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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modernization of Technical Requirements for Licensing of Advanced Non-Light Water Reactors: Risk-Informed and Performance-Based Evaluation of Defense-in-Depth Adequacy

This document supports the work contained in Nuclear Energy Institute (NEI) 18-04 “Risk-Informed Performance-Based Technology Inclusive Guidance for Advanced Reactor Licensing Basis Development” Revision 0. NEI 18-04 presents a modern, technology-inclusive, risk-informed, and performance-based (TI-RIPB) process for selection of Licensing Basis Events (LBEs); safety classification of structures, systems, and components (SSCs) and associated risk-informed special treatments; and determination of defense-in-depth (DID) adequacy for non-LWRs. The NEI guidance document provides one acceptable means for addressing the aforementioned topics as part of demonstrating a specific design provides reasonable assurance of adequate radiological protection. This report provides the framework and associated methodology guidelines and discussion for establishing, then evaluating, confirming, and documenting the adequacy of defense-in-depth (DID) for advanced non-light-water reactor technologies. It was developed as part of the Licensing Modernization Project led by Southern Company and cost-shared by the United States Department of Energy and has benefited from considerable NRC formal reviews and public workshops. The methodology converts the DID philosophy into a structured process that is implementable, embraces existing United States and international definitions and philosophies of DID that set the foundation for the process. It builds on the DID framework developed in the Department of Energy Next Generation Nuclear Plant Project and earlier works on this subject. The approach to establishing DID adequacy involves the incorporation of DID attributes into the plant capabilities and programmatic elements of DID. The integrated evaluation of DID adequacy includes both quantitative elements to incorporate risk-informed and performance-based (RIPB) considerations and qualitative elements that address uncertainties and limitations in the quantitative models and supporting data. Demonstration of DID adequacy ensures that there are multiple layers of defense for risk-significant challenges to the design and that the plant capabilities and programs that support each layer are provided in a manner that minimizes dependencies among these layers. The focus of this report is assurance of DID adequacy with respect to protection of the public from radiological exposures resulting from accidental releases of radioactive material. While other hazards are not specifically addressed, this methodology is expected to be beneficial for determining DID adequacy for them as well. Risk-informed evaluation of DID considers the integrated performance of all plant SSCs and associated programs to manage daily operational activities, transients, and accidents, including the evaluation of strategies for accident prevention and mitigation. The RIPB LBE scenario methodology used in this evaluation defines the challenges to the plant safety features included in the plant design basis and beyond, and the scope of all deterministic and probabilistic safety evaluations. By examining event sequences across the whole spectrum of LBEs, a systematic assessment of DID can be accomplished.

42 ENGINEERING↗
DOE OSTI2021

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Full Technical Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap. We investigated a deeply rooted plant, switchgrass (Panicum virgatum), as a means of increasing carbon stocks in marginal and agricultural soils. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow-rooted conventional crop cover. To test this hypothesis, we compared soil depth profiles beneath deeply rooted switchgrass (cultivated for 4-30 years) and paired shallowrooted annual controls. We studied 12 field sites, 3 that were collected in 2018 before the start of the project as part a Department of Energy (DOE) Sustainable Biofuels study, and 9 that we collected in 2019 on a national field sampling campaign across the eastern US. In our publication from the 2018 study, which was written in collaboration with the LLNL Soil Microbiome Scientific Focus Area (SFA), we found that C stocks increased under switchgrass, but that the increases were dependent on soil texture. In the 2019 study, we found that carbon accrual tended to occur most consistently in low C soil in the southern US, which could indicate that perennial grasses may be a viable strategy to increase SOC in marginals soils in this region. We have published two studies from the 2019 sampling campaign thus far, we measured microbial growth parameters that will aid in modeling subsoil carbon cycling, and we found that switchgrass appears to move water upward in the soil profile and could promote drought tolerance, which a phenomenon commonly performed by trees known as ‘hydraulic redistribution;’ our study is the first to show this can occur in deep-rooted grasses. Finally, we published a modeling paper in collaboration with the LLNL Soil Microbiome SFA, where we found that poorly crystalline minerals are abundant and strongly correlated with organic C in geographically limited zones with enhanced weathering rates. Our results will inform technological development in the agricultural carbon sequestration sector as well as future negative emissions policies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Safety and Security Defense-in-Depth for Nuclear Power Plants

This report describes the risk-informed technical elements that will contribute to a defense-in-depth assessment for cybersecurity. Risk-informed cybersecurity must leverage the technical elements of a risk-informed approach appropriately in order to evaluate cybersecurity risk insights. HAZCADS and HAZOP+ are suitable methodologies to model the connection between digital harm and process hazards. Risk assessment modeling needs to be expanded beyond HAZCADS and HAZOP+ to consider the sequence of events that lead to plant consequences. Leveraging current practices in PRA can lead to categorization of digital assets and prioritizing digital assets commensurate with the risk. Ultimately, the culmination of cyber hazard methodologies, event sequence modeling, and digital asset categorization will facilitate a defense-in-depth assessment of cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Short-Depth QAOA circuits and Quantum Annealing on Higher-Order Ising Models (Rev.2)

The Quantum Alternating Operator Ansatz (QAOA) and Quantum Annealing (QA) are quantum algorithms that are both based on the adiabatic theorem and both have the goal of sampling the optimal solution(s) of combinatorial optimization problems. Quantum annealing has been physically instantiated on D-Wave devices using superconducting flux qubits, and QAOA can be programmed on digital gate-model quantum computers such as the programmable superconducting transmon qubits devices of the IBMQ series, for instance ibm washington. QAOA and QA address the same types of problems, but it is unclear how they will scale to large problem sizes and to larger and higher-fidelity quantum computers. In this article, we present a direct comparison between QAOA, one and two rounds, run on all 127 qubits of ibm washington and QA run on D-Wave Advantage system4.1 and Advantage system6.1. The problems which allow for this comparison are random Ising model problems whose connectivity matches the heavy hexagonal lattice topology of ibm washington and the Pegasus graph connectivity of the two D-Wave devices. We create two classes of problem instances for this comparison: one with higher order terms (ZZZ variable interactions), linear terms, and quadratic terms, and a separate problem type with only linear and quadratic terms. Our QAOA circuits are novel and extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of ibm washington’s heavy hexagonal lattice and can be applied to future heavy-hex chips. We also test the effectiveness of the error suppression technique digital dynamical decoupling on the QAOA circuits. The QAOA circuits compiled to ibm washington are composed of several thousand circuit instructions, approximately 3, 000 depending on the details of the circuit, making these some the largest quantum circuits ever executed on a digital quantum processor. QAOA and QA are compared against the classical heuristic algorithm of simulated annealing and all problem instances are exactly solved using CPLEX in order to evaluate which samplers, if any, correctly found the ground state solution(s) of the problem instances. We find that (i) QA outperforms QAOA on all problem instances, (ii) QAOA samples the problems better than random sampling, and (iii) QAOA angle computation exhibits clear parameter concentration across the ensemble of Ising models.

127 Qubits↗
DOE OSTI2023

Downhole Sensing and Event-Driven Sensor Fusion for Depth-of-Cut Based Autonomous Fault Response and Drilling Optimization

Achieving robust and efficient drilling is a critical part of reducing the cost of geothermal energy exploration and extraction. Drilling performance is often evaluated using one or more of three key metrics: depth of cut (DOC), rate of penetration (ROP), and mechanical specific energy (MSE). All three of these quantities are related to each other. DOC refers to the depth a bit penetrates into rock during drilling. This is an important quantity for estimating bit behavior. ROP is the simply the DOC multiplied by the rotational rate, and represents how quickly the drill bit is advancing through the ground. ROP is often the parameter used for drilling control and optimization. Finally, MSE provides insight into drilling efficiency and rock type. MSE calculations rely on ROP, drilling force, and drilling torque. Surface-based sensors at the top of the drill are often used to measure all these quantities. However, top-hole measurements can deviate substantially from the behavior at the bit due to lag, vibrations, and friction. Therefore, relying only on top-hole information can lead to suboptimal drilling control. In this work, we describe recent progress towards estimating ROP, DOC, and MSE using down-hole sensing. We assume down-hole measurements of torque, weight-on-bit (WOB). Our hypothesis is that these measurements can provide more rapid and accurate measures of drilling performance. We show how a multi-layer perceptron (MLP) machine learning algorithm can provide rapid and accurate performance when evaluated on experimental data taken from Sandia’s Hard Rock Drilling Facility. In addition, we implement our algorithms on an embedded system intended to emulate a bottom-hole-assembly for sensing and estimation. Our experimental results show that DOC can be estimated accurately and in real-time. These estimates when combined with measurements for rotary speed, torque, and force can provide improved estimates for ROP and MSE. These results have the potential to enable better drilling assessment, improved control, and extended component lifetimes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

The Role of Normal Stress and Shear Stress Heterogeneity in the Inferred Depth-Independence of Stress Drop

Earthquake stress drops are inferred to be independent of their source depth,contradicting standard linear scaling predictions for frictional stick-slip modelsof earthquakes, assuming increasing fault normal stress due to rockoverburden. Here, we examine the scaling between averaged stress drops andincreasing normal stress for simulated earthquakes sequences in continuumrate-and-state fault models. Our models exhibit a power-law-like scaling that isweaker than the linearity predicted by traditional friction models. This resultoccurs as the fault dimension becomes increasingly larger than the earthquakenucleation scale with increasing normal stress. Consequently, the averagedbehavior of ruptures becomes increasingly dominated by conditions for rupturepropagation, reflecting more heterogeneous shear stress conditions. As naturalfaults can be considerably larger than the smallest earthquakes they host, suchweaker scaling between averaged rupture conditions and normal stress maypartially explain the lack of an inferred depth dependence of earthquake stressdrops.

Yang, Minghan↗
DOE OSTI2026

Block Island Environmental Monitoring Data (Weather, Waves & Sensor Depth)

This dataset contains meteorological, oceanographic, and sensor-depth data collected near Block Island during the 2016 and 2017 RODEO field seasons to provide environmental context for concurrent acoustic and survey operations. It includes buoy-derived wind and wave time series, in-situ water temperature logger records, and depth (pressure) records from a sensor mounted on the vertical line array.

17 WIND ENERGY↗
DOE OSTI2023

Summary Data for paper titled: Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Summary Data

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

Naasko, Katherine I↗
DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Sequence Data

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

Naasko, Katherine I↗
DOE OSTI2024

New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

Abstract We present the Ly α Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040–1600 Å based on the red side of the Ly α emission line (1216–1600 Å). We developed synthetic spectra based on a Gaussian mixture model representation of nonnegative matrix factorization (NMF) coefficients. These coefficients were derived from high-resolution, low-redshift ( z < 0.2) Hubble Space Telescope/Cosmic Origins Spectrograph (COS) quasar spectra. We supplemented this COS-based synthetic sample with an equal number of DESI Year 5 mock spectra. LyCAN performs extremely well on testing sets, achieving a median error in the forest region of 1.5% on the DESI mock sample, 2.0% on the COS-based synthetic sample, and 4.1% on the original COS spectra. LyCAN outperforms principal component analysis (PCA) and NMF-based prediction methods using the same training set by 40% or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of 2.1 ≤ z ≤ 4.2 and perform an absolute measurement of the evolution of the effective optical depth. This is the largest sample employed to measure the optical depth evolution to date. We fit a power law of the form τ ( z ) = τ 0 ( 1 + z ) γ to our measurements and find τ 0 = (2.46 ± 0.14) × 10 −3 and γ = 3.62 ± 0.04. Our results show particular agreement with high-resolution, ground-based observations around z = 2, indicating that LyCAN is able to predict the quasar continuum in the forest region with only spectral information outside the forest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microtopographic and Depth Controls on Active Layer Chemistry in Arctic Polygonal Ground: Supporting Data

Polygonal ground is a signature characteristic of the Arctic, and permafrost thaw can potentially generate substantial feedbacks to Arctic ecosystems and climate. This study describes the first comprehensive spatial examination of active layer biogeochemistry that extends across high- and low-centered polygons and their features, including depth. Water chemistry measurements were made on active layer water samples collected near Barrow, Alaska during summer, 2012. Several significant differences in chemistry were observed between high- and low-centered polygons suggesting polygon types may be useful for landscape-scale geochemical classification. However, differences were found for polygon features (centers and troughs) for analytes that were not significant for type, suggesting that finer scale features control biogeochemistry in a different way than polygon type. Depth variations were also significant, demonstrating important multi-dimensional aspects of polygonal ground biogeochemistry. These results have major implications for understanding how polygonal ground ecosystems function, and how they may respond to future change. This data package includes one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Vegetation Warming Experiment: 15N Uptake Experiment Environmental Observations and Thaw Depth, Utqiagvik (Barrow), Alaska, 2018

This dataset consists of measured soil thaw depth, soil temperature, soil moisture, and Arctagrostis latifolia height in vegetation warming experiment enclosures and paired control plots. Vegetation warming chambers (Zero Power) were deployed on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. These chambers (Figure 1) consistently elevated air temperatures by approximately 4°C using a self-venting system described by Lewin et al (2017). Five chambers were deployed from June 17, 2018 to September 24, 2018 on the BEO within a 1 km2 area centered on 71.275N, -156.641W. Each chamber was co-located with an ambient plot where temperatures were not manipulated on patches of tundra containing the target species Arctagrostis latifolia. An intensive field campaign in late July investigated the impact of warming had on A. latifolia biomass, chemistry, and uptake of 15N labeled ammonia that was injected into the surface soils for one week. Initial measurements were taken on July 21, 2018. Harvest occurred on July 27, 2018. Water-extractable nutrients in soils were measured in July following harvests of A. latifolia plants and underlying soils. Availability of ammonia, nitrate, and phosphate throughout the growing season was measured by extracting nutrients bound to anion and cation binding resins deployed from July through September. Environmental variables (thaw depth, surface soil temperatures, surface soil moisture) were measured. Leaf traits and root traits of A. latifolia were also measured. Included are two *.csv data files and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thaw depth, soil moisture, and vegetation height, Teller and Kougarok sites, Seward Peninsula, Alaska, 2022

Thaw depth, soil moisture, and vegetation height sampled from locations on the Teller MM27, Kougarok MM80, and Kougarok MM82 NGEE-Arctic sites, Seward Peninsula, Alaska. These data were collected in support of the ongoing NGEE-Arctic and NASA ABoVE data synthesis work. Samples were collected in July 2022, including 6 transects covering the entire Teller MM27 watershed and 4 transects covering 4 thaw ponds at Kougarok MM80 and MM82. This data package includes sample information and thaw depth, soil moisture, vegetation height data (.csv). Metadata files include data descriptions (_dd.csv) for tabular data and file level metadata (.csv). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PSTN-054: Updated estimates of the Rubin system throughput and expected LSST image depth

This document presents updated estimates, as of May 2022, of the Rubin system throughput and compares them to throughput requirements from the LSST Science Requirements Document. In addition, it uses these estimates to forecast LSST's median single-visit and co-added image depths for the current (OpSim v2.0) baseline LSST cadence simulation: (23.8, 24.5, 24.0, 23.4, 22.7, 22.0) and (25.6, 26.9, 26.9, 26.4, 25.6, 24.8) in ugrizy, respectively. In addition, it uses these estimates to forecast LSST’s median single-visit and co-added image depths for the current baseline LSST cadence simulation. Estimated system performance relies on actual measurements of the performance of various system hardware components, and on simulations where measurements are still unavailable. The updated performance estimates meet all the relevant requirements from the LSST Science Requirements Document. As our knowledge of the as-buit system continues to improve, updates to these estimates will continue to be provided.

79 ASTRONOMY AND ASTROPHYSICS↗