory C release in all four warming and clipping treatments. In soils without substrate addition, warming significantly stimulated soil C release but clipping decreased it. However, additions of glucose and C 3 plant materials (C 3 addition) offset the warming effects, whereas C 4 addition still showed the warming-induced stimulation of soil C release. Our results suggest that long-term warming may inhibit microbial capacity for decomposition of C 3 litter but may enhance it for decomposition of C 4 litter. Such warming-induced adaptation of microbial communities may weaken the positive C-cycle feedback to warming due to increased proportion of C 4 species in plant community and decreased litter quality. In contrast, clipping may weaken microbial capacity for warming-induced decomposition of C 4 litter but may enhance it for C 3 litter. Warming- and clipping-induced shifts in microbial metabolic capacity may be strongly associated with changes in plant species composition and could substantially influence soil C dynamics in response to global change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Prediction of atomization energy using graph kernel and active learning

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

SU-D-202-01: Functional Lung Avoidance and Response-Adaptive Escalation (FLARE) RT: Feasibility of a Precision Radiation Oncology Strategy

Purpose: NSCLC patient RT is planned without consideration of spatial heterogeneity in lung function or tumor response, which may have contributed to failed uniform dose escalation in a randomized trial. The feasibility of functional lung avoidance and response-adaptive escalation (FLARE) RT to reduce dose to [{sup 99m}Tc]MAA-SPECT/CT perfused lung while redistributing 74Gy within [{sup 18}F]FDG-PET/CT biological target volumes was assessed. Methods: Eight Stage IIB–IIIB NSCLC patients underwent FDG-PET/CT and MAA-SPECT/CT treatment planning scans. Perfused lung objectives were derived from scatter/collimator/attenuation-corrected MAA-SPECT uptake relative to ITV-subtracted lung to maintain <20Gy mean lung dose (MLD). Prescriptions included 60Gy to PTV and concomitant boost of 74Gy mean to biological target volumes (BTV=GTV+PET margin) scaled to each BTV voxel by relative FDG-PET SUV. Dose-painting-by-numbers prescriptions were integrated into commercial TPS via previously reported ROI discretization. Dose constraints for lung, heart, cord, and esophagus were defined. FLARE RT plans were optimized with VMAT, proton pencil beam scanning (PBS) with 3%-3mm robust optimization, and combination PBS (avoidance) plus VMAT (escalation). Dosimetric differences were evaluated by Friedman non-parametric paired test with multiple sampling correction. Results: PTV and normal tissue objectives were not violated in 24 FLARE RT plans. Population median of mean BTV dose was 73.7Gy (68.5–75.5Gy), mean FDG-PET peak dose was 89.7Gy (73.5–103Gy), MLD was 12.3Gy (7.5–19.6Gy), and perfused MLD was 4.8Gy (0.9–12.1Gy). VMAT achieved higher dose to the FDG-PET peak subvolume (p=0.01), while PBS delivered lower dose to lung (p<0.001). Voxelwise linear correlation between BTV dose and FDG-PET uptake was higher for VMAT (R=0.93) and PBS+VMAT (R=0.94) compared to PBS alone (R=0.89). Conclusion: FLARE RT is feasible with VMAT and PBS. A combination of PBS for functional lung avoidance and VMAT for FDG-PET dose escalation balances target/normal tissue objective tradeoffs. These results support future testing of FLARE RT safety and efficacy within a precision radiation oncology trial. This work was supported by a Research Scholar grant from the Radiological Society of North American Research & Education Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

3D Printing of Cement-Based Materials Using Seawater for Simulated Marine Environments

Global demand for adaptable and rapidly deployable construction solutions in offshore, coastal, and fluvial environments continues to rise, driven by pressing needs to develop energy platforms, improve coastal resilience, and support emergency response in the face of natural disasters. Increased investment in human-made coastal infrastructure, such as piers, support structures for power lines, offshore wind farms, and seawall protection systems, further underscores this trend. This study investigates the development of printable concrete mixtures for underwater environments using seawater as a replacement for freshwater, using a 3D printing syringe-based extrusion system. The effect of seawater addition and the printing medium (in air vs. underwater) was assessed via rheological and mechanical performance characterization. The results indicate rheological properties are favorable for seawater adoption by producing mixtures with higher yield stress and viscosity with the same levels of admixtures used for freshwater. Seawater-based mixtures demonstrated superior dimensional stability compared to freshwater counterparts, maintaining cross-sectional geometry, while compressive strength results showed no statistical differences between in-air and underwater samples. However, flexural strength was significantly influenced by geometry and printing medium. These findings establish critical rheological parameters for printable underwater mixtures and highlight the need for optimized curing strategies and layer bonding techniques to improve interfacial strength in underwater 3D printing applications.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Nevada National Security Site (NNSS) Environmental Remediation Using EPA's Model Evaluation Guidance to Move to Closure - 15588

The National Nuclear Security Administration Nevada Field Office (NNSA/NFO) has adapted the Environmental Protection Agency's guidance on environmental model evaluation as a framework for evaluating corrective action investigation models. Model evaluation is an important confidence building activity, which is needed to support closure of the Nevada National Security Site (NNSS) Corrective Action Units (CAUs) associated with underground nuclear testing. Through this process, site-investigation models of groundwater flow and contaminant transport are reviewed and compared to new site-specific data to determine if the results can be used to support regulatory decisions required for closure and to achieve NNSA/NFO site management objectives. The regulatory decisions include identifying and establishing CAU regulatory boundaries; identifying institutional controls, including use-restriction boundaries; and developing a long-term closure monitoring program. A successful case study from Frenchman Flat, Nevada is used to illustrate the model evaluation process. Groundwater flow and transport from two of the 10 underground nuclear tests in Frenchman Flat had the largest potential for contaminating groundwater, and were selected for evaluation. Wells were drilled, logged, hydraulically tested, sampled, and analyzed for radionuclides and the data was evaluated with respect to previous site conceptualization and numerical model results. The approach and results of this process as applied to the PIN STRIPE test and how it helped build confidence to close the Frenchman Flat CAU is described in this case study. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Affinity maturation in a human humoral response to influenza hemagglutinin

Affinity maturation of the B cell antigen receptor (BCR) is a conserved and crucial component of the adaptive immune response. BCR lineages, inferred from paired heavy- and light-chain sequences of rearranged Ig genes from multiple descendants of the same naive B cell precursor (the lineages’ unmutated common ancestor, “UCA”), make it possible to reconstruct the underlying somatic evolutionary history. We present here an extensive structural and biophysical analysis of a lineage of BCRs directed against the receptor binding site (RBS) of subtype H1 influenza virus hemagglutinin (HA). The lineage includes 8 antibodies detected directly by sequencing, 3 in 1 principal branch and 5 in the other. When bound to HA, the heavy-chain third complementarity determining region (HCDR3) fits with an invariant pose into the RBS, but in each of the 2 branches, the rest of the Fab reorients specifically, from its position in the HA-bound UCA, about a hinge at the base of HCDR3. New contacts generated by the reorientation compensate for contacts lost as the H1 HA mutated during the time between the donor’s initial exposure and the vaccination that preceded sampling. Our data indicate that a “pluripotent” naive response differentiated, in each branch, into 1 of its possible alternatives. This property of naive BCRs and persistence of multiple branches of their progeny lineages can offer broader protection from evolving pathogens than can a single, linear pathway of somatic mutation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Leaf gas exchange and fitted parameters, two sites in Panama, 2022

Photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured in the Parque Natural Metropolitano (PNM) and Gamboa, Panama, from January to April 2022 are presented. Measurements were made on leaves from 46 different tree, shrub and liana species, from sunlit canopy and understory locations. Leaf area index from 8 vertical profiles at PNM are also included. The aim of this measurement campaign was three-fold: (i) to improve our understanding of the vertical variation in leaf-level water use efficiency; (ii) to develop an understanding of the sensitivity of stomata to changes in environmental conditions, especially light, humidity, and temperature; (iii) to improve models which can predict leaf traits from leaf contact spectral measurements. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data reported here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA). These data can be cross linked using the unique sample ID and are provided in separate data packages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗

DOE OSTI2014

WE-G-18C-08: Real Time Tumor Imaging Using a Novel Dynamic Keyhole MRI Reconstruction Technique

Purpose: To test the hypothesis that the novel Dynamic Keyhole MRI reconstruction technique can accelerate image acquisition whilst maintaining high image quality for lung cancer patients. Methods: 18 MRI datasets from 5 lung cancer patients were acquired using a 3T MRI scanner. These datasets were retrospectively reconstructed using (A) The novel Dynamic Keyhole technique, (B) The conventional keyhole technique and (C) the conventional zero filling technique. The dynamic keyhole technique in MRI refers to techniques in which previously acquired k-space data is used to supplement under sampled data obtained in real time. The novel Dynamic Keyhole technique utilizes a previously acquired a library of kspace datasets in conjunction with central k-space datasets acquired in realtime. A simultaneously acquired respiratory signal is utilized to sort, match and combine the two k-space streams with respect to respiratory displacement. Reconstruction performance was quantified by (1) comparing the keyhole size (which corresponds to imaging speed) required to achieve the same image quality, and (2) maintaining a constant keyhole size across the three reconstruction methods to compare the resulting image quality to the ground truth image. Results: (1) The dynamic keyhole method required a mean keyhole size which was 48% smaller than the conventional keyhole technique and 60% smaller than the zero filling technique to achieve the same image quality. This directly corresponds to faster imaging. (2) When a constant keyhole size was utilized, the Dynamic Keyhole technique resulted in the smallest difference of the tumor region compared to the ground truth. Conclusion: The dynamic keyhole is a simple and adaptable technique for clinical applications requiring real-time imaging and tumor monitoring such as MRI guided radiotherapy. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by a factor of five compared with full k-space methods.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗