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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗
DOE OSTI2017

Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method

The Monte Carlo (MC) method is the 'gold standard' for performing nuclear reactor calculations and criticality safety analyses because of its ability to handle complex geometries and physics. Reactor and criticality safety analyses require solving eigenvalue problems in complex nuclear systems. Once a stationary fission source distribution is obtained in MC simulations, the sample mean of many stationary cycles is calculated. The MC simulation sample mean is typically referred to as a tally. Variance, or standard deviation, of the sample mean/tally is needed to indicate the level of statistical uncertainty in the simulation and to understand the convergence of the sample mean. Current MC codes typically use sample variance to estimate the statistical uncertainty of the simulation and assume that the MC stationary cycles are independent. However, it is known that cycles are correlated, and estimators of the variance that ignore these correlations systematically underestimate the variance. In this paper, we propose a novel method in the Fourier frequency domain to estimate the variance of the sample mean in MC eigenvalue calculations. Results indicate that the proposed estimator can be unbiased compared to the real variance. The proposed estimator requires minimal additional computing effort.

42 ENGINEERING↗
DOE OSTI2016

SU-F-T-122: 4Dand 5D Proton Dose Evaluation with Monte Carlo

Purpose: We evaluated uncertainties in therapeutic proton doses of a lung treatment, taking into account intra-fractional geometry changes, such as breathing, and inter-fractional changes, such as tumor shrinkage and weight loss. Methods: A Monte Carlo study was performed using four dimensional CT image sets (4DCTs) and weekly repeat imaging (5DCTs) to compute fixed RBE (1.1) and variable RBE weighted dose in an actual lung treatment geometry. The MC2 Monte Carlo system was employed to simulate proton energy deposition and LET distributions according to a thoracic cancer treatment plan developed with a 3D-CT in a commercial treatment planning system, as well as in each of the phases of 4DCT sets which were recorded weekly throughout the course of the treatment. A cumulative dose distribution in relevant structures was computed and compared to the predictions of the treatment planning system. Results: Using the Monte Carlo method, dose deposition estimates with the lowest possible uncertainties were produced. Comparison with treatment planning predictions indicates that significant uncertainties may be associated with therapeutic lung dose prediction from treatment planning systems, depending on the magnitude of inter- and intra-fractional geometry changes. Conclusion: As this is just a case study, a more systematic investigation accounting for a cohort of patients is warranted; however, this is less practical because Monte Carlo simulations of such cases require enormous computational resources. Hence our study and any future case studies may serve as validation/benchmarking data for faster dose prediction engines, such as the track repeating algorithm, FDC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Does jackknife scale really matter for accurate large-scale structure covariances?

ABSTRACT The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings impact the precision of the covariance estimate on the correlation function multipoles and the error on the inferred baryon acoustic scale. We compare the measurement with the MultiDark Patchy mock galaxy catalogues, and we also validate it against a set of lognormal mocks with the same survey geometry. We build several jackknife configurations that vary in size and number of resamplings. We introduce the Hartlap factor in the covariance estimate that depends on the number of jackknife resamplings. We also find that it is useful to apply the tapering scheme to estimate the precision matrix from a limited number of resamplings. The results from CMASS and mock catalogues show that the error estimate of the baryon acoustic scale does not depend on the jackknife scale. For the shift parameter α, we find an average error of 1.6 per cent, 2.2 per cent and 1.2 per cent, respectively, from CMASS, Patchy, and lognormal jackknife covariances. Despite these uncertainties fluctuate significantly due to some structural limitations of the jackknife method, our α estimates are in reasonable agreement with published pre-reconstruction analyses. Jackknife methods will provide valuable and complementary covariance estimates for future large-scale structure surveys.

Favole, Ginevra (ORCID:000000028218563X)↗
DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗
DOE OSTI2015

A quantum relaxation-time approximation for finite fermion systems

We propose a relaxation time approximation for the description of the dynamics of strongly excited fermion systems. Our approach is based on time-dependent density functional theory at the level of the local density approximation. This mean-field picture is augmented by collisional correlations handled in relaxation time approximation which is inspired from the corresponding semi-classical picture. The method involves the estimate of microscopic relaxation rates/times which is presently taken from the well established semi-classical experience. The relaxation time approximation implies evaluation of the instantaneous equilibrium state towards which the dynamical state is progressively driven at the pace of the microscopic relaxation time. As test case, we consider Na clusters of various sizes excited either by a swift ion projectile or by a short and intense laser pulse, driven in various dynamical regimes ranging from linear to strongly non-linear reactions. We observe a strong effect of dissipation on sensitive observables such as net ionization and angular distributions of emitted electrons. The effect is especially large for moderate excitations where typical relaxation/dissipation time scales efficiently compete with ionization for dissipating the available excitation energy. Technical details on the actual procedure to implement a working recipe of such a quantum relaxation approximation are given in appendices for completeness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

42 ENGINEERING↗
DOE OSTI2017

Laboratory simulations of the mixed solvent extraction recovery of dominate polymers in electronic waste

Highlights: • Mixed plastics in e-waste are separated with a solvent extraction method. • Recovery procedure obtained an acceptable recovery rate with high purity. • The recovered plastics are greatly energy-saving compared with virgin polymers. • Cost-saving can be potentially achieved for specific composition of plastics. - Abstract: The recovery of four dominant plastics from electronic waste (e-waste) using mixed solvent extraction was studied. The target plastics included polycarbonate (PC), polystyrene (PS), acrylonitrile butadiene styrene (ABS), and styrene acrylonitrile (SAN). The extraction procedure for multi-polymers at room temperature yielded PC, PS, ABS, and SAN in acceptable recovery rates (64%, 86%, 127%, and 143%, respectively, where recovery rate is defined as the mass ratio of the recovered plastic to the added standard polymer). Fourier transform infrared spectroscopy (FTIR) was used to verify the recovered plastics’ purity using a similarity analysis. The similarities ranged from 0.98 to 0.99. Another similar process, which was denoted as an alternative method for plastic recovery, was examined as well. Nonetheless, the FTIR results showed degradation may occur over time. Additionally, the recovery cost estimation model of our method was established. The recovery cost estimation indicated that a certain range of proportion of plastics in e-waste, especially with a higher proportion of PC and PS, can achieve a lower cost than virgin polymer product. It also reduced 99.6%, 30.7% and 75.8% of energy consumptions and CO{sub 2} emissions during the recovery of PC, PS and ABS, and reduced the amount of plastic waste disposal via landfill or incineration and associated environmental impacts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) es- timates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

58 GEOSCIENCES↗
DOE OSTI2021

Multi-Area Model-Free State Estimation via Distributed Tensor Decomposition

This paper proposes a model-free method for distribution system state estimation based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A third-order tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. To demonstrate the efficacy of the developed algorithm, numerical simulations are carried out using an IEEE test system.

alternating direction method of multipliers↗
DOE OSTI2017

A Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments

In this study, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of the mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity. Throughout the analysis, nominal values for uncertainty inputs were taken from a well-characterized engine test facility. However, the analysis did not take into account the calibration practice of experiments run in that facility and the resulting uncertainty values are therefore not indicative of the expected accuracy of those experimental results. A future study will employ the methodology developed here to explore the effects of different calibration methods on the different uncertainty values in order to evaluate best practices for accurate engine measurements.

02 PETROLEUM↗
DOE OSTI2021

Transforming a Simple Structure Model to Represent a Complex Dynamic System with Unknown Boundary Restraints

Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. In this study, the researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.

42 ENGINEERING↗
DOE OSTI2015

Estimation of the potential efficiency of a multijunction solar cell at a limit balance of photogenerated currents

A method is proposed for estimating the potential efficiency which can be achieved in an initially unbalanced multijunction solar cell by the mutual convergence of photogenerated currents: to extract this current from a relatively narrow band-gap cell and to add it to a relatively wide-gap cell. It is already known that the properties facilitating relative convergence are inherent to such objects as bound excitons, quantum dots, donor-acceptor pairs, and others located in relatively wide-gap cells. In fact, the proposed method is reduced to the problem of obtaining such a required light current-voltage (I–V) characteristic which corresponds to the equality of all photogenerated short-circuit currents. Two methods for obtaining the required light I–V characteristic are used. The first one is selection of the spectral composition of the radiation incident on the multijunction solar cell from an illuminator. The second method is a double shift of the dark I–V characteristic: a current shift J{sub g} (common set photogenerated current) and a voltage shift (−J{sub g}R{sub s}), where R{sub s} is the series resistance. For the light and dark I–V characteristics, a general analytical expression is derived, which considers the effect of so-called luminescence coupling in multijunction solar cells. The experimental I–V characteristics are compared with the calculated ones for a three-junction InGaP/GaAs/Ge solar cell with R{sub s} = 0.019 Ω cm{sup 2} and a maximum factual efficiency of 36.9%. Its maximum potential efficiency is estimated as 41.2%.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Search for top squarks in final states with many light-flavor jets and 0, 1, or 2 charged leptons in proton-proton collisions at $\sqrt{s}=13$ TeV

Several new physics models including versions of supersymmetry (SUSY) characterized by R-parity violation (RPV) or with additional hidden sectors predict the production of events with top quarks, low missing transverse momentum, and many additional quarks or gluons. The results of a search for top squarks decaying to two top quarks and six additional light-flavor quarks or gluons are reported. The search employs a novel machine learning method for background estimation from control samples in data using decorrelated discriminators. The search is performed using events with 0, 1, or 2 electrons or muons in conjunction with at least six jets. No requirement is placed on the magnitude of the missing transverse momentum. The result is based on a sample of proton-proton collisions at $\sqrt{s}=13$ TeV corresponding to 138 fb −1 of integrated luminosity collected with the CMS detector at the LHC in 2016–2018. With no statistically significant excess of events observed beyond the expected contributions from the standard model, the data are used to determine upper limits on the top squark pair production cross section in the frameworks of RPV and stealth SUSY. Models with top squark masses less than 700 (930) GeV are excluded at 95% confidence level for RPV (stealth) SUSY scenarios.

Hadron-Hadron Scattering↗
DOE OSTI2018

Xylem embolism refilling and resilience against drought-induced mortality in woody plants: processes and trade-offs

Understanding which species are able to recover from drought, under what conditions, and the mechanistic processes involved, will facilitate predictions of plant mortality in response to global change. In response to drought, some species die because of embolism-induced hydraulic failure, whilst others are able to avoid mortality and recover, following rehydration. Several tree species have evolved strategies to avoid embolism, whereas others tolerate high embolism rates but can recover their hydraulic functioning upon drought relief. Here, we focus on structures and processes that might allow some plants to recover from drought stress via embolism reversal. We provide insights into how embolism repair may have evolved, anatomical and physiological features that facilitate this process, and describe possible trade-offs and related costs. Recent controversies on methods used for estimating embolism formation/repair are also discussed, providing some methodological suggestions. Although controversial, embolism repair processes are apparently based on the activity of phloem and ray/axial parenchyma. The mechanism is energetically demanding, and the costs to plants include metabolism and transport of soluble sugars, water and inorganic ions. We propose that embolism repair should be considered as a possible component of a ‘hydraulic efficiency-safety’ spectrum. We also advance a framework for vegetation models, describing how vulnerability curves may change in hydrodynamic model formulations for plants that recover from embolism.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Meta-analysis of 18F-FDG PET/CT in the diagnosis of infective endocarditis

Background: The current diagnosis of infective endocarditis (IE) is based on the modified Duke criteria, which has approximately 80% sensitivity for the diagnosis of native valve endocarditis (NVE), with lower sensitivity for the diagnosis of prosthetic valve endocarditis (PVE) and culture-negative endocarditis. There is preliminary evidence that {sup 18}F-FDG PET/CT is an adjunctive diagnostic test with high accuracy reported in small studies to date. We therefore performed a meta-analysis of studies evaluating the use of PET/CT in the diagnosis of IE to establish a more precise estimate of accuracy. Methods: PubMed, Embase, Cochrane library, CINAHL, Web of Knowledge, and www.clinicaltrials.gov were searched from January 1990 to April 2017 for studies evaluating the accuracy of PET/CT for the evaluation of possible IE. Results: We identified 13 studies involving 537 patients that were included in the meta-analysis. The pooled sensitivity of PET/CT for diagnosis of IE was 76.8% (95% CI 71.8–81.4%; Q = 39.9, P < 0.01; I{sup 2} = 69.9%) and the pooled specificity was 77.9% (95% CI 71.9–83.2%; Q = 44.42, P < 0.01; I{sup 2} = 73.0%). Diagnostic accuracy was improved for PVE with sensitivity of 80.5% (95% CI 74.1–86.0%; Q = 25.5, P < 0.01; I{sup 2} = 72.5%) and specificity of 73.1% (95% CI 63.8–81.2%; Q = 32.1, P < 0.01; I{sup 2} = 78.2%). Additional extracardiac foci of infection were found on 17% of patients on whole body PET/CT. Conclusion: PET/CT is a useful adjunctive diagnostic tool in the evaluation of diagnostically challenging cases of IE, particularly in prosthetic valve endocarditis. It also has the potential to detect clinically relevant extracardiac foci of infection, malignancy, and other sources of inflammation leading to more appropriate treatment regimens and surgical intervention.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Mesostructure and porosity effects on the thermal conductivity of additively manufactured interpenetrating phase composites

In this paper, we have investigated the relationship between structure and thermal conductivity in additively manufactured interpenetrating A356/316L composites. We used X-ray microcomputed tomography to characterize the pore structure in as-fabricated composites, finding microporosity in both constituents as well as a 50 μm thick layer of interfacial porosity separating the constituents. We measured the thermal conductivity of a 43 vol% 316L composite to be 53 Wm -1 K -1 , which is significantly less than that predicted by a simple rule-of-mixtures approximation, presumably because of the residual porosity. Motivated by these experimental results we used periodic homogenization theory to determine the combined effects of porosity and unit cell structure on the effective thermal conductivity. This analysis showed that in fully dense composites, the topology of the constituents has a weak effect on the thermal conductivity, whereas in composites with interfacial porosity, the size and structure of the unit cell strongly influence the thermal conductivity. Finally, we also found that an approximation formula of the strong contrast expansion method gives excellent estimates of the effective thermal conductivity of these composites, providing a powerful tool for designing functionally graded composites and for identifying mesostructures with optimal thermal conductivity values.

36 MATERIALS SCIENCE↗