Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “State 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.

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

At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses. Published by the American Physical Society 2024

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Effective Mass from Seebeck Coefficient

Abstract Engineering semiconductor devices requires an understanding of the effective mass of electrons and holes. Effective masses have historically been determined in metals at cryogenic temperatures estimated using measurements of the electronic specific heat. Instead, by combining measurements of the Seebeck and Hall effects, a density of states effective mass can be determined in doped semiconductors at room temperature and above. Here, a simple method to calculate the electron effective mass using the Seebeck coefficient and an estimate of the free electron or hole concentration, such as that determined from the Hall effect, is introduced here is the Seebeck effective mass, n H is the charge carrier concentration measured by the Hall effect ( n H = 1/ eR H , R H is Hall resistance) in 10 20 cm −3 , T is the absolute temperature in K, S is the Seebeck coefficient, and k B / e = 86.3 μV K −1 . This estimate of the effective mass can aid the understanding and engineering of the electronic structure as it is largely independent of scattering and the effects of microstructure (grain boundary resistance). It is particularly helpful in characterizing thermoelectric materials.

Snyder, Gerald Jeffrey↗
DOE OSTI2024

A Platform for Ultra-Fast Proton Probing of Matter in Extreme Conditions

Recent developments in ultrashort and intense laser systems have enabled the generation of short and brilliant proton sources, which are valuable for studying plasmas under extreme conditions in high-energy-density physics. However, developing sensors for the energy selection, focusing, transport, and detection of these sources remains challenging. This work presents a novel and simple design for an isochronous magnetic selector capable of angular and energy selection of proton sources, significantly reducing temporal spread compared to the current state of the art. The isochronous selector separates the beam based on ion energy, making it a potential component in new energy spectrum sensors for ions. Analytical estimations and Monte Carlo simulations validate the proposed configuration. Due to its low temporal spread, this selector is also useful for studying extreme states of matter, such as proton stopping power in warm dense matter, where short plasma stagnation time (<100 ps) is a critical factor. The proposed selector can also be employed at higher proton energies, achieving final time spreads of a few picoseconds. This has important implications for sensing technologies in the study of coherent energy deposition in biology and medical physics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Characterizing non-Markovian and coherent errors in quantum simulation

Quantum simulation of many-body systems, particularly using ultracold atoms and trapped ions, presents a unique form of quantum control—it is a direct implementation of a multi-qubit gate generated by the Hamiltonian. As a consequence, it also faces a unique challenge in terms of benchmarking, because the well-established gate benchmarking techniques are unsuitable for this form of quantum control. Here we show that the symmetries of the target many-body Hamiltonian can be used not only to benchmark but to characterize experimental errors in the quantum simulation. We use our results to develop protocols to characterize these errors, which can be implemented using state-of-the-art technology. We consider two forms of errors: (i) unitary errors arising out of systematic errors in the applied Hamiltonian and (ii) canonical non-Markovian errors arising out of random shot-to-shot fluctuations in the applied Hamiltonian. We show that the dynamics of the expectation value of the target Hamiltonian itself, which is ideally constant in time, can be used to characterize these errors. In the presence of errors, the expectation value of the target Hamiltonian shows a characteristic thermalization dynamics, when it satisfies the operator thermalization hypothesis (OTH). That is, an oscillation in the short time followed by relaxation to a steady-state value in the long time limit. We show that while the steady-state value can be used to characterize the coherent errors, the amplitude of the oscillations can be used to estimate the non-Markovian errors. We prove a sandwich theorem to establish a linear relation between the amplitude of the oscillations and the magnitude of the non-Markovian errors. Moreover, by varying the initial state, we show that the steady state values can be used to completely construct the generator of the coherent errors. Using these results, we develop two experimental protocols to characterize the unitary errors based on these results, one of which requires single-qubit addressing and the other one doesn't. We also develop a protocol to characterize non-Markovian errors. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.

machine learning↗
DOE OSTI2021

Unraveling peculiar magnetism and band topology in Mn 3 Sb

Magnetic, pseudogap, topological, magnetostructural, and elastic behaviors of Mn 3 Sb have been unraveled. The ferrimagnetism (FIM) is described by localized and delocalized electron magnetism resulting in different magnetic moments on Mn atoms, confirming the neutron diffraction data. The identified magnetostructural properties are due to the non-equivalent Mn atoms in its lowest symmetry structure. The electronic structure is also unique due to variable valance states of Mn atoms. The magnetic moment (4.10 μB) of non-equivalent Mn1 atom is antiparallely aligned with the magnetic moments (2.34 μB) of Mn 2 and Mn 3 atoms. The estimated Curie temperature, TC, is higher than the room temperature, which may have above the room temperature applications in spintronic devices. The band structure and density of states (DOS) show the characteristics of band topology (opening of a gap in Dirac-like band features) and pseudo-gap, respectively, around the Fermi level. While expanding the unit cell, the tetragonal FIM ground state transforms to the cubic primitive FIM phase, however, the contraction transforms it to the L1 2 ferromagnetic (FM) phase. The estimated elastic constants, bulk to shear modulus ratio, and elastic anisotropy factor indicate that Mn3Sb exhibits mechanically stable, ductile, and anisotropic behaviors, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Reductive Dynamic and Static Excited State Quenching of a Homoleptic Ruthenium Complex Bearing Aldehyde Groups

A new homoleptic Ru polypyridyl complex bearing two aldehyde groups on each bipyridine ligand, [Ru(dab) 3 ](PF 6 ) 2 , where dab is 4,4′-dicarbaldehyde-2,2′-bipyridine, was synthesized, characterized, and utilized for iodide photo-oxidation studies. In acetonitrile (CH 3 CN) solution, the complex displayed an intense metal-to-ligand charge transfer (MLCT) absorbance maximum at 475 nm (ε = 22,000 M –1 cm –1 ) and an infrared (IR) band at 1712 cm –1 assigned to the pendent aldehyde groups. Visible light excitation in air-saturated solution resulted in room temperature photoluminescence (PL) with a maximum at 675 nm, a quantum yield, ϕ PL = 0.048, and an excited state lifetime, τ ο = 440 ns, from which radiative and nonradiative relaxation rate constants were extracted, k r = 9.1 × 10 4 s –1 and knr = 1.8 × 10 6 s –1 . Pulsed visible light excitation yielded transient UV–vis and IR absorption spectra consistent with an MLCT excited state; relaxation occurred with the maintenance of two isosbestic points in the visible region, and a lifetime that agreed with that measured by time-resolved PL. Cyclic voltammetry studies in a CH 3 CN solution with 0.1 M TBAPF 6 electrolyte revealed a quasi-reversible oxidation, E°(Ru III/II ) = +1.25 V vs. Fc +/0 , and three sequential one-electron reductions at −1.10, −1.25, and −1.54 V vs. Fc +/0 . Here, an excited state reduction potential of E°(Ru *2+/+ ) = +0.89 V vs. Fc +/0 was estimated with the Rehm–Weller expression. Titration of tetrabutylammonium iodide, TBAI, into a CD 3 CN solution of [Ru(dab) 3 ](PF 6 ) 2 resulted in significant shifts in the aldehyde H atom and 3,3′-biypridyl resonances that were analyzed with a 1:1 equilibrium model, from which K eq = 460 M –1 was extracted, increasing to 5800 M –1 when the solvent was changed to acetone-d 6 . Iodide titrations resulted in a significant quenching of the [Ru(dab) 3 ] *2+ lifetime and quantum yield in both CH 3 CN and acetone solvents. In CH 3 CN, the quenching was mainly dynamic and well described by the Stern–Volmer model, from which a quenching rate constant, k q , of 4.5 × 10 10 M –1 s –1 and an equilibrium constant, K eq , of 8.3 × 10 3 M –1 were obtained. In acetone, the static quenching pathway by iodide was greatly enhanced, with a K eq of 1.2 × 10 4 M –1 and a higher k q of 9.2 × 10 10 M –1 s –1 .

Aldehydes↗
DOE OSTI2023

Parametric estimation of Poisson's ratio for thin hinged-hinged plates

Cornu's method is an elegant calculation besieged by an impractical approach to obtain accurate estimates for Poisson's ratio. Conventionally, Cornu's method requires several components, and each component can adversely affect the accuracy of the measurement. Furthermore, Cornu's conventional method requires a long beam because beams with short length-to-width ratios cause the estimate of Poisson's ratio to diverge from the true value of Poisson's ratio. We believe that, with the right modifications, Cornu's method can become an attractive approach to obtaining precise estimates for Poisson's ratio from mode shapes. Here we use finite element simulations to show how to use Cornu's method to estimate Poisson's ratio from a mode shape. Our modified Cornu's method removes knife-edges and loading components for a hinged-hinged plate under steady-state excitation. Given the true value of Poisson's ratio, as a performance specification, we show that simple parametric expressions can fit estimates for Poisson's ratio for different length-to-width ratios of thin hinged-hinged plates. Additionally, we show that estimates for Poisson's ratio from higher modes align with the results from the first mode and explain our expectation for this outcome. Furthermore, our results challenge the idea that anticlastic, monoclastic, and synclastic deformation uniquely correspond to positive, zero, and negative estimates of Poisson's ratio, respectively. With the emergence of materials-by-design, we expect that this parametric technique will be able to assist in experimental qualification of thin beam and plate structures with respect to the desired value of Poisson's ratio.

42 ENGINEERING↗
DOE OSTI2026

Value of Geothermal Energy Storage for Supply-Side and Demand-Side Applications

This report presents the results of a study examining the value potential for geothermal energy storage (GES), a long-duration energy storage resource that stores thermal and/or geomechanical energy in the subsurface. GES could benefit the overall U.S. power system by temporally shifting electricity generation (supply-side) or meeting building heating and cooling load (demand-side). This report analyzes supply-side and demand-side opportunities independently because of differences in applications and models. Currently there is significant uncertainty about the development costs for GES, with only a limited number of demonstration plants for electric energy storage and building heating and cooling storage developments. In this report, we estimate the value of supply-side and demand-side GES to the bulk power system in the contiguous United States. Because of the significant uncertainty about GES development costs, this analysis does not consider GES deployment costs but instead focuses on the value of GES to the U.S. electricity system. The estimated values of GES provide reference points for economically competitive commercial cost targets. Supply-side GES is modeled as part of an enhanced geothermal system (EGS) generation plant in NREL's Regional Energy Deployment System (ReEDS) capacity expansion model (Ho et al. 2021). In contrast to conventional geothermal plants, which generate constant power, EGS plants have unique features that may allow for in-reservoir energy storage for flexible generation. Demand-side GES for heating and cooling, including seasonal hot and cold storage and short-duration heat pump storage, is incorporated into a price-taker model using Cambium electricity marginal cost projections. To establish an upper bound for the value of GES, analysis focused on favorable scenarios for storage with high generation from zero marginal cost, variable renewable energy resources. High penetrations of variable renewable energy generation can increase hourly electricity price variability, which increases the value of temporal energy arbitrage for storage technologies like GES.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Potential of U.S. Marine and Offshore Wind Energy to Power Marine Carbon Dioxide Removal

The goal of this project was to provide a preliminary feasibility assessment of powering different marine carbon dioxide removal (mCDR), carbon capture (mCC), and carbon sequestration (mCS) strategies with marine energy. The investigation focused on methods with clear power requirements at sea beyond monitoring such as artificial upwelling, deep ocean storage, electrochemical (eChem) mCDR and mCC, offshore microalgae cultivation, and seaweed farming and sinking. The project found that eChem mCDR powered by marine energy and offshore wind energy available in the United States could meet global CDR scales needed by 2040 and 2050 to limit warming to 1.5 degrees C by 2100 and marine energy alone could greatly contribute to reaching scales needed by 2040. Note that this preliminary estimate assumes that it is possible to harvest all the marine and offshore wind resources available in the United States with existing technology options. Though the biological methods were limited in scale they still hold promise in developing carbon negative fuels and products which can reduce emissions in the short term.

afforestation↗
DOE OSTI2022

Time dependent supervisory control update with FARM using rolling window

This report describes improvements to the Feasible Actuator Range Modifier (FARM) component of the RAVEN-based HYBRID framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON plug-in that solves the power dispatch problem. The solution to the dispatch problem involves economically optimal dispatches that satisfy limits on production variables and their rates of variation (explicit constraints) as well as process variables tied to the service life of equipment (implicit constraints). FARM serves to validate or confirm that a HERON solution for explicit constraints also satisfies the implicit constraints. FARM-alpha was released by Argonne National Laboratory in January 2021 followed by FARM-Beta in January 2022 with the latter providing increased flexibility for the user. In this report, FARM-Gamma, the latest version of the code, is described. The major improvement is the implementation of a system identification algorithm based on the Dynamic Mode Decomposition with Control (DMDc) coupled with a “Rolling Window” scheme that allows obtaining linear time-varying state-space models. This feature equips FARM with the most accurate approximation of system dynamics, and it relieves the user from the burden of performing an exhaustive off-line characterization of the dynamics. FARM-Gamma capabilities are assessed by solving the power dispatch problem for a representative IES unit. The simulation times corresponding to the different releases are estimated and compared. These values capture the increasing computational burden of the successively higher fidelity state-space models adopted by FARM-Alpha, FARM-Beta and FARM-Gamma. The code implementation provides significant flexibility, i.e., the user can always select the most suitable version of FARM according to the problem to be solved and the available computational resources. It is anticipated that FARM will play a role in addressing several future IES applications. We outline how it can support the coordinated management and safe operation of a nuclear plant coupled to industrial processes to produce hydrogen and synfuels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Evaluating the sustainability of the 2017 US biofuel industry with an integrated techno-economic analysis and life cycle assessment

The United States produced more than 66.2 billion Liters of biofuel, primarily in the form of corn ethanol (61 billion Liters) and soybean biodiesel (6 billion Liters), for the transportation industry in 2017. No work currently exists that evaluates both the economic and environmental outcomes of this industry with adequate geospatial resolution and national scope. In this study, a model framework is constructed to perform Techno-Economic Analysis and Life Cycle Assessment using high-resolution input datasets to provide a granular estimation of corn ethanol and soybean biodiesel sustainability performance in most counties in the United States in 2017 (the most recent year for which data is available). Results show a total life cycle, greenhouse gas emission volume of 99.5 million Mg CO 2-eq produced in 2017 by these two biofuels (94.8 million Mg from corn ethanol, and 4.7 million Mg from soybean biodiesel), excluding indirect land use change emissions. Further, this volume corresponds to a combined carbon intensity of 65.3 gCO 2-eq /MJ, which is a greenhouse gas improvement compared to petroleum-based gasoline (the carbon intensity of conventional gasoline is 93.0 gCO 2-eq /MJ). However, these biofuels are produced at an additional economic cost ($1.8 billion over the displaced gas/diesel). Additionally, these biofuels generate unfavorable impacts in other environmental categories when compared to petroleum-based fuels, particularly in areas of water, land, and nitrogen intensity. This work enables stakeholders to assess the specific value of biofuels as a means of emission reduction and determine if the attained improvements are worth the associated opportunity costs.

09 BIOMASS FUELS↗
DOE OSTI2023

Simulation-based anomaly detection for multileptons at the LHC

Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Manipulating Cu vacancy in Yb triangular lattice Materials: Investigating the quantum disordered compound CuYbSe 2 and spin glass Cu 2.73 ⁢YbP 2

Yb-based triangular lattice materials have attracted significant attention due to their potential for hosting exotic magnetic quantum states. In this paper, we report the structural, magnetic, and thermodynamic properties of two Yb triangular lattice compounds with vacancy disorder at nonmagnetic Cu sites: CuYbSe 2 and Cu 2.73 ⁢YbP 2 . Single-crystal x-ray diffraction determines notable Cu-site vacancies in both materials. Unlike a single Cu site with 0.5 occupancy in CuYbSe 2 , Cu 2.73 ⁢YbP 2 only presents 27% vacancies on one of three Cu sites. Magnetic susceptibility measurements indicate dominant antiferromagnetic interactions between Yb 3+ ions. The effective magnetic moment is consistent with an effective spin-1/2 state of Yb 3+ . CuYbSe 2 exhibits strong magnetic anisotropy between in-plane and out-of-plane directions with the estimated exchange interactions 𝐽 𝑎 /𝑘 B = 6.48 K and 𝐽 𝑐 /𝑘 B = 2.55 K. No magnetic ordering is observed down to 0.4 K, supporting its quantum disordered ground state and candidacy as a quantum spin liquid. In contrast, Cu 2.73 ⁢YbP 2 shows clear evidence of spin freezing at 4.5 K in both magnetization and heat capacity data, indicating a spin glass state. The field-induced magnetic orders are found in CuYbSe 2 . These results highlight the critical role of structural vacancies in perturbing Yb-based triangular lattices. In conclusion, we demonstrate that while the intrinsic Yb triangular lattices are comparable, the presence of Cu vacancies can fundamentally alter the magnetic ground state—tuning the system between quantum disordered and spin glass behavior.

Frustrated magnetism↗
DOE OSTI2020

Extinction Dynamics of the Foot-and-Mouth Disease Virus Carrier State Under Natural Conditions

Foot-and-mouth disease (FMD) is one of the most economically important livestock diseases worldwide. Following the clinical phase of FMD, a large proportion of ruminants remain persistently infected for extended periods. Although extinction of this carrier state occurs continuously at the animal and population levels, studies vary widely in their estimates of the duration of persistent infection. There is a need for robust statistical models to capture the dynamics of persistent infection for the sake of guiding FMD control and trade policies. The goal of the current study was to develop and assess statistical models to describe the extinction of FMD virus (FMDV) persistent infection using data from primary longitudinal studies of naturally infected cattle and Asian buffalo in Vietnam and India. Specifically, accelerated failure time (AFT) models and generalized linear mixed models (GLMM) were developed to predict the probability of persistent infection in seropositive animals and identified carriers at the individual animal level at sequential time points after outbreaks. The primary studies were analyzed by country and combined using an individual-participant data meta-analysis approach. The models estimated similar trends in the duration of persistent infection for the study/species groups included in the analyses, however the significance of the trends differed between the models. The overall probabilities of persistent infection were similar as predicted by the AFT and GLMM models: 6 months: 99% (AFT) /80% (GLMM), 12 months: 51% (AFT) /32% (GLMM), 18 months: 6% (AFT) /5% (GLMM), 24 months: 0.8% (AFT) /0.6% (GLMM). These models utilizing diverse and robust data sets predict higher probabilities of persistence than previously published, suggesting greater endurance of carriers subsequent to an outbreak. This study demonstrates the utility of statistical models to investigate the dynamics of persistent infection and the importance of large datasets, which can be achieved by combining data from several smaller studies in meta-analyses. Results of this study enhance current knowledge of the FMDV carrier state and may inform policy decisions regarding FMDV persistent infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Latent bone modelling for estimation of plutonium concentration in skeleton of former nuclear workers

Abstract The skeleton is a major plutonium retention site in the human body. Estimation of the total plutonium activity in the skeleton is a challenging problem. For most tissue donors at the United States Transuranium and Uranium Registries, a limited number of bone samples is available. The skeleton activity is calculated using plutonium activity concentration (Cskel) and skeleton weight. In this study, latent bone modelling was used to estimate Cskel from the limited number of analysed bone samples. Data from 13 non-osteoporotic whole-body donors were used to develop latent bone model (LBM) to estimate Cskel for seven cases with four to eight analysed bone samples. LBM predictions were compared to Cskel estimated using an arithmetic mean in terms of accuracy and precision. For the studied cases, LBM offered a significant reduction of uncertainty of Cskel estimate.

Environmental Sciences & Ecology↗
DOE OSTI2020

Efficient Nighttime Biogenic SOA Formation in a Polluted Residual Layer

Abstract Organic nitrates formed from nighttime reaction between anthropogenic nitrate radicals (NO3) and biogenic volatile organic compounds (BVOCs) are potentially an important but highly uncertain source of secondary organic aerosol (SOA). Here we report on the enhanced nighttime biogenic SOA formation observed in a polluted residual layer over Sacramento, California, the morning of 15 June 2010 during the Carbonaceous Aerosols and Radiative Effects Study (CARES). Trajectory analysis showed that the residual layer air, containing trace amounts of isoprene and monoterpenes left over after nighttime oxidation, was influenced by the San Francisco Bay Area emissions the previous evening. The residual layer aerosol was also enriched in nitrate, with about 64% of it estimated to be in the form of organic nitrates. The nitrate:organic mass ratio of the SOA was about 0.47±0.044, which corresponds to the range typically found in isoprene mononitrates. Assuming the SOA was composed of organic mononitrates, its nominal molecular weight was estimated at 186±11 g mol-1, consistent with the highly functionalized isoprene hydroxynitrates that have been observed in the particle phase in the southeast United States. Overall, our findings show that the efficiency of nighttime biogenic SOA formation, expressed as the change in organic aerosol mass relative to carbon monoxide (?OA/?CO), was about 100 µg m-3 ppmv, which falls in the range 77-157 µg m-3 ppmv previously estimated for enhanced daytime SOA formation from mixed anthropogenic and biogenic emissions during CARES.

Zaveri, Rahul A.↗
DOE OSTI2025

Parametric and Nonparametric Models of U.S. Cost Overruns for Nuclear Power Plants

This study presents new data-driven models to estimate the effect of capacity on the percentage of cost overruns in the United States for nuclear power plant construction projects before and after the Three Mile Island accident. Parametric and nonparametric models have been developed that describe the significant shifts in nuclear energy costs during the dynamic environment. Employing a contemporary descriptive methodology and a quantitative analysis, we furnish a comprehensive overview of the alterations in cost overrun distribution and show the changes observed in other pivotal metrics alongside cost overruns. Our emphasis lies in documenting the fluctuations in cost overruns alongside nuclear reactor capacity levels and the increase of the overnight capital costs to build nuclear reactors. Our results show that increasing the size of nuclear reactors is not a factor statistically significant to decrease the percentage of cost overruns, and the probit model results provide evidence that an increase in size increases the probability of having cost overruns larger than 100% (double the estimated cost). We also compare our findings to two other regions: Asia and Europe.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗