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In order to evaluate side-lobe contamination from the near-nadir region for Ku-Band radars, a statistical characterization of global near-nadir backscatter is constructed.
Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s −1 ) and damaging (≥25.7 m s −1 ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.
Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.
Abstract Learning problems involve settings in which an algorithm has to make decisions based on data, and possibly side information such as expert knowledge. This study has two main goals. First, it reviews and generalizes different results on the data and model complexity of quantum learning, where the data and/or the algorithm can be quantum, focusing on information‐theoretic techniques. Second, it introduces the notion of copy complexity, which quantifies the number of copies of a quantum state required to achieve a target accuracy level. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. As a result, empirical risk minimization is generally inapplicable. The paper presents novel results on the copy complexity for both training and testing. To make the paper self‐contained and approachable by different research communities, an extensive background material is provided on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, the differences between quantum and classical learning are highlighted by addressing both supervised and unsupervised learning, and extensive pointers are provided to the literature.
To facilitate the effective solution of multidisciplinary, multiobjective complex design problems, a departure from the traditional parametric design analysis and single objective optimization approaches is necessary in the preliminary stages of design. A necessary tradeoff becomes one of efficiency vs. accuracy as approximate models are sought to allow fast analysis and effective exploration of a preliminary design space. In this paper we apply a general robust design approach for efficient and comprehensive preliminary design to a large complex system: a high speed civil transport (HSCT) aircraft. Specifically, we investigate the HSCT wing configuration design, incorporating life cycle economic uncertainties to identify economically robust solutions. The approach is built on the foundation of statistical experimentation and modeling techniques and robust design principles, and is specialized through incorporation of the compromise Decision Support Problem for multiobjective design. For large problems however, as in the HSCT example, this robust design approach developed for efficient and comprehensive design breaks down with the problem of size - combinatorial explosion in experimentation and model building with number of variables -and both efficiency and accuracy are sacrificed. Our focus in this paper is on identifying and discussing the implications and open issues associated with the problem of size for the preliminary design of large complex systems.
Herein we present graph neural networks (GNNs) as an efficient and accurate machine learning approach to predict mechanical properties of polycrystalline materials. Here, a GNN was developed based on graph representation of polycrystals incorporating only fundamental features of grains including their crystallographic orientation, size, and grain neighbor connectivity information. We tested our method on modeling stiffness and yield strength of α -Ti microstructures, varying in their crystallographic texture. We find the GNN predicts both properties with high accuracy with mean relative errors of ~1% for unseen microstructures from a given set of textures and < 2% for microstructures of unseen texture, even when presented with limited training data. This accuracy is comparable to methods that require high-resolution three-dimensional (3D) microstructure data, such as 3D convolutional neural networks (3D-CNNs) and models that depend on the computation of spatial statistics. The present results show that graph-based deep learning is a promising framework for property prediction, especially considering the high cost associated with obtaining high-resolution 3D microstructure data and the general scarcity of experimental materials datasets.
NaCl-KCl molten salt system has been proposed as a primary component of several promising heat transfer eutectics and fuel host for Generation IV molten salt reactors (MSR). In this work, several key thermodynamic parameters, including high temperature enthalpies, isobaric heat capacities (C p ) and molar enthalpies of mixing (ΔH mix ), of the NaCl -KCl system (NaCl, 75mol% NaCl – 25mol% KCl, 51mol% NaCl – 49mol% KCl, 25mol% NaCl – 75mol% KCl, and KCl) were measured by high temperature drop calorimetry (HTDC) utilizing laser sealed nickel and aluminum crucibles. Salts were checked for phase purity by X-ray diffraction (XRD), and water content by Karl Fischer coulometric titrimetry and thermogravimetric analysis (TGA). Thermo-mechanical analysis (TMA) utilizing custom-built boron nitride (BN) crucibles was employed to determine temperatures of phase transitions (solid $\rightarrow$ liquid) for the salts and plotted against the pseudobinary phase diagram for the NaCl-KCl system. Small deviations (3-10 %) were found among C p values obtained from HTDC, differential scanning calorimetry (DSC), and NIST, comparable to the average uncertainty based on two standard deviations of measured data that are ~6% for DSC and ~3% for HTDC. Additionally, the measured enthalpies and C p values of mixed NaCl-KCl salts (e.g., 51mol% NaCl – 49mol% KCl eutectic) were shown to behave as statistical mixtures of its pure endmembers, consistent with ΔH mix = ~ 0 kJ/mol at 800 °C, and a regular interaction parameter of Ω = 1.20 ± 0.76 kJ/mol. In conclusion, this study represents a step towards improved accuracy and precision in determinations of thermodynamic parameters of molten salts by using DSC, TMA, and HTDC, in completion of the Molten Salt Thermal Properties Database (MSTDB).
We report molecular assembly processes are generally driven by thermodynamic properties in solutions. Atomistic modeling can be very helpful in designing and understanding complex systems, except that bulk solvent is very inefficient to treat explicitly as discrete molecules. The SLIC/CDC multiscale model combines continuum solvent electrostatics based on the solvent layer interface condition (SLIC) with statistical thermodynamic models for hydrogen bonding and nonpolar modes: cavity formation, dispersion interactions, combinatorial mixing (CDC). The SLIC/CDC model predicts Gibbs energies of solvation for a database of 500 solutes in water with average accuracy better than 1 kcal/mol both for experimental measurements and for explicit-solvent molecular dynamics simulations. The separate SLIC/CDC energy mode values agree quantitatively with those computed from explicit-solvent molecular dynamics. The SLIC/SASA multiscale model combines the SLIC continuum electrostatic model with the solvent accessible surface area (SASA) nonpolar energy mode. The SLIC/SASA model predicts Gibbs energies of solvation with better than 1.4 kcal/mol average accuracy in aqueous systems and better than 1.6 kcal/mol average accuracy in ionic liquids. Both models predict solvation entropies, and are the first implicit-solvation models capable of predicting solvation heat capacities.
The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.
ABSTRACT Introduction The US Army Load Carriage Decision Aid (LCDA) metabolic model is used by militaries across the globe and is intended to predict physiological responses, specifically metabolic costs, in a wide range of dismounted warfighter operations. However, the LCDA has yet to be adapted for vest-borne load carriage, which is commonplace in tactical populations, and differs in energetic costs to backpacking and other forms of load carriage. Purpose The purpose of this study is to develop and validate a metabolic model term that accurately estimates the effect of weighted vest loads on standing and walking metabolic rate for military mission-planning and general applications. Methods Twenty healthy, physically active military-age adults (4 women, 16 men; age, 26 ± 8 yr old; height, 1.74 ± 0.09 m; body mass, 81 ± 16 kg) walked for 6 to 21 min with four levels of weighted vest loading (0 to 66% body mass) at up to 11 treadmill speeds (0.45 to 1.97 m·s −1 ). Using indirect calorimetry measurements, we derived a new model term for estimating metabolic rate when carrying vest-borne loads. Model estimates were evaluated internally byk-fold cross-validation and externally against 12 reference datasets (264 total participants). We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured walking metabolic rate. Estimation accuracy, precision, and level of agreement were also evaluated by the bias, standard deviation of paired differences, and concordance correlation coefficient (CCC), respectively. Results Metabolic rate estimates using the new weighted vest term were statistically equivalent (P< 0.01) to measured values in the current study (bias, −0.01 ± 0.54 W·kg −1 ; CCC, 0.973) as well as from the 12 reference datasets (bias, −0.16 ± 0.59 W·kg −1 ; CCC, 0.963). Conclusions The updated LCDA metabolic model calculates accurate predictions of metabolic rate when carrying heavy backpack and vest-borne loads. Tactical populations and recreational athletes that train with weighted vests can confidently use the simplified LCDA metabolic calculator provided as Supplemental Digital Content to estimate metabolic rates for work/rest guidance, training periodization, and nutritional interventions.
The current state-of-the-art technology for in-situ stress measurements involves an integrated approach that combines borehole breakout observations, drilling-induced tensile fractures, and hydraulic fracturing tests (i.e., “mini-fracs”). This methodology has achieved wide application in the oil and gas industry but has several limitations that often prevent successful in-situ stress measurements. One major limitation is that breakouts do not appear in all boreholes and are generally only a natural occurrence that cannot easily be controlled. Because borehole breakouts are used to directly measure the maximum horizontal in-situ stress magnitude, the absence of borehole breakouts presents a major data gap for in-situ stress measurements. In response to this data gap, a new US Department of Energy (US DOE)-sponsored thermal breakout technology that will provide a method for thermally inducing borehole breakouts and allow the consistent measurement of the maximum horizontal stress magnitude is currently in development. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which can be directly correlated to the maximum horizontal stress magnitude. The first step in this project was an analytical modeling study of the thermal breakout process. Based on the Kirsch solution (Kirsch 1898), a deterministic and statistical analysis was performed on the pertinent parameters that influence the maximum horizontal stress calculation. As a result of the analysis, the findings indicate that the thermal breakout technology is feasible and provides improved accuracy and/or an enhanced ability to measure the maximum horizontal stress. Future work as part of this US DOE-sponsored project includes additional validation through more detailed numerical modeling, laboratory testing, and field testing of the thermal breakout technology.
Two types of discrete adaptive procedures are proposed for updating statistical parameters such as the means and covariance matrices. These two types include nonweighted adaptive procedures and weighted adaptive procedures. It is believed that the use of the proposed procedures will enhance the performance accuracy of a pattern recognition system. The procedures discussed include an approach for a weighted mean vector adaptation.
The present investigation is concerned with the effects of stochastic geometry and random environmental torques on the pointing accuracy of spinning and three-axis stabilized satellites. The study of pointing accuracies requires a knowledge of the rates of error growth over and above any criteria for the asymptotic stability of the satellites. For this reason the investigation is oriented toward the determination of the statistical properties of the responses of the satellites. The geometries of the satellites are considered stochastic so as to have a phenomenological model of the motions of the flexible structural elements of the satellites. A widely used method of solving stochastic equations is the Fokker-Planck approach where the equations are assumed to define a Markoff process and the transition probability densities of the responses are computed directly as a function of time. The Fokker-Planck formulation is used to analyze the response vector of a rigid satellite.
Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.
This paper presents a statistical methodology whereby the probability limits associated with CFD grid resolution of inlet flow analysis can be determined which provide quantitative information on the distribution of that error over the specified operability range. The objectives of this investigation is to quantify the effects of both random (accuracy) and systemic (biasing) errors associated with grid resolution in the analysis of the Lockheed Martin Company (LMCO) N+2 Low Boom external compression supersonic inlet. The study covers the entire operability space as defined previously by the High Speed Civil Transport (HSCT) High Speed Research (HSR) program goals. The probability limits in terms of a 95.0% confidence interval on the analysis data were evaluated for four ARP1420 inlet metrics, namely (1) total pressure recovery (PFAIP), (2) radial hub distortion (DPH/P), (3) ) radial tip distortion (DPT/P), and (4) ) circumferential distortion (DPC/P). In general, the resulting +/-0.95 delta Y interval was unacceptably large in comparison to the stated goals of the HSCT program. Therefore, the conclusion was reached that the "standard grid" size was insufficient for this type of analysis. However, in examining the statistical data, it was determined that the CFD analysis results at the outer fringes of the operability space were the determining factor in the measure of statistical uncertainty. Adequate grids are grids that are free of biasing (systemic) errors and exhibit low random (precision) errors in comparison to their operability goals. In order to be 100% certain that the operability goals have indeed been achieved for each of the inlet metrics, the Y+/-0.95 delta Y limit must fall inside the stated operability goals. For example, if the operability goal for DPC/P circumferential distortion is ≤0.06, then the forecast Y for DPC/P plus the 95% confidence interval on DPC/P, i.e. +/-0.95 delta Y, must all be less than or equal to 0.06.
Weather and climate forecasting, using climate models, have become essential tools and life-savers in the West African region; in spite of the fact that climate models do not fully comply with attributes of forecast qualities—RASAP: reliability, association, skill, accuracy, and precision. The objective of this paper is to quantitatively evaluate, in comparison to CRU and ERA5 datasets, the RASAP compliance-level of the weather@home2 modeling system (w@h2). Findings from some statistical evaluations show that, to a moderately significant extent, w@h2 model provides useful information during the monsoon seasons; skills to capture the Little Dry Season over the Guinea zone; predictive skills for the onset season; ability to reproduce all the annual characteristics of the surface maximum air temperature over the region; as well as skill to detect heat waves that usually ravage West Africa during the boreal spring. The model displays traces of attributes that are needed for seasonal climate predictions and applications. Deficiencies in the quantitative reproducibility point to the facts that the model does provide a reliability akin to that of regional climate models. This paper further furnishes a prospective user with information on whether the model might be “useful or not” for a particular application.
Abstract. Floating offshore wind turbine (FOWT) systems are subject to complex environmental loads, with significant potential for damage in extreme storm conditions. Design simulations in these conditions are required to assess the survivability of the device with some level of confidence. Aero-hydro-servo-elastic engineering tools can be used with a reasonable balance of accuracy and computational efficiency. The models require many input parameters to describe the air and water conditions, the system properties, and the load calculations. Each of these parameters has some possible range, due to either statistical uncertainty or variations with time. Variation in the input parameters can have important effects on the uncertainty in the resulting loads, but it is not practical to perform detailed assessments of the impact of this uncertainty for every input parameter. This work demonstrates a method to identify the input parameters that have the most impact on the loads to focus further inspection. The process is done specifically for extreme storm load cases defined in the International Electrotechnical Commission design requirements for floating offshore wind turbines. The analysis was performed using the International Energy Agency Wind 15 MW offshore reference wind turbine atop the University of Maine VolturnUS-S reference platform in two US offshore wind regions, the Gulf of Maine and Humboldt Bay. It was found that the direction of incident waves and current, yaw misalignment, and the length of mooring line sections were among the primary sensitivities.
The purpose of this work is twofold. First, given the computational resources available today, it is possible to reach, using DNS, higher Reynolds numbers than in Briggs et al.. In the present study, the microscale Reynolds numbers reached in the low- and high-energy homogeneous regions are, respectively, 32 and 69. The results reported earlier can thus be complemented and their robustness in the presence of increased turbulence studied. The second aim of this work is to perform a detailed and documented LES of the shear-free mixing layer. In that respect, the creation of a DNS database at higher Reynolds number is necessary in order to make meaningful LES assessments. From the point of view of LES, the shear-free mixing-layer is interesting since it allows one to test how traditional LES models perform in the presence of an inhomogeneity without having to deal with difficult numerical issues. Indeed, as argued in Briggs et al., it is possible to use a spectral code to study the shear-free mixing layer and one can thus focus on the accuracy of the modelling while avoiding contamination of the results by commutation errors etc. This paper is organized as follows. First we detail the initialization procedure used in the simulation. Since the flow is not statistically stationary, this initialization procedure has a fairly strong influence on the evolution. Although we will focus here on the shear-free mixing layer, the method proposed in the present work can easily be used for other flows with one inhomogeneous direction. The next section of the article is devoted to the description of the DNS. All the relevant parameters are listed and comparison with the Veeravalli & Warhaft experiment is performed. The section on the LES of the shear-free mixing layer follows. A detailed comparison between the filtered DNS data and the LES predictions is presented. It is shown that simple eddy viscosity models perform very well for the present test case, most probably because the flow seems to be almost isotropic in the small-scale range that is not resolved by the LES.