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At least 1,135 records · Page 63

2HDM neutral scalars under the LHC

Two Higgs Doublet Models (2HDM) provide a simple framework for new physics models with an extended Higgs sector. The current LHC results, including both direct searches for additional non-Standard Model (SM) Higgs bosons, as well as precision measurements of the SM-like Higgs couplings, already provide strong constraints on the 2HDM parameter spaces. In this paper, we examine those constraints for the neutral scalars in the Type-I and Type-II 2HDM. In addition to the direct search channels with SM final states: H/A → ff¯¯¯, VV, Vh, hh, we study in particular the exotic decay channels of H/A → AZ/HZ once there is a mass hierarchy between the non-SM Higgses. We found that H/A → AZ/H Z channel has unique sensitivity to the alignment limit region which remains unconstrained by conventional searches and Higgs precision measurements. This mode also extends the reach at intermediate tβ for heavy mA that are not covered by the other direct searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

79 ASTRONOMY AND ASTROPHYSICS↗

Dynamic reduced-order models of integrated physics-specific systems for carbon sequestration

In this study, CO 2 leakage from a deep storage reservoir into a shallow groundwater aquifer through a wellbore is simulated using physics-specific models conceptualized as nonisothermal two-phase flow in a deep storage reservoir and through the wellbore, and reactive transport in the aquifer. Supercritical CO 2 is injected into the reservoir subsystem with uncertain permeabilities of reservoir and injection strategies (rate and duration as decision variables). The simulated pressure and CO 2 saturation at an uncertain location are connected to the wellbore model as a boundary condition to output CO 2 flux to the reactive transport model in the shallow aquifer. Uncertainties are propagated from the deep reservoir model, to the wellbore leakage model, and eventually to the reactive transport model, thus contributing to system performance. Finally, to minimize the risk associated with the CO 2 leakage, we develop dynamic reduced order models for quantifying and managing the uncertainty propagation.

58 GEOSCIENCES↗

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗

Exploring Western North Pacific Tropical Cyclone Activity in the High‐Resolution Community Atmosphere Model

Abstract High‐resolution climate models (∼28 km grid spacing) can permit realistic simulations of tropical cyclones (TCs), thus enabling their investigation in relation to the climate system. On the global scale, previous works have demonstrated that the Community Atmosphere Model (CAM) version 5 presents a reasonable TC climatology under prescribed present‐day (1980–2005) forcing. However, for the Western North Pacific (WNP) region, known biases in simulated TC genesis frequency and location under‐represent the basin's dominant share in observations. This study addresses these model biases in WNP by evaluating WNP TCs in a decadal simulation, and exploring potential improvements through nudging experiments. Among the major environmental controls of TC genesis, the lack of mid‐level moisture is identified as the leading cause of the deficit in simulated WNP TC genesis over the Pacific Warm Pool. Subsequent seasonal experiments explore the effect of constraining the large‐scale environment on TC development by nudging WNP temperature field toward reanalysis at various strengths. Temperature nudging elicits a significant response in TC genesis and intensity development, as well as in moisture and convection over the Warm Pool. These responses are sensitive to the choice of nudging timescale. Overall, the nudging experiments demonstrate that improvements in the large‐scale environment can lead to improvements in simulated TCs, suggesting future model developments in relation to model physics. In this way, the potential improvements in model fidelity will contribute to the understanding of how the mean state of current or future climates may give rise to extremes such as TCs.

58 GEOSCIENCES↗

Indirect drive ignition at the National Ignition Facility

This paper reviews scientific results from the pursuit of indirect drive ignition on the National Ignition Facility (NIF) and describes the program's forward looking research directions. In indirect drive on the NIF, laser beams heat an x-ray enclosure called a hohlraum that surrounds a spherical pellet. X-ray radiation ablates the surface of the pellet, imploding a thin shell of deuterium/tritium (DT) that must accelerate to high velocity (v > 350 km s –1 ) and compress by a factor of several thousand. Since 2009, substantial progress has been made in understanding the major challenges to ignition: Rayleigh Taylor (RT) instability seeded by target imperfections; and low-mode asymmetries in the hohlraum x-ray drive, exacerbated by laser-plasma instabilities (LPI). Requirements on velocity, symmetry, and compression have been demonstrated separately on the NIF but have not been achieved simultaneously. We now know that the RT instability, seeded mainly by the capsule support tent, severely degraded DT implosions from 2009–2012. Experiments using a 'high-foot' drive with demonstrated lower RT growth improved the thermonuclear yield by a factor of 10, resulting in yield amplification due to alpha particle heating by more than a factor of 2. Furthermore, large time dependent drive asymmetry in the LPI-dominated hohlraums remains unchanged, preventing further improvements. High fidelity 3D hydrodynamic calculations explain these results. Future research efforts focus on improved capsule mounting techniques and on hohlraums with little LPI and controllable symmetry. In parallel, we are pursuing improvements to the basic physics models used in the design codes through focused physics experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Real-time out-of-plane artifact subtraction tomosynthesis imaging using prior CT for scanning beam digital x-ray system

Purpose: The scanning beam digital x-ray system (SBDX) is an inverse geometry fluoroscopic system with high dose efficiency and the ability to perform continuous real-time tomosynthesis in multiple planes. This system could be used for image guidance during lung nodule biopsy. However, the reconstructed images suffer from strong out-of-plane artifact due to the small tomographic angle of the system. Methods: The authors propose an out-of-plane artifact subtraction tomosynthesis (OPAST) algorithm that utilizes a prior CT volume to augment the run-time image processing. A blur-and-add (BAA) analytical model, derived from the project-to-backproject physical model, permits the generation of tomosynthesis images that are a good approximation to the shift-and-add (SAA) reconstructed image. A computationally practical algorithm is proposed to simulate images and out-of-plane artifacts from patient-specific prior CT volumes using the BAA model. A 3D image registration algorithm to align the simulated and reconstructed images is described. The accuracy of the BAA analytical model and the OPAST algorithm was evaluated using three lung cancer patients’ CT data. The OPAST and image registration algorithms were also tested with added nonrigid respiratory motions. Results: Image similarity measurements, including the correlation coefficient, mean squared error, and structural similarity index, indicated that the BAA model is very accurate in simulating the SAA images from the prior CT for the SBDX system. The shift-variant effect of the BAA model can be ignored when the shifts between SBDX images and CT volumes are within ±10 mm in the x and y directions. The nodule visibility and depth resolution are improved by subtracting simulated artifacts from the reconstructions. The image registration and OPAST are robust in the presence of added respiratory motions. The dominant artifacts in the subtraction images are caused by the mismatches between the real object and the prior CT volume. Conclusions: Their proposed prior CT-augmented OPAST reconstruction algorithm improves lung nodule visibility and depth resolution for the SBDX system.

60 APPLIED LIFE SCIENCES↗

WTEC Panel Report on International Assessment of Research and Development in Simulation-Based Engineering and Science

This WTEC panel report assesses the international research and development activities in the field of Simulation- Based Engineering and Science (SBE&S). SBE&S involves the use of computer modeling and simulation to solve mathematical formulations of physical models of engineered and natural systems. SBE&S today has reached a level of predictive capability that it now firmly complements the traditional pillars of theory and experimentation/observation. As a result, computer simulation is more pervasive today – and having more impact – than at any other time in human history. Many critical technologies, including those to develop new energy sources and to shift the cost-benefit factors in healthcare, are on the horizon that cannot be understood, developed, or utilized without simulation. A panel of experts reviewed and assessed the state of the art in SBE&S as well as levels of activity overseas in the broad thematic areas of life sciences and medicine, materials, and energy and sustainability; and in the crosscutting issues of next generation hardware and algorithms; software development; engineering simulations; validation, verification, and uncertainty quantification; multiscale modeling and simulation; and SBE&S education. The panel hosted a U.S. baseline workshop, conducted a bibliometric analysis, consulted numerous experts and reports, and visited 59 institutions and companies throughout East Asia and Western Europe to explore the active research projects in those institutions, the computational infrastructure used for the projects, the funding schemes that enable the research, the collaborative interactions among universities, national laboratories, and corporate research centers, and workforce needs and development for SBE&S.

97 MATHEMATICS AND COMPUTING↗

Coupled Mass and Heat Transport Models for Nuclear Fuels using Thermodynamic Calculations

We have developed a theoretical framework for incorporation of thermodynamic equilibrium calculations into engineering models of nuclear fuel problems based on the finite element method (FEM). The framework is based on linear irreversible thermodynamics and their incorporation into integral equations for balancing energy and mass. For coupled energy and mass transport, thermodynamic calculations are performed at the nodal points of the FEM mesh and are used at the integration points to calculate the driving forces and fluxes for irreversible thermodynamic problems. The consistent coupling of thermodynamic models with isotopic calculations and other nuclear fuel physics models has been described. The approach has been demonstrated on simple unit problems. Implementation in practical nuclear fuel problems will be developed follow-on research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

Simplifying Geospatial Workflows with GeoGridFusion

Growing demands to understand PV deployment and reliability across an expanding range of climates, along with increasing computational power, are driving the need for simplified computing tools that support large-scale analysis and geospatial workflows. While existing open-source libraries and PV system modeling tools offer extensive collections of empirical and physical models, they often lack the ability to scale to large geospatial datasets. The tool presented here, GeoGridFusion, enables PV modelers to store and utilize gridded geospatial data from sources such as NSRDB, PVGIS, and others. GeoGridFusion harmonizes diverse datasets, taxonomies, and nomenclatures, and includes utilities that support intuitive geospatial area selection.

14 SOLAR ENERGY↗

Mass and energy flow in the solar chromosphere and corona

The work reviews some investigations into the mass and energy flow in the solar chromosphere and corona; the objective of these investigations is the development of a physical model that will not only account for the physical conditions in the outer atmosphere of the sun, but can also be applied to the study of the outer atmospheres of other stars. Particular attention is given to mass and energy flow in regions with weak and strong magnetic fields, to observational evidence for wave heating and systematic mass flows, and to heating mechanisms. Consideration is given throughout to mechanisms of energy input and energy loss.

Withbroe, G. L.↗

Quantum chemical calculation of the equilibrium structures of small metal atom clusters

A decomposition of the molecular energy is presented that is motivated by the atom superposition and electron delocalization physical model of chemical binding. The energy appears in physically transparent form consisting of a classical electrostatic interaction, a zero order two electron exchange interaction, a relaxation energy, and the atomic energies. Detailed formulae are derived in zero and first order of approximation. The formulation extends beyond first order to any chosen level of approximation leading, in principle, to the exact energy. The structure of this energy decomposition lends itself to the fullest utilization of the solutions to the atomic sub problems to simplify the calculation of the molecular energy. If nonlinear relaxation effects remain minor, the molecular energy calculation requires at most the calculation of two center, two electron integrals. This scheme thus affords the prospects of substantially reducing the computational effort required for the calculation of molecular energies.

Kahn, L. R.↗

Development of an engineering level prediction method for high angle of attack aerodynamics

The present work is concerned with predicting the unsteady flow considered to be the cause of the structural failure of twin vertical tail aircraft. An engineering tool has been produced for high angle of attack aerodynamics using the simplest physical models. The main innovation behind this work is its emphasis on the modeling of two key aspects of the dominant physics associated with high angle-of-attack airflows, namely unsteady separation and vortex breakdown.

Reisenthel, Patrick H.↗

Comparison of Coupled Radiative Flow Solutions with Project Fire 2 Flight Data

A nonequilibrium, axisymmetric, Navier-Stokes flow solver with coupled radiation has been developed for use in the design or thermal protection systems for vehicles where radiation effects are important. The present method has been compared with an existing now and radiation solver and with the Project Fire 2 experimental data. Good agreement has been obtained over the entire Fire 2 trajectory with the experimentally determined values of the stagnation radiation intensity in the 0.2-6.2 eV range and with the total stagnation heating. The effects of a number of flow models are examined to determine which combination of physical models produces the best agreement with the experimental data. These models include radiation coupling, multitemperature thermal models, and finite rate chemistry. Finally, the computational efficiency of the present model is evaluated. The radiation properties model developed for this study is shown to offer significant computational savings compared to existing codes.

Olynick, David R.↗

Comparison of Coupled Radiative Navier-Stokes Flow Solutions with the Project Fire II Flight Data

A nonequilibrium, axisymmetric, Navier-Stokes flow solver with coupled radiation has been developed to use in the design of thermal protection systems for vehicles where radiation effects are important. The present method has been compared with an existing flow and radiation solver and with the Project Fire II experimental data. Very good agreement has been obtained over the entire Fire II trajectory with the experimentally determined values of the stagnation radiation intensity in the .2 to 6.2 eV range and with the total stagnation heating. The agreement was significantly better than previous numerical predictions. The effects of a number of flow models are examined to determine which combination of physical models produces the best agreement with the experimental data. These models include radiation coupling, multi-temperature thermal models, finite-rate chemistry, and a quasi-steady-state or Boltzmann assumption for the calculation of the excited electronic states. Finally, the computational efficiency of the present model is evaluated. The radiation properties model developed for this study is shown to offer significant computational savings compared to existing codes.

Olynick, David R.↗