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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TH-E-17A-10: Markerless Lung Tumor Tracking Based On Beams Eye View EPID Images

Purpose: Dynamic tumor tracking or motion compensation techniques have proposed to modify beam delivery following lung tumor motion on the flight. Conventional treatment plan QA could be performed in advance since every delivery may be different. Markerless lung tumor tracking using beams eye view EPID images provides a best treatment evaluation mechanism. The purpose of this study is to improve the accuracy of the online markerless lung tumor motion tracking method. Methods: The lung tumor could be located on every frame of MV images during radiation therapy treatment by comparing with corresponding digitally reconstructed radiograph (DRR). A kV-MV CT corresponding curve is applied on planning kV CT to generate MV CT images for patients in order to enhance the similarity between DRRs and MV treatment images. This kV-MV CT corresponding curve was obtained by scanning a same CT electron density phantom by a kV CT scanner and MV scanner (Tomotherapy) or MV CBCT. Two sets of MV DRRs were then generated for tumor and anatomy without tumor as the references to tracking the tumor on beams eye view EPID images. Results: Phantom studies were performed on a Varian TrueBeam linac. MV treatment images were acquired continuously during each treatment beam delivery at 12 gantry angles by iTools. Markerless tumor tracking was applied with DRRs generated from simulated MVCT. Tumors were tracked on every frame of images and compared with expected positions based on programed phantom motion. It was found that the average tracking error were 2.3 mm. Conclusion: This algorithm is capable of detecting lung tumors at complicated environment without implanting markers. It should be noted that the CT data has a slice thickness of 3 mm. This shows the statistical accuracy is better than the spatial accuracy. This project has been supported by a Varian Research Grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Chandra Detection of Intracluster X-Ray sources in Virgo

We present a survey of X-ray point sources in the nearest and dynamically young galaxy cluster, Virgo, using archival Chandra observations that sample the vicinity of 80 early-type member galaxies. The X-ray source populations at the outskirts of these galaxies are of particular interest. We detect a total of 1046 point sources (excluding galactic nuclei) out to a projected galactocentric radius of ∼40 kpc and down to a limiting 0.5–8 keV luminosity of ∼2×10{sup 38} erg s{sup −1}. Based on the cumulative spatial and flux distributions of these sources, we statistically identify ∼120 excess sources that are not associated with the main stellar content of the individual galaxies, nor with the cosmic X-ray background. This excess is significant at a 3.5σ level, when Poisson error and cosmic variance are taken into account. On the other hand, no significant excess sources are found at the outskirts of a control sample of field galaxies, suggesting that at least some fraction of the excess sources around the Virgo galaxies are truly intracluster X-ray sources. Assisted with ground-based and HST optical imaging of Virgo, we discuss the origins of these intracluster X-ray sources, in terms of supernova-kicked low-mass X-ray binaries (LMXBs), globular clusters, LMXBs associated with the diffuse intracluster light, stripped nucleated dwarf galaxies and free-floating massive black holes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

Reducing variance in batch partitioning measurements

The partitioning experiment is commonly performed with little or no attention to reducing measurement variance. Batch test procedures such as those used to measure K{sub d} values (e.g., ASTM D 4646 and EPA402 -R-99-004A) do not explain how to evaluate measurement uncertainty nor how to minimize measurement variance. In fact, ASTM D 4646 prescribes a sorbent:water ratio that prevents variance minimization. Consequently, the variance of a set of partitioning measurements can be extreme and even absurd. Such data sets, which are commonplace, hamper probabilistic modeling efforts. An error-savvy design requires adjustment of the solution:sorbent ratio so that approximately half of the sorbate partitions to the sorbent. Results of Monte Carlo simulations indicate that this simple step can markedly improve the precision and statistical characterization of partitioning uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Determination of the critical manifold tangent space and curvature with Monte Carlo renormalization group

We show that the critical manifold of a statistical mechanical system in the vicinity of a critical point is locally accessible through correlation functions at that point. A practical numerical method is presented to determine the tangent space and the curvature to the critical manifold with variational Monte Carlo renormalization group. Because of the use of a variational bias potential of the coarse-grained variables, critical slowing down is greatly alleviated in the Monte Carlo simulation. In addition, this method is free of truncation error. Furthermore, we study the isotropic Ising model on square and cubic lattices, the anisotropic Ising model, and the tricritical Ising model on square lattices to illustrate the method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Data-driven learning of Mori–Zwanzig operators for isotropic turbulence

Developing reduced-order models for turbulent flows, which contain dynamics over a wide range of scales, is an extremely challenging problem. In statistical mechanics, the Mori–Zwanzig (MZ) formalism provides a mathematically exact procedure for constructing reduced-order representations of high-dimensional dynamical systems, where the effects due to the unresolved dynamics are captured in the memory kernel and orthogonal dynamics. Turbulence models based on MZ formalism have been scarce due to the limited knowledge of the MZ operators, which originates from the difficulty in deriving MZ kernels for complex nonlinear dynamical systems. In this work, we apply a recently developed data-driven learning algorithm, which is based on Koopman's description of dynamical systems and Mori's linear projection operator, on a set of fully resolved isotropic turbulence datasets to extract the Mori–Zwanzig operators. With data augmentation using known turbulence symmetries, the extracted Markov term, memory kernel, and orthogonal dynamics are statistically converged and the generalized fluctuation–dissipation relation can be verified. The properties of the memory kernel and orthogonal dynamics, and their dependence on the choices of observables are investigated to address the modeling assumptions that are commonly used in MZ-based models. A series of numerical experiments are then constructed using the extracted kernels to evaluate the memory effects on prediction. The results show that the prediction errors are strongly affected by the choice of observables and can be further reduced by including the past history of the observables in the memory kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2015

Statistical precision of the intensities retrieved from constrained fitting of overlapping peaks in high-resolution mass spectra

Least-squares fitting of overlapping peaks is often needed to separately quantify ions in high-resolution mass spectrometer data. A statistical simulation approach is used to assess the statistical precision of the retrieved peak intensities. The sensitivity of the fitted peak intensities to statistical noise due to ion counting is probed for synthetic data systems consisting of two overlapping ion peaks whose positions are pre-defined and fixed in the fitting procedure. The fitted intensities are sensitive to imperfections in the m/Q calibration. These propagate as a limiting precision in the fitted intensities that may greatly exceed the precision arising from counting statistics. The precision on the fitted peak intensity falls into one of three regimes. In the "counting-limited regime" (regime I), above a peak separation &chi; ~ 2 to 3 half-widths at half-maximum (HWHM), the intensity precision is similar to that due to counting error for an isolated ion. For smaller &chi; and higher ion counts (~ 1000 and higher), the intensity precision rapidly degrades as the peak separation is reduced ("calibration-limited regime", regime II). Alternatively for &chi; < 1.6 but lower ion counts (e.g. 10–100) the intensity precision is dominated by the additional ion count noise from the overlapping ion and is not affected by the imprecision in the m/Q calibration ("overlapping-limited regime", regime III). The transition between the counting and m/Q calibration-limited regimes is shown to be weakly dependent on resolving power and data spacing and can thus be approximated by a simple parameterisation based only on peak intensity ratios and separation. A simple equation can be used to find potentially problematic ion pairs when evaluating results from fitted spectra containing many ions. Longer integration times can improve the precision in regimes I and III, but a given ion pair can only be moved out of regime II through increased spectrometer resolving power. As a result, studies presenting data obtained from least-squares fitting procedures applied to mass spectral peaks should explicitly consider these limits on statistical precision.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A controlled statistical study to assess measurement variability as a function of test object position and configuration for automated surveillance in a multicenter longitudinal COPD study (SPIROMICS)

Purpose: A test object (phantom) is an important tool to evaluate comparability and stability of CT scanners used in multicenter and longitudinal studies. However, there are many sources of error that can interfere with the test object-derived quantitative measurements. Here the authors investigated three major possible sources of operator error in the use of a test object employed to assess pulmonary density-related as well as airway-related metrics. Methods: Two kinds of experiments were carried out to assess measurement variability caused by imperfect scanning status. The first one consisted of three experiments. A COPDGene test object was scanned using a dual source multidetector computed tomographic scanner (Siemens Somatom Flash) with the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) inspiration protocol (120 kV, 110 mAs, pitch = 1, slice thickness = 0.75 mm, slice spacing = 0.5 mm) to evaluate the effects of tilt angle, water bottle offset, and air bubble size. After analysis of these results, a guideline was reached in order to achieve more reliable results for this test object. Next the authors applied the above findings to 2272 test object scans collected over 4 years as part of the SPIROMICS study. The authors compared changes of the data consistency before and after excluding the scans that failed to pass the guideline. Results: This study established the following limits for the test object: tilt index ≤0.3, water bottle offset limits of [−6.6 mm, 7.4 mm], and no air bubble within the water bottle, where tilt index is a measure incorporating two tilt angles around x- and y-axis. With 95% confidence, the density measurement variation for all five interested materials in the test object (acrylic, water, lung, inside air, and outside air) resulting from all three error sources can be limited to ±0.9 HU (summed in quadrature), when all the requirements are satisfied. The authors applied these criteria to 2272 SPIROMICS scans and demonstrated a significant reduction in measurement variation associated with the test object. Conclusions: Three operator errors were identified which significantly affected the usability of the acquired scan images of the test object used for monitoring scanner stability in a multicenter study. The authors’ results demonstrated that at the time of test object scan receipt at a radiology core laboratory, quality control procedures should include an assessment of tilt index, water bottle offset, and air bubble size within the water bottle. Application of this methodology to 2272 SPIROMICS scans indicated that their findings were not limited to the scanner make and model used for the initial test but was generalizable to both Siemens and GE scanners which comprise the scanner types used within the SPIROMICS study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

An exploration of heterogeneity in supernova type Ia samples

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that the Panstarrs dataset is the most stable of the three to changes in the data, although it does not, at the moment, go to high enough redshifts to tightly constrain the equation of state of dark energy, w . The Union2.1, drawn from several different sources, appears to be somewhat susceptible to changes within the dataset. The JLA reconstructs well for a smaller number of cosmological parameters. At higher degrees of freedom, the dependence of its errors on redshift can lead to varying results between subsets. Panstarrs is inconsistent with the other two datasets at about 2σ confidence level, and JLA and Union2.1 are about 1σ away from each other. For the Ω{sub 0} {sub m} − w cosmological reconstruction, with no additional data, the 1σ range of values in w for selected subsets of each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs. The range in Ω{sub 0} {sub m} for the same subsets remains approximately similar for all three datasets. We find that although there are differences in the fitting and correction techniques used in the different samples, the most important criterion is the selection of the SNe, a slightly different SNe selection can lead to noticeably different results both in the purely statistical analysis and in cosmological reconstruction. We note that a single, high quality low redshift sample could help decrease the uncertainties in the result. We also note that lack of homogeneity in the magnitude errors may bias the results and should either be modeled, or its effect neutralized by using other, complementary datasets. A supernova sample with high quality data at both high and low redshifts, constructed from a few surveys to avoid heterogeneity in the sample, and with homogeneous errors, would result in a more robust cosmological reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

On the surface energy balance closure at different temporal scales

Measurements of the surface energy fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made in the Columbia River Basin (Oregon) in an area of complex terrain during a 10-month long portion of the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to study the surface energy budget (SEB) and surface fluxes over different temporal scales. This study analyzes and discusses SEB closure based on half-hourly, daily, monthly, seasonal, and sub-annual (~10-month) temporal averages. The data were collected over all four seasons for different states of the underlying ground surface (dry, wet, and frozen). Our half-hourly direct measurements of energy balance show that the sum of the turbulent sensible and latent heat fluxes systematically underestimate positive net radiation by around 20–30% during daytime and overestimate negative net radiation at night. This imbalance of the surface energy budget is comparable to other terrestrial sites. However, on average, the residual energy imbalance is significantly reduced at daily, weekly, and monthly averaging timescales, and moreover, the SEB can be closed for this site within reasonable limits on seasonal and sub-annual timescales (311-day averaging for the entire field campaign dataset). Increasing the averaging time to daily and longer time intervals substantially reduces the ground heat flux and storage terms, because energy locally entering the soil, air column, and vegetation in the morning is released in the afternoon and evening. Averaging on daily to sub-annual timescales also reduces random instrumental measurement errors and other uncertainties as well as smooths out a hysteresis effect (phase lag) in the SEB relationship between different components. This study shows that SEB closure is better for dry soils compared to wet soils and the statistical dependence of the turbulent fluxes and net radiation for freezing soil surfaces appears weak, if not non-existent, apparently due to lack of the latent heat of fusion term in the traditional SEB equation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Comparative Study of Selected Wave Function and Density Functional Methods for Noncovalent Interaction Energy Calculations Using the Extended S22 Data Set

In this paper, an extension of the S22 data set of Jurecka et al. (Jurečka, P.; Šponer, J.; Cerný, J.; Hobza, P. Phys. Chem. Chem. Phys. 2006, 8, 1985.), the data set of benchmark CCSD(T)/CBS interaction energies of twenty-two noncovalent complexes in equilibrium geometries, is presented. The S22 data set has been extended by including the stretched (one shortened and three elongated) complex geometries of the S22 data set along the main noncovalent interaction coordinate. The goal of this work is to assess the accuracy of the popular wave function methods (MP2-, MP3- and, CCSD-based) and density functional methods (with and without empirical correction for the dispersion energy) for noncovalent complexes based on a statistical evaluation not only in equilibrium, but also in nonequilibrium geometries. The results obtained in this work provide information on whether an accurate and balanced description of the different interaction types and complex geometry distortions can be expected from the tested methods. This information has an important implication in the calculation of large molecular complexes, where the number of distant interacting molecular fragments, often in far from equilibrium geometries, increases rapidly with the system size. The best performing WFT methods were found to be the SCS-CCSD (spin-component scaled CCSD, according to Takatani, T.; Hohenstein, E. G.; Sherrill, C. D. J. Chem. Phys. 2008, 128, 124111), MP2C (dispersion-corrected MP2, according to Hesselmann, A. J. Chem. Phys. 2008, 128, 144112), and MP2.5 (scaled MP3, according to Pitonák, M.; Neogrády, P.; Cerný, J.; Grimme, S.; Hobza, P. ChemPhysChem 2009, 10, 282.). Since none of the DFT methods fulfilled the required statistical criteria proposed in this work, they cannot be generally recommended for large-scale calculations. The DFT methods still have the potential to deliver accurate results for large molecules, but most likely on the basis of an error cancellation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

2D-FFTLog: efficient computation of real-space covariance matrices for galaxy clustering and weak lensing

ABSTRACT Accurate covariance matrices for two-point functions are critical for inferring cosmological parameters in likelihood analyses of large-scale structure surveys. Among various approaches to obtaining the covariance, analytic computation is much faster and less noisy than estimation from data or simulations. However, the transform of covariances from Fourier space to real space involves integrals with two Bessel integrals, which are numerically slow and easily affected by numerical uncertainties. Inaccurate covariances may lead to significant errors in the inference of the cosmological parameters. In this paper, we introduce a 2D-FFTLog algorithm for efficient, accurate, and numerically stable computation of non-Gaussian real-space covariances for both 3D and projected statistics. The 2D-FFTLog algorithm is easily extended to perform real-space bin-averaging. We apply the algorithm to the covariances for galaxy clustering and weak lensing for a Dark Energy Survey Year 3-like and a Rubin Observatory’s Legacy Survey of Space and Time Year 1-like survey, and demonstrate that for both surveys, our algorithm can produce numerically stable angular bin-averaged covariances with the flat sky approximation, which are sufficiently accurate for inferring cosmological parameters. The code CosmoCov for computing the real-space covariances with or without the flat-sky approximation is released along with this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Weak lensing measurement of the mass–richness relation of SDSS redMaPPer clusters

Here, we perform a measurement of the mass–richness relation of the redMaPPer galaxy cluster catalogue using weak lensing data from the Sloan Digital Sky Survey. We carefully characterized a broad range of systematic uncertainties, including shear calibration errors, photo-zz biases, dilution by member galaxies, source obscuration, magnification bias, incorrect assumptions about cluster mass profiles, cluster centering, halo triaxiality, and projection effects. We then compare measurements of the lensing signal from two independently-produced shear and photometric redshift catalogues to characterize systematic errors in the lensing signal itself. Using a sample of 5,570 clusters from 0.1 ≤ zz ≤ 0.33, the normalization of our power-law mass vs. λ relation is log 10 [M 200m /h -1 M ⊙ ] = 14.344 ± 0.021 (statistical) ±0.023 (systematic) at a richness λ = 40, a 7 per cent calibration uncertainty, with a power-law index of 1.33+0.09-0.101.33$+0.09\atop{-0.10}$ (1σ). Finally, the detailed systematics characterization in this work renders it the definitive weak lensing mass calibration for SDSS redMaPPer clusters at this time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

DESI DR2 Baryon Acoustic Oscillations from the Lyman Alpha Forest Multipoles

We present an alternative measurement of the Baryon Acoustic Oscillation (BAO) using the Legendre multipole representation of the Ly$α$ forest correlation functions from the second data release (DR2) of the Dark Energy Spectroscopic Instrument survey. Compressing the auto- and cross-correlation functions into Legendre multipoles yields a positive-definite covariance matrix without any smoothing -- unlike the baseline DR2 analysis -- thanks to a significantly reduced data vector size. We introduce the statistical corrections required to debias the finite-sample covariance matrix estimate and demonstrate that monopole and quadrupole terms for both auto- and cross-correlations can be used even when the correlation functions are distorted by continuum errors and contaminated by metals. This formalism has slightly diminished the constraining power of the BAO scale, while considerably weakening constraints on nuisance parameters. We measure the isotropic BAO scale with $0.93\%$ precision at $z_\mathrm{eff}=2.35$, the Hubble parameter $H(z_\mathrm{eff})=(239.5\pm3.4)~(147.09~\mathrm{Mpc}/r_d) ~\mathrm{km~s}^{-1}~\text{Mpc}^{-1}$, and the transverse comoving distance $D_M(z_\mathrm{eff})=(5.80 \pm 0.10)~(r_d/147.09~\mathrm{Mpc})$~Gpc for a given value of the sound horizon ($r_d$). Our BAO results are entirely consistent with the baseline DR2 analysis.

Karaçaylı, Naim Göksel [Chicago U., KICP; Ohio Sta↗
DOE OSTI2022

A Framework for Inverse Prediction Using Functional Response Data

Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

42 ENGINEERING↗
DOE OSTI2016

Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2003-2010 Passenger Cars and LTVs”

NHTSA recently completed a logistic regression analysis updating its 2003, 2010, and 2012 studies of the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT; Kahane 2010, Kahane 2012, Puckett 2016). The new study updates the 2012 analysis using FARS data from 2005 to 2011 for model year 2003 to 2010. Using the updated databases, NHTSA estimates that reducing vehicle mass by 100 pounds while holding footprint fixed would increase fatality risk per VMT by 1.49% for lighter-than-average cars and by 0.50% for heavierthan- average cars, but reduce risk by 0.10% for lighter-than-average light-duty trucks, by 0.71% for heavier-than-average light-duty trucks, and by 0.99% for CUVs/minivans. Using a jack knife method to estimate the statistical uncertainty of these point estimates, NHTSA finds that none of these estimates are statistically significant at the 95% confidence level; however, the 1.49% increase in risk associated with mass reduction in lighter-than-average cars, and the 0.71% and 0.99% decreases in risk associated with mass reduction in heavier-than-average light trucks and CUVs/minivans, are statistically significant at the 90% confidence interval. The effect of mass reduction on risk that NHTSA estimated in 2016 is more beneficial than in its 2012 study, particularly for light trucks and CUVs/minivans. The 2016 NHTSA analysis estimates that reducing vehicle footprint by one square foot while holding mass constant would increase fatality risk per VMT by 0.28% in cars, by 0.38% in light trucks, and by 1.18% in CUVs and minivans.This report replicates the 2016 NHTSA analysis, and reproduces their main results. This report uses the confidence intervals output by the logistic regression models, which are smaller than the intervals NHTSA estimated using a jack-knife technique that accounts for the sampling error in the FARS fatality and state crash data. In addition to reproducing the NHTSA results, this report also examines the NHTSA data in slightly different ways to get a deeper understanding of the relationship between vehicle weight, footprint, and safety. The results of the NHTSA baseline results, and these alternative analyses, are summarized in Table ES.1; statistically significant estimates, based on the confidence intervals output by the logistic regression models, are shown in red in the tables. We found that NHTSA’s reasonable assumption that all vehicles will have ESC installed by 2017 in its baseline regression model slightly increases the estimated increase in risk from mass reduction in cars, but substantially decreases the estimated increase in risk from footprint reduction in all three vehicle types (Alternative 1 in Table ES.1; explained in more detail in Section 2.1 of this report). This is because NHTSA projects ESC to substantially reduce the number of fatalities in rollovers and crashes with stationary objects, and mass reduction appears to reduce risk, while footprint reduction appears to increase risk, in these types of crashes, particularly in cars and CUVs/minivans. A single regression model including all crash types results in slightly different estimates of the relationship between decreasing mass and risk, as shown in Alternative 2 in Table ES.1.

33 ADVANCED PROPULSION SYSTEMS↗