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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Observations of Boundary Layer Convergence Lines and Associated Updrafts in the U.S. Southern Great Plains

Abstract Boundary layer convergence lines (CLs) are highly effective at deep-convection initiation (DCI), suggesting that their associated updraft properties differ from those of more widespread turbulent updrafts in the planetary boundary layer (PBL). This study exploits observations at the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) observatory in Oklahoma from 2011 to 2016 to quantify CL properties and their relation to turbulent PBL eddies preceding CL arrival. Two independent methods for estimating CL properties are developed at two locations in the SGP region, both relying on the assumption of a 2D circulation in the CL-normal plane but using different combinations of instruments. The first (the radar method) relies mainly on scanning radar data and is applied to 61 CLs passing near a high-resolution scanning radar based in Nardin, Oklahoma, while the second (the surface method) relies mainly on surface wind data and is applied to 68 CLs crossing the SGP facility in nearby Lamont, Oklahoma. Mean daytime (1000–1900 LST) CL width (∼2 km) and convergence magnitude (∼0.003 s −1 ) are similar for both methods, and mean daytime CL depth is ∼0.75 km. The two methods disagree at night (0000–1000 and 1900–2400 LST), where the surface method estimates wider and weaker CLs than the radar method. This difference may stem from the radar beam overshooting the shallow, highly stable nocturnal PBL. The largest CL updrafts are slightly wider (∼20%) and stronger (∼40%) than the largest PBL updrafts in the pre-CL period, generating 50%–100% larger updraft mass fluxes over most of the PBL depth. Significance Statement Deep convection is commonly initiated by boundary layer convergence lines (CLs), which are associated with intense surface-based wind convergence and strong updrafts that may lift air to saturation. Although CLs form regularly, they are far less common than ordinary, short-lived turbulent thermals in the daytime boundary layer. To better understand why CLs are so effective at deep-convection initiation, we observationally quantify their morphologies and strengths and compare these properties to those of surrounding turbulent updrafts. Perhaps surprisingly, the CLs are found to exhibit only slightly larger scales and strengths as the turbulent updrafts. Although these marginal increases help to explain the preference for storms to initiate along CLs, they likely are not the whole story.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Combining Emergent Constraints for Climate Sensitivity

A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ENSO-Induced Teleconnection: Process-Oriented Diagnostics to Assess Rossby Wave Sources and Ambient Flow Properties in Climate Models

Climate model fidelity in representing ENSO-induced teleconnection is assessed with process-oriented diagnostics that examine a chain of processes, from equatorial Pacific precipitation to the midlatitude circulation pattern over the Pacific–North American regions. Such processes are rarely addressed during model development. Using an upper-tropospheric divergent level, local vorticity gradient of the ambient zonal flow (∂ 2 U¯/∂y 2 ) and a restoring force for Rossby waves (β * ) are estimated, the equivalent barotropic vorticity equation is solved, and an anomalous Rossby wave source (RWS') quantified. The analysis is applied to AMIP5 and AMIP6 simulations. For a realistic circulation response representation, the hypothesis that models accurately represent the strength and location of RWS', and spatial variations in β * is tested. Compared to AMIP5, in AMIP6 there are clear improvements in representing RWS' and β * . To validate the hypothesis, the analysis identifies two metrics: spatially coherent RWS' in the subtropical North Pacific, and longitudes of negative β * over the western-central North Pacific. By projecting these metrics in two and three-dimensional views, improvements or degradations in model versions are apparent. If a model’s fidelity in representing ∂ 2 U¯/∂y 2 and RWS' are compromised, then radiated Rossby waves are reflected more equatorward, resulting in zonally elongated circulation anomalies over the central North Pacific. Thus, during climate model development, applying this analysis frequently will keep a regular check on the fidelity of the modeled response to anomalous El Niño convection in conjunction with changing model ambient flow dependencies. As a result, this analysis is intended to form a process-oriented diagnostics package, a community contribution to the NOAA Model Diagnostics Task Force.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Biogeophysical Effects of Land-Use and Land-Cover Change Not Detectable in Warmest Month

Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (t LU ) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that t LU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Insights into ENSO Diversity from an Intermediate Coupled Model. Part II: Role of Nonlinear Dynamics and Stochastic Forcing

Abstract In this study, we investigate how a single leading linear El Niño–Southern Oscillation (ENSO) mode, as studied in Part I, leads to the irregular coexistence of central Pacific (CP) and eastern Pacific (EP) ENSO, a phenomenon known as ENSO spatiotemporal diversity. This diversity is fundamentally generated by deterministic nonlinear pathways to chaos via the period-doubling route and, more prevailingly, the subharmonic resonance route with the presence of a seasonally varying basic state. When residing in the weakly nonlinear regime, the coupled system sustains a weak periodic oscillation with a mixed CP/EP pattern as captured by the linear ENSO mode. With a stronger nonlinearity effect, the ENSO behavior experiences a period-doubling bifurcation. The single ENSO orbit splits into coexisting CP-like and EP-like ENSO orbits. A sequence of period-doubling bifurcation results in an aperiodic oscillation featuring irregular CP and EP ENSO occurrences. The overlapping of subharmonic resonances between ENSO and the seasonal cycle allows this ENSO irregularity and diversity to be more readily excited. In the strongly nonlinear regime, the coupled system is dominated by regular EP ENSO. The deterministic ENSO spatiotemporal diversity is thus confined to a relatively narrow range corresponding to a moderately unstable ENSO mode. Stochastic forcing broadens this range and allows ENSO diversity to occur when the ENSO mode is weakly subcritical. A close relationship among a weakened mean zonal temperature gradient, stronger ENSO activity, and more (fewer) occurrences of EP (CP) ENSO is noted, indicating that ENSO–mean state interaction may yield ENSO regime modulations on the multidecadal time scale.

Geng, Licheng↗
DOE OSTI2021

Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models

Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long short-term memory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most LSTM applications focus on well-monitored catchments with abundant and high-quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that 1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash–Sutcliffe efficiency above 0.8, even with only 2 years of calibration data; 2) when the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy; 3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting; and 4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. In conclusion, these insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment

Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. Here, we have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Investigations into EGR dilution tolerance in a pre-chamber ignited GDI engine

EGR is a useful means to improve fuel economy in spark-ignited gasoline engines, especially under part-load conditions by means of reducing throttling losses. There is also a significant reduction in NOx emissions due to the reduced peak cylinder temperatures. However, the ability of conventional spark ignition systems to reliably ignite dilute mixtures limits the dilution tolerance due to the onset of combustion instability. In this study, an active pre-chamber system was evaluated at part load condition with various EGR rates using a regular grade full boiling range gasoline. A premixed air-fuel mixture was supplied to the pre-chamber, which assists in scavenging the residual mass fractions to enable reliable ignition of EGR dilute mixtures in the main combustion chamber. A range of pre-chamber injection, air-fuel mixture formulation, and ignition timing strategies were evaluated during the EGR sweeps. In addition, 0-D and 1-D simulations were conducted in GT-Power to assess the thermodynamic state and composition in the pre-chamber, as well as estimate the jet momentum at the various dilution rates. The active pre-chamber extended the EGR dilution limit from 20% for conventional SI to above 30%, with a higher pre-chamber flow rate (more scavenging) resulting in increased combustion stability. A tradeoff between the jet momentum and spark timing was observed when analyzing the GT-Power simulations and the pre-chamber jet momentum was found to decrease with increasing EGR rate. At unstable high EGR operation, no relation could be found between the pre-chamber indicated data and main chamber combustion stability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Practical low-temperature gasoline combustion for very high efficiency off-road, medium- and heavy-duty engines

Low-temperature gasoline combustion (LTGC) with additive-mixing fuel injection (AMFI) is a new combustion strategy that has been demonstrated to deliver 9%–25% better brake thermal efficiency than similar-sized market-leading diesel engines over the operating map. Moreover, the LTGC-AMFI engine shows near-zero smoke, and NOx emissions are 4–100 times lower than those of a diesel, sufficiently low that no aftertreatment, or only passive NOx aftertreatment, would be sufficient (diesel exhaust fluid is not required). LTGC-AMFI combustion is based on kinetically controlled compression ignition of a dilute charge with a variable amount of low-to-moderate fuel stratification. Fast combustion control is provided by adding minute amounts of an ignition-enhancing additive into the fuel each engine cycle to control its reactivity. This strategy was used to operate a medium-duty (MD) LTGC-AMFI engine at loads from idle to 16.3 bar BMEP and speeds from 600 to 2400 rpm with regular E10 gasoline, which covers nearly the entire operating map of a typical MD engine. Turbine-out temperatures were sufficient for an oxidation catalyst to control hydrocarbon and CO emissions. Autonomie simulations over the GEM ARB Transient and the GEM 55 mph Cruise driving cycles for class-6 trucks using this technology showed fuel economies of 8.1 and 11.4 mpg-gasoline-equivalent, respectively, corresponding to 18.6% and 13.4% improvements over a similar-size diesel engine. Engine-out NOx emissions were 0.024 and 0.01 g/bhp-h, respectively, well below current U.S. emission standards. These results show that switching from diesel to LTGC-AMFI engines would greatly reduce greenhouse gas (GHG) emissions for off-road, MD and HD applications, which will continue to rely on combustion engines because electrification is not practical in the foreseeable future. Finally, with their reduced fuel consumption, the lower cost of gasoline compared to diesel fuel, and much lower aftertreatment costs, LTGC-AMFI engines also offer a significantly lower total cost of ownership.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Conducting more inclusive solar geoengineering research: A feminist science framework

Solar geoengineering, or deliberate climate modification, has been receiving increased attention in recent years. Given the far-reaching consequences of any potential solar geoengineering deployments, it is prudent to identify inherent biases, blind spots, and other potential issues at all stages of the research process. Here we articulate a feminist science-based framework to concretely describe how solar geoengineering researchers can be more inclusive of different perspectives and potentially contradictory conclusions, in the process illuminating potential implicit bias and enhancing the conclusions that can be gained from their studies. Importantly, this framework is an adoptable method of practice that can be refined, with the aim of conducting better research in solar geoengineering. As an illustration, we retrospectively apply this framework to a well-read solar geoengineering study (also led by the first author of this study), improving transparency by revealing its implicit values, conclusions made from its evidence base, and the methodologies that study pursues. We conclude here with a set of recommendations for the geoengineering research community whereby more inclusive research can become a regular part of practice. Throughout this process, we illustrate how feminist science scholars can use this approach to study climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multi-year assessment of the impact of ship-borne radiosonde observations on polar WRF forecasts in the Arctic

Abstract To compensate for the lack of conventional observations over the Arctic Ocean, ship-borne radiosonde observations have been regularly carried out during summer Arctic expeditions and the observed data have been broadcast via the global telecommunication system since 2017. With these data obtained over the data-sparse Arctic Ocean, observing system experiments were carried out using a polar-optimized version of the Weather Research and Forecasting (WRF) model and the WRF Data Assimilation (WRFDA) system to investigate their effects on analyses and forecasts over the Arctic. The results of verification against reanalysis data reveal: (1) DA effects on analyses and forecasts; (2) the reason for the year-to-year variability of DA effects; and (3) the possible role of upper-level potential vorticity in delayed DA effects. The overall assimilation effects of the extra data on the analyses and forecasts over the Arctic are positive. Initially, the DA effects are the most apparent in the temperature variables in the middle/lower troposphere, which spread to the wind variables in the upper troposphere. The effects decrease with time but reappear after approximately 120 h, even in the 240-h forecasts. The effects on forecasts vary depending on the proximity of the radiosonde observation locations to the high synoptic variability. The upper-level potential vorticity is known to play an important role in the development of Arctic cyclones, and it is suggested as a possible explanation for the delayed DA effects after about 120 h.

Geology↗
DOE OSTI2024

High-Fidelity Numerical Wave Tank Verification & Validation Study: Wave Generation Through Paddle Motion: Preprint

This paper presents a numerical benchmark study of wave propagation due to a paddle motion using different high-fidelity numerical models, which are capable of replicating the nearly actual physical wave tank testing. A full time series of the measured wave generation paddle motion which was used to generate wave propagation in the physical wave tank will be utilized in each of the models contributed by IEA OES Task 10's participants, which includes both computational fluid dynamics (CFD) and smooth hydrodynamic particle (SPH). The high-fidelity simulations of the physical wave testcase will allow for the evaluation of the initial transient effects from wave ramp-up and its evolution in the wave tank over time for two representative regular waves with varying levels of nonlinearity. A couple of interesting metrics like the predicted wave surface elevation at select wave probes, wave period, and phase-shift in time will be assessed to evaluate the relative accuracy of numerical models versus experimental data within specified time intervals. These models will serve as a guide for modelers in the wave energy community and provide a base case to allow further and more detailed numerical modeling of the fixed Kramer Sphere Cases under wave excitation force wave tank testing.

HYDRO ENERGY,TIDAL AND WAVE POWER↗
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
DOE OSTI2023

Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation

Here we propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We derive the method from a class of projective operator splitting methods fairly recently proposed by Combettes and Eckstein, significantly expanding the known applications of those methods. Our derivation assures convergence for convex problems whose feasible set is compact, subject to some standard regularity conditions and a mild “fairness” condition on subproblem selection. The method’s convergence guarantees are deterministic and do not require randomization, in contrast to other proposed asynchronous variations of progressive hedging. Computational experiments described in an online appendix show the method to outperform progressive hedging on large-scale problems in a highly parallel computing environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Shielding Analyses for SNS Accelerator Power Upgrade

The Proton Power Upgrade (PPU) project [1] at Spallation Neutron Source (SNS) is aiming at increasing the neutron beam intensities at the First Target Station (FTS) and advancing accelerator capability to additionally power a future Second Target Station (STS) [2]. PPU will involve upgrading the SNS accelerator complex to double the currently available proton beam power from 1.4 to 2.8 MW, by increasing the proton beam energy from 1 GeV to 1.3 GeV and by increasing the proton beam current.PPU will enable new science capabilities enhancing the experiment throughput of the 19 existing FTS instruments, which are heavily oversubscribed. STS will include 22 additional neutron scattering instruments. Numerous neutronics analyses are performed to support PPU evaluating the FTS systems for higher beam power and for system upgrades. Part of the PPU scope is an extension of the Ring to Target Beam Transport (RTBT) line, a stub out of the accelerator tunnel for the future Ring to STS Transport (RTST) line[3]. As the RTST tunnel will not be built within the PPU project, the stub will be shielded by a plug, which is planned to be made from regular concrete.In order to provide radiation protection safety, analyses are required to design adequate plug thickness for the stub. The thickness of the stub is driven by the criterion that the area downstream of the plug is “unrestricted access area”, demanding the dose rate downstream the stub be below 0.25 mrem/h at 30 cm from the surface at normal operation and below 20 Rem/hr for accident cases. Additionally, analyses verifying the amount of soil for shielding the accelerator tunnel to the above stated requirements are performed.

Popova, Irina I.↗