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A steady state model for the distribution of stress and temperature on the San Andreas Fault
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Liquid-fuel-distribution and fuel-state effects on combustion performance of a single tubular combustor
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Global Distribution of the Phase State and Mixing Times within Secondary Organic Aerosol Particles in the Troposphere Based on Room-Temperature Viscosity Measurements
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Additional development of a probe to measure velocity distributions in the exhaust of steady state plasma accelerators Final report
Probe to measure velocity distributions in exhaust of steady-state plasma accelerators
Preserving Tracer Correlations in Moment-Based Atmospheric Transport Models
A linear non-diffusive algorithm for advective transport is developed that greatly improves the detail at which aerosols and clouds can be represented in atmospheric models. Linear advection schemes preserve tracer correlations but the most basic linear scheme is rarely used by atmospheric modelers on account of its excessive numerical diffusion. Higher-order schemes are in widespread use, but these present new problems as nonlinear adjustments are required to avoid occurrences of negative concentrations, spurious oscillations, and other non-physical effects. Generally successful at reducing numerical diffusion during the advection of individual tracers, for example, particle number or mass, the higher-order schemes fail to preserve even the simplest of correlations between interrelated tracers. As a result, important attributes of aerosol and cloud populations including radial moments of particle size distributions, molecular precursors related through chemical equilibria, aerosol mixing state, and distribution of cloud phase are poorly represented. We introduce a new transport scheme, minVAR, that is both non-diffusive and preservative of tracer correlations, thereby combining the best features of the basic and higher-order schemes while enabling new features such as the tracking of sub-grid information at arbitrarily fine scales with high computational efficiency.
Recent Advances in Radiation-Induced Actinide Redox Chemistry
The actinide series boasts many unique physical and chemical features worthy of both fundamental and applied study. However, the chemical influence of their inherent radiation field is often overlooked, especially as we begin to explore the late actinides in more detail than ever possible before. From the perspective of used nuclear fuel reprocessing, the absorption of ionizing radiation induces the formation of a variety of transient and steady-state excited states, radicals, ions, and molecular degradation products, many of which are highly redox active and can lead to significant changes in a reprocessing solvent system’s physical and chemical properties. For example, radiolysis of the actinides can drive steady-state redox distributions and the formation of non-traditional oxidation states which can complicate their separation and recovery from fission products. This scenario is further exacerbated when complexation is taken into account. Consequently, a molecular-level understanding of radiation effects on the actinides over multiple time, distance, and material domains is essential for supporting innovation in used nuclear fuel reprocessing technologies. Attaining this knowledge necessitates a firm grasp of actinide radiation chemistry to develop predictive, mechanistic, multiscale models to support engineering efforts. Presented here are several recent studies that highlight recent advances in actinide radiation chemistry, in particular, the effect of actinide complexation on ligand reactivity towards radiation-induced transients.
Photoelectron fluxes in the Martian ionosphere
Calculations are presented of the steady-state photoelectron distribution in the upper atmosphere of Mars, consistent with the neutral upper atmosphere and ionosphere particle concentrations and temperatures measured by Viking 1. Uncertainties in the calculations affect the thermal electron gas heating rate. Major conclusions are that (1) over most of the altitude range of the Martian ionosphere, the steady-state photoelectron flux amplitude is larger than that in the earth's, so that photoelectron-impact-excited airglow on Mars is generally more significant than it is on earth; (2) the steady-state photoelectron energy distribution in the Martian ionosphere is softer and more structured than that in the terrestrial ionosphere; (3) photoelectron impact ionization contributes about 30% to the total ionization rate in the Martian ionosphere; and (4) photoelectron impact excitation contributes 20-30% of the CO2(+) and CO zenith airglow emissions on Mars.
Probing the 𝑗 dependence of angular distributions and 𝑁=20 shell rigidity via the 36 S (𝑝,𝑑) 35 S reaction
An investigation of the N=20 36 S nucleus has been performed through a detailed study of the 3 6S (p,d)3 5S neutron-removal reaction, employing a 66-MeV proton beam at iThemba Laboratory for Accelerator Based Sciences and an innovative target design. Using the high-resolution K = 600 magnetic spectrometer, 98 states in 3 5S were identified up to 16-MeV excitation energy, including 47 previously unobserved states. Angular distributions and spectroscopic factors, including isobaric analog-state contributions, were extracted for 81 levels. A pronounced j dependence in the angular distributions of ℓ=2 states provides refined insights into the spin-orbit splitting. Finite-range adiabatic distorted-wave approximation calculations qualitatively reproduce the observed j dependence. Comparisons of the measured 1d 5/2 spectroscopic strength distribution with large-scale shell-model and ab initio calculations show good agreement overall, and the robustness of the N=20 shell closure in 36 S is confirmed when comparing the relatively low fp orbital occupancies in 40 Ca and 3 6S across the Fermi surface. This study underscores the utility of neutron-removal reactions in probing nuclear structure and the Fermi surface of sd nuclei and beyond. The findings advance our understanding of shell evolution and offer constraining data for theoretical models.
Measurement of the top quark Yukawa coupling from $\mathrm{t\bar{t}}$ kinematic distributions in the dilepton final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
A measurement of the Higgs boson Yukawa coupling to the top quark is presented using proton-proton collision data at s=13 TeV, corresponding to an integrated luminosity of 137 fb-1, recorded with the CMS detector. The coupling strength with respect to the standard model value, Yt, is determined from kinematic distributions in tt¯ final states containing ee, μμ, or eμ pairs. Variations of the Yukawa coupling strength lead to modified distributions for tt¯ production. In particular, the distributions of the mass of the tt¯ system and the rapidity difference of the top quark and antiquark are sensitive to the value of Yt. The measurement yields a best fit value of Yt=1.16-0.35+0.24, bounding Yt<1.54 at a 95% confidence level.
Quantum Scattering Study of Ro-Vibrational Excitations in N+N(sub 2) Collisions under Re-entry Conditions
A three-dimensional time-dependent quantum dynamics approach using a recently developed ab initio potential energy surface is applied to study ro-vibrational excitation in N+N2 exchange scattering for collision energies in the range 2.1- 3.2 eV. State-to-state integral exchange cross sections are examined to determine the distribution of excited rotational states of N(sub 2). The results demonstrate that highly-excited rotational states are produced by exchange scattering and furthermore, that the maximum value of (Delta)j increases rapidly with increasing collision energies. Integral exchange cross sections and exchange rate constants for excitation to the lower (upsilon = 0-3) vibrational energy levels are presented as a function of the collision energy. Excited-vibrational-state distributions for temperatures at 2,000 K and 10,000 K are included.
On the Use of Smart Meter Data to Estimate the Voltage Magnitude on the Primary Side of Distribution Service Transformers
This paper develops a novel method to estimate the voltage magnitude on the primary side of distribution service transformers. The proposed method relies exclusively on smart meters, and therefore it is fully data-driven. This is an important feature because electric utilities have detailed models of only the primary network - that is, the network between the distribution substation and the primary side of service transformers that are installed closer to end-customer sites. The network that connects the secondary side of service transformers to end-customer sites, referred to as the secondary network, is simply represented by a lumped load. For each secondary network, the proposed method uses data acquired from only 2 smart meters: the closest and the farthest-in the sense of electrical distance - from the service transformer. As a reference to this feature, the proposed method is named SM2Vp. To our knowledge, this is the first time a method is shown to provide actionable information for realtime operation and control of power distribution grids using only two smart meters per secondary network. This is important because utilities have experienced barriers in managing and using large data sets for real-time operation and control. SM2Vp is primarily intended to provide pseudo-measurements for distribution system state estimation, but it can also be used directly for voltage control schemes. The performance of SM2Vp is demonstrated by numerical simulations carried out on three secondary network synthetic models and by using field data provided by a utility partner serving customers in southwestern California. A maximum relative error of approximately 3.9% or less is observed for the primary voltage magnitude estimates in all numerical experiments.
Max-independent set and the quantum alternating operator ansatz
he maximum-independent set (MIS) problem of graph theory using the quantum alternating operator ansatz is studied. We perform simulations on the Rigetti Forest simulator for the square ring, K 2,3 , and K3,3 graphs and analyze the dependence of the algorithm on the depth of the circuit and initial states. The probability distribution of observation of the feasible states representing maximum-independent sets is observed to be asymmetric for the MIS problem, which is unlike the Max-Cut problem where the probability distribution of feasible states is symmetric. For asymmetric graphs, it is shown that the algorithm clearly favors the independent set with the larger number of elements even for finite circuit depth. Finally, we also compare the approximation ratios for the algorithm when we choose different initial states for the square ring graph and show that it is dependent on the choice of the initial state.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
Categorizing distributed wind energy installations in the United States to inform research and stakeholder priorities
Abstract Background Distributed wind energy adoption in the United States can contribute to the diverse portfolio of energy technologies needed to achieve ambitious decarbonization goals. However, with limited deployment to date, the current distributed wind market must be better understood; these efforts will support the range of stakeholders who will drive successful deployment. This article first distinguishes three categories of distributed wind from existing literature: (1) behind the meter, (2) intended for explicit local load, and (3) physically distributed. A novel methodology to classify individual wind installations into each of these categories is then presented and applied to two data sets of wind installations in the United States to categorize and illuminate distinct segments in the distributed wind market. Results Physically distributed installations, constituted by small to moderately sized projects serving local loads on distribution systems solely because of their proximity to them, account for the highest amount of capacity but the lowest number of installations out of the three categories. The inverse is true for behind-the-meter installations, which are used to serve on-site loads. Installations intended for explicit local load, which are interconnected on the utility side of the distribution system and intentionally built to provide energy to loads on the same distribution system, rank in the middle for both installed capacity and number of installations. Conclusions Distributed wind energy deployment in the United States is geographically widespread, but the extent to which a single category is developed in each state varies. Policies, wind resources, and broad energy technology trends contribute to these deployment patterns. By identifying the extent to which each category of installations exists, decision-makers are empowered with data necessary to tailor research and development programs and address stakeholder priorities through policy and other means, ultimately supporting future deployment.
Geographic Access to Radiation Therapy Facilities in the United States
The current distribution of radiation therapy (RT) facilities in the United States is not well established. A comprehensive inventory of U.S. RT facilities was last assessed in 2005, based on data from state regulatory agencies and dosimetric quality assurance bodies. We updated this database to characterize population-level measures of geographic access to RT and analyze changes over the past 15 years.