SEARCH · Search NASA

Results for “Distributed Estimation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,261 records · Page 70

All sources NASA NTRS DOE OSTI

DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Residual mean field model of valence quarks in the nucleon

Abstract We develop a non-perturbative model for valence parton distribution functions (PDFs) based on the mean field interactions of valence quarks in the nucleonic interior. The main motivation for the model is to obtain a mean field description of the valence quarks as a baseline to study the short range quark–quark interactions that generate the high x tail of PDFs. The model is based on the separation of the valence three-quark cluster and residual system in the nucleon. Then the nucleon structure function is calculated within the effective light-front diagrammatic approach introducing nonperturbative light-front valence quark and residual wave functions. Within the model a new relation is obtained between the position, $$x_p$$ x p , of the peak of $$xq_V(x)$$ x q V ( x ) distribution of the valence quark and the effective mass of the residual system, $$m_R$$ m R , in the form: $$x_{p} \approx {1\over 4} (1-{m_R\over m_N})$$ x p ≈ 1 4 ( 1 - m R m N ) at starting $$Q^2$$ Q 2 . This relation explains the difference in the peak positions for d- and u-quarks through the expected difference of residual masses for valence d- and u-quark distributions. The parameters of the model are fixed by fitting the calculated valence quark distributions to the phenomenological PDFs. This allowed us to estimate the overall mean field contribution in baryonic and momentum sum rules for valence d- and u-quarks. Finally, the evaluated parameters of the non-perturbative wave functions of valence 3q-cluster and residual system can be used in calculation of other quantities such as nucleon form factors, generalized partonic and transverse momentum distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

The detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method, we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and point-spread function classes. Using three energy bins spanning 0.3–1, 1–3, and 3–10 GeV, we identify 270{sub −10}{sup +30} point sources inside a 40{sup ∘}×40{sup ∘} region around the NGP above our point-source inclusion limit of 3×10{sup −11} cm{sup −2} s{sup −1} sr{sup −1} GeV{sup −1} at the 1–3 GeV energy bin. Modeling the flux distribution as a power law, we infer the slope to be −1.92{sub −0.05}{sup +0.07} and estimate the contribution of point sources to the total emission as 18{sub −2}{sup +2}%. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Evaluation of Rooftop Solar Potential in Chernihiv and Lviv, Ukraine, and Efficacy of High-Resolution 3D Data Digital Twins [Slides]

NREL evaluated rooftop solar photovoltaic (PV) siting opportunities in the cities of Chernihiv and Lviv, Ukraine, leveraging very-high-resolution 3D elevation data to calculate technical potential. The study assessed total rooftop solar capacity and annual energy production. In Chernihiv and Lviv, 116,503 buildings were analyzed for their rooftop solar potential. The buildings in the study areas include a mixture of residential, commercial, and industrial buildings, characterized by diverse roof shapes and sizes. The total estimated rooftop solar capacity is 332 MWDC in Chernihiv and 873 MWDC in Lviv with annual energy production up to 376.2 GWhDC in Chernihiv and 995.5 GWhDC in Lviv. This accounting provides a clear estimate of the potential rooftop solar installations that could be realized under optimal conditions, considering both technical constraints and the geographic distribution of available rooftop space. Related to Russia's invasion of Ukraine, NREL estimates loss from buildings damaged or destroyed of 2,754 buildings, 20.15 MWDC of capacity, and 22,869 MWhDC of annual energy production in Chernihiv as well as a loss of 1,316 buildings, 34.49 MWDC of capacity, and 39,369 MWhDC of annual energy production in Lviv. The study also assessed the feasibility of adapting this methodology on a national scale using either simulated digital surface models (DSMs) or a digital twin approach. While the very-high-resolution DSM provided more precise results, the simulated DSM demonstrated reasonable accuracy for broader applications in modeling aggregated distributed solar supply. The feasibility of using a digital twin for Ukraine's national rooftop solar potential is considered promising, with certain limitations in areas with highly variable building stock and heavy war damage.

14 SOLAR ENERGY↗

DOE OSTI2020

Sputter-deposited low-stress boron carbide films

Significant challenges have been faced in the manufacturing of low-stress B 4 C films. Here, we demonstrate a set of process parameters for direct-current magnetron sputter deposition of smooth, high-purity, amorphous B 4 C films with near-zero total residual stress and with thicknesses up to 10 μm. Films are characterized by a combination of high-energy ion scattering, x-ray diffraction, electron microscopy, and stress measurements based on substrate curvature. In order to facilitate the process transfer to other sputtering geometries, the favorable deposition conditions are correlated with distributions of landing energies and incident angles of depositing species estimated by Monte Carlo simulations of ballistic collisions and gas phase atomic transport. Based on such simulations, a decrease in compressive stress with increasing Ar working gas pressure is attributed to the corresponding broadening of the angular distribution of depositing species and associated atomic shadowing effects.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Maximum Likelihood Estimation: Some Basics

The maximum likelihood estimation is a general estimation procedure. It is often compared to estimation procedures like the ordinary least squares regression or generalized method of moments, to name a few. We discuss some basics about the maximum likelihood estimation, its advantages and disadvantages, and provide an example application to a gamma distribution function.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Maximum Likelihood Estimation: Some Basics

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

In this note, we present a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stochastic distribution control.

42 ENGINEERING↗

DOE OSTI2018

Neutron Capture Energies for Flux Normalization and Approximate Model for Gamma-Smeared Power

The Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) neutronics simulator MPACT has used a single recoverable fission energy for each fissionable nuclide assuming that all recoverable energies come only from fission reaction, for which capture energy is merged with fission energy. This approach includes approximations and requires improvement by separating capture energy from the merged effective recoverable energy. This report documents the procedure to generate recoverable neutron capture energies and the development of a program called CapKappa to generate capture energies. Recoverable neutron capture energies have been generated by using CapKappa with the evaluated nuclear data file (ENDF)/B-7.0 and 7.1 cross section and decay libraries. The new capture kappas were compared to the current SCALE-6.2 and the CASMO-5 capture kappas. These new capture kappas have been incorporated into the Simplified AMPX 51- and 252-group libraries, and they can be used for the AMPX multigroup (MG) libraries and the SCALE code package. The CASL VERA neutronics simulator MPACT does not include a gamma transport capability, which limits it to explicitly estimating local energy deposition from fission, neutron, and gamma slowing down and capture. Since the mean free path of gamma rays is typically much longer than that for the neutron, and the total gamma energy is about 10% to the total energy, the gamma-smeared power distribution is different from the fission power distribution. Explicit local energy deposition through neutron and gamma transport calculation is significantly important in multi-physics whole core simulation with thermal-hydraulic feedback. Therefore, the gamma transport capability should be incorporated into the CASL neutronics simulator MPACT. However, this task will be timeconsuming in developing the neutron induced gamma production and gamma cross section libraries. This study is to investigate an approximate model to estimate gammasmeared power distribution without performing any gamma transport calculation. A simple approximate gamma smearing model has been investigated based on the facts that pinwise gamma energy depositions are almost flat over a fuel assembly, and assembly-wise gamma energy deposition is proportional to kappa-fission energy deposition. The approximate gamma smearing model works well for single assembly cases, and can partly improve the gamma smeared power distribution for the whole core model. Although the power distributions can be improved by the approximate gamma smearing model, still there is an issue to explicitly obtain local energy deposition. A new simple approach or gamma transport/diffusion capability may need to be incorporated into MPACT to estimate local energy deposition for more robust multi-physics simulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗

DOE OSTI2023

Robust neural network-enhanced estimation of local primordial non-Gaussianity

When applied to the nonlinear matter distribution of the universe, neural networks have been shown to be very statistically sensitive probes of cosmological parameters, such as the linear perturbation amplitude σ 8 . However, when used as a “black box,” neural networks are not robust to baryonic uncertainty. We propose a robust architecture for constraining primordial non-Gaussianity f NL , by training a neural network to locally estimate σ 8 , and correlating these local estimates with the large-scale density field. We apply our method to N-body simulations, and show that σ⁡(f NL ) is 3.5 times better than the constraint obtained from a standard halo-based approach. Finally, we show that our method has the same robustness property as large-scale halo bias: baryonic physics can change the normalization of the estimated f NL , but cannot change whether f NL is detected.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Three‐Phase CO 2 Flow in a Basalt Fracture Network

Abstract Geologic CO 2 sequestration in basalt reservoirs is predicated on permanent CO 2 isolation via rapid mineralization reactions. This process is supported by a substantial body of evidence, including laboratory experiments documenting rapid mineralization rates, regional storage estimates indicating large, accessible storage reservoirs, and two successful pilot‐scale studies. Nevertheless, there remains significant uncertainty in the behavior of CO 2 flow within basalt fracture networks, particularly in the context estimating physical trapping potential in early time and as CO 2 undergoes phase change. In this study, a Monte Carlo numerical model is designed to simulate a supercritical CO 2 plume infiltrating a low‐permeability flood basalt entablature. The fracture network model is based on outcrop‐scale LiDAR mapping of Columbia River Basalt, and CO 2 flow is simulated within fifty equally probable realizations of the fracture network. The spatial distribution of fracture permeability for each realization is randomly drawn from a basalt aperture distribution, and ensemble results are analyzed with e‐type estimates to compute mean and standard deviation of fluid pressure and CO 2 saturation. Results of this model after 10 years of simulation suggest that (1) CO 2 flow converges on a single dominant flow path, (2) CO 2 accumulates at fracture intersections, and (3) variability in permeability can account for a 1.6 m depth interval within which free CO 2 may change phase from supercritical fluid to subcritical liquid or gas. In the context of CO 2 sequestration in basalt, these results suggest that physical CO 2 trapping may be substantially enhanced as carbonate minerals precipitate within the basalt fracture network.

Gierzynski, Alec O.↗

DOE OSTI2019

Statistical analyses of reservoir and fracturing parameters for a multifractured shale oil reservoir in Mississippi

The use of multifractured horizontal wells has improved the efficiency of hydrocarbon extraction from shale gas and oil plays. It is highly desirable to estimate the characteristics of the reservoir and well fracturing through production data analysis. Production rate transient data from 16 wells in the Mississippi sections were analyzed to estimate local reservoir permeability and hydraulic fracture parameters. Key factors affecting well productivity have been identified for optimizing future well completion. Based on the theory of distance of investigation during transient linear flow, the reservoir matrix permeability of TMS was estimated to be between 53 nd and 210 nd, averaging at 116 nd. The matrix permeability follows the lognormal distribution and is considered homogeneous according to the coefficient of variation. The fracture half-length was estimated using the matrix permeability data from the rate transient analysis. The fracture half-length was found to have a mean value of 234 ft with a standard deviation of 66 ft in a normal distribution. The fracture conductivity was back-calculated by matching pseudosteady production rate data to Guo et al's (SPE Reserve Eval Eng.12, 2009, 879) productivity model for boundary-dominated flow. The fracture conductivity was estimated to range from 0.4 md-ft to 3.2 md-ft, averaging at 1.4 md-ft. Based on Guo et al's (SPE Reserve Eval Eng.12, 2009, 879) well productivity model applied to TMS condition, well productivity can be improved significantly by increasing fracture length and conductivity. Future TMS wells should be completed with conductivity values of greater than 10 md-ft.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2023

Markov Chain Monte Carlo Parameter Estimation of Deflagration Losses in a Rotating Detonation Engine

One of the practical challenges of the studies of rotating detonation engines (RDEs) is the direct estimation of losses from experimental measurements. This study attempts at resolving this limitation by combining a reduced order model (ROM) of the detonation wave characteristics with a Markov chain Monte Carlo parameter estimation framework. The model considers simple deflagration losses and the overall impact of deflagration on RDE performance. To evaluate this model, a Markov Chain Monte Carlo (MCMC) sampling technique was applied to estimate the loss parameters within the model for a set of conditions operated in hydrogen-air over a range of mass flow rates and equivalence ratios. The MCMC parameter estimation framework allowed for the determination of a posterior distribution of the loss parameters for each test condition, an examination of the correlation between the loss parameters and measured performance metrics of the RDE, and an uncertainty propagation of these parameters. The predicted model loss parameters were then compared to indirect experimental measurements of the deflagration combustion fractions to evaluating the validity of the approach, and shed light on the benefits and drawbacks of the model, measurement techniques, and the estimation framework.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Evaluating next-generation intensity–duration–frequency curves for design flood estimates in the snow-dominated western United States

Civil infrastructure such as culverts and bridges are commonly designed using precipitation-based intensity-duration-frequency (PREC-IDF) curves, which assume that the occurrence of precipitation is in the form of rainfall and immediately available for the rainfall-runoff process. In snow-dominated regions, where most winter precipitation occurs as snow that melts during spring to early summer, the use of standard PREC-IDF curves may lead to substantial underestimation of design floods and high failure risk of infrastructure. In this context, we developed next-generation IDF (NG-IDF) curves that characterize the actual water reaching the land surface (i.e., rainfall plus snowmelt) to enhance standard infrastructure design in snow-dominated regions. This study evaluates the performance of NG-IDF curves coupled with U.S. Department of Agriculture Technical Release 55 hydrologic model in estimating design floods for 246 snowy locations in different hydroclimate regimes of the western United States. Design flood estimates from a well-validated continuous simulation using a physics-based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), were used as the performance benchmark. Compared with the benchmark estimates, the standard PREC-IDF curves led to substantial errors in design flood estimates while the NG-IDF curves significantly reduced these errors. For example, the averaged error in the 50-year design flood estimates over the 246 locations was reduced from 31% with the use of PREC-IDF curves to 12% with the use of NG-IDF curves. Despite the different model structures, the single-event NG-IDF approach versus the continuous simulation DHSVM did not exhibit statistically significant differences in 91% of the 246 locations for the 50-year design flood estimates. We find this indicates a satisfactory performance of NG-IDF curves to estimate design flow under the conditions tested in the snow-dominated western United States. This article also presents technical suggestions and the limitations of infrastructure design using NG-IDF curves for regulatory agencies and practicing engineers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

$t\bar{t}b\bar{b}$ at the LHC: on the size of corrections and b -jet definitions

We report on the calculation of the next-to-leading order QCD corrections to the production of a $t\bar t$ pair in association with two heavy-flavour jets. We concentrate on the di-lepton $t\bar t$ decay channel at the LHC with $\sqrt{s}$ = 13 TeV. The computation is based on pp → $e^+ν_eμ^-\bar{ν}_μb\bar{b}b\bar{b}$ matrix elements and includes all resonant and non-resonant diagrams, interferences and off-shell effects of the top quark and the W gauge boson. As it is customary for such studies, results are presented in the form of inclusive and differential fiducial cross sections. We extensively investigate the dependence of our results upon variation of renormalisation and factorisation scales and parton distribution functions in the quest for an accurate estimate of the theoretical uncertainties. We additionally study the impact of the contributions induced by the bottom-quark parton density. Results presented here are particularly relevant for measurements of $t\bar{t}H(H → b\bar{b})$ and the determination of the Higgs coupling to the top quark. In addition, they might be used for precise measurements of the top-quark fiducial cross sections and to investigate top-quark decay modelling at the LHC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗