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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Artificial neural network implementation of a near-ideal error prediction controller

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

Mcvey, Eugene S.↗
DOE OSTI2023

Energy Frontier Research Centers: Center for the Computational Design of Functional Layered Materials (CCDM) August 1, 2014 - July 31, 2018; Center for Complex Materials from First Principles (CCM) August 1, 2018 - July 31, 2021 (Final Report)

The mission of the DOE Energy Frontier Research Centers CCDM (2014-2018) and CCM (2018-2021) was to theoretically develop, computationally apply, and experimentally validate electronic structure methods for all materials, with a focus on the complex materials, especially layered and two-dimensional materials, strongly-correlated materials, and liquid water. This was achieved by over 200 published journal articles authored by about 17 senior investigators from physics and chemistry and from theory, computation, and experiment, plus their collaborators. In particular, the Centers confirmed the predictive power of the SCAN (strongly constrained and appropriately normed) density functional, which was constructed to satisfy 17 known exact constraints and several appropriate norms. Without being fitted to real bonded systems, and at a modest computational cost, SCAN correctly predicted covalent, ionic, metallic, hydrogen, and van der Waals bonds in many challenging materials. SCAN gave an improved description of defects in semiconductors, surface properties of metals, seven phases of ice, liquid water, liquid and supercooled silicon, subtle structural distortions in ferroelectrics, formation energies and structural predictions for solids, and critical pressures for structural phase transitions. Perhaps most remarkably, SCAN correctly described some strongly-correlated materials that were previously believed to be beyond the reach of density-functional approximations. SCAN is the only density functional that correctly predicts the band gap closing under chemical doping of the cuprate high-temperature superconducting materials. SCAN also predicts a landscape of competing stripe and magnetic phases in the cuprates. For some materials with some codes, SCAN has convergence problems that are greatly reduced by the CCM-developed r 2 SCAN, without loss of accuracy or rigor. SCAN and r 2 SCAN still make some self-interaction error, which is greatly reduced by the CCDM/ CCM-developed local orbital scaling correction (LOSC). These Centers further proved that the fundamental energy gaps of a solid from an orbital energy difference and from total energy differences are the same for a large class of generalized Kohn-Sham (GKS) functionals, including SCAN and standard hybrid functionals, and that symmetry breaking arises when a dynamic density fluctuation drops to zero frequency. The Centers identified new mechanisms for catalysis in layered materials with ions intercalated between the layers, investigated charge density waves both in model systems and in real layered materials, studied changes of band gap with the number of layers, and explored topological ultrathin films, bent nanoribbons, and defects.

08 HYDROGEN↗
DOE OSTI2020

Modal analysis of the behavior of inertial particles in turbulence subjected to Stokes drag

The clustering of small heavy inertial particles subjected to Stokes drag in turbulence, which is the concern of present study, is known to be minimal at small and large Stokes number and substantial at St = $\textit{O}$(1). This nonmonotonic trend, which has been shown computationally and experimentally, is yet to be explained analytically. In this study, we obtain an analytical expression for the Lyapunov exponents that quantitatively predicts this trend. The sum of the exponents, which is the normalized rate of change of the signed volume of a small cloud of particles, is correctly predicted to be negative and positive at small and large Stokes numbers, respectively, asymptoting to $\textit{τQ}$ as $\textit{τ}$ → 0 and $τ^{–1/2}|Q|^{1/4}$ as $\textit{τ} → ∞$, where $\textit{τ}$ is the particle relaxation time and $\textit{Q(τ)}$ is the difference between the norm of the rotation- and strain-rate tensors computed along the particle trajectory, which remains negative at all $\textit{τ}$ in turbulent flows. Additionally, the trajectory crossing is predicted only in hyperbolic flows where Q < 0 for sufficiently inertial particles with a $\textit{τ}$ that scales with $|Q|^{–1/2}$. Following the onset of crossovers, a transition from clustering to dispersion is predicted correctly. We show these behaviors are not unique to three-dimensional isotropic turbulence and can be reproduced closely by a one-dimensional mono-harmonic flow, which appears as a fundamental canonical problem in the study of particle clustering. Analysis of this one-dimensional canonical flow shows that the rate of clustering, quantified as the product of the Lyapunov exponent and particle relaxation time, is bounded by –1/2, behaving with extreme nonlinearity in the hyperbolic flows and always remaining positive in the elliptic flows. Finally, this model problem also confirms that dispersion in a hyperbolic flow will occur only if particles' trajectory cross. These findings, which stem from our analysis, are corroborated by the direct numerical simulations.

42 ENGINEERING↗
DOE OSTI2023

Large Eddy Simulation of Externally Induced Ingress about an Axial Seal by Stator Vanes

Turbine inlet temperatures in advanced gas turbines could be as high as 2000 °C. To prevent ingress of this hot gas into the wheelspace between the stator and rotor disks, whose metals can only handle temperatures up to 850 °C, rim seals and sealing flows are used. This study examines the abilities of large eddy simulation (LES) based on the WALE subgrid model and Reynolds-averaged Navier–Stokes (RANS) based on the SST model in predicting ingress in a rotor–stator configuration with vanes but no blades, a configuration with experimental data for validation. Results were obtained for an operating condition, where the ratio of the external Reynolds number to the rotational Reynolds number is 0.538. At this operating condition, both LES and RANS were found to correctly predict the coefficient of pressure, C p , located downstream of the vanes and upstream of the seal, but only LES was able to correctly predict the sealing effectiveness. This shows C p by itself is inadequate in quantifying externally induced ingress. RANS was unable to predict the sealing effectiveness because it significantly under predicted the pressure drop in the hot gas path along the axial direction, especially about the seal region. This affected the pressure difference across the seal in the radial direction, which ultimately drives ingress.

42 ENGINEERING↗
DOE OSTI2020

Modeling gas–shell mixing in ICF with separated reactants

Mixing between fuel and shell materials in ICF implosions can affect implosion dynamics and even prevent ignition. We use data from a series of separated reactant experiments on the National Ignition Facility to calibrate and test the predictive power of gas–shell mix models. Two models are used to estimate fuel–shell mix: a Reynolds-averaged turbulence model and molecular diffusion. Minor uncertainties in capsule manufacture, experimental conditions, and values for mix model parameters produce significant variation in simulation results. Using input/output pairs from 1D simulations, we train Gaussian process surrogate models to predict experimental quantities of interest. The surrogates are used to construct posteriors for mix model parameters by marginalizing over uncertainties in capsule manufacture and experimental conditions. Mix models are calibrated with a subset of experimental data (neutron yields, ion temperature, and bang time) and tested using the remaining data. In general, both the diffusion and turbulence model correctly predict experimental DT and TT neutron yields. Despite having more free parameters, the turbulence model underpredicts ion temperature at high convergence ratio. Furthermore, the simpler diffusion model correctly predicts these temperatures, suggesting nonhydrodynamic gas–shell mix. The computational model consistently overpredicts DD neutron yield, indicating possible shortcomings outside of the mix model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

High-Order Wall-Modeled Large-Eddy Simulation of High-Lift Configuration

This paper presents the assessment of several recent enhancements for a high-order wall-modeled large-eddy simulation (WMLES) approach and demonstrates order independence with a fixed data exchange location in the wall model. The two enhancements include the use of isotropic tetrahedral elements to improve accuracy and an explicit subgrid-scale model, the Vreman model, to improve accuracy and robustness. The [Formula: see text] study focused on the high-lift Common Research Model (HL-CRM) at the angle of attack of 19.57 deg, a benchmark problem from the 4th AIAA High-Lift Prediction Workshop. Solution polynomial orders of [Formula: see text], and 5 were used in the study. The study demonstrated [Formula: see text] independence in integrated forces, pitch moment, velocity profile in the wall-normal direction, and surface flow topology. It also showed that a [Formula: see text] order of at least 3 ([Formula: see text]) was needed to correctly predict the external inviscid flow and the surface flow topology. Thereafter, [Formula: see text] simulations over several other angles of attack demonstrated that the high-order WMLES approach can correctly predict the maximum lift and flow separation regions for HL-CRM with about 40 million degrees of freedom (DOF) compared to at least 250 million DOF required by second-order methods.

Engineering↗
DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗
NASA NTRS1995

Modeling the vestibulo-ocular reflex of the squirrel monkey during eccentric rotation and roll tilt

Model simulations of the squirrel monkey vestibulo-ocular reflex (VOR) are presented for two motion paradigms: constant velocity eccentric rotation and roll tilt about a naso-occipital axis. The model represents the implementation of three hypotheses: the "internal model" hypothesis, the "gravito-inertial force (GIF) resolution" hypothesis, and the "compensatory VOR" hypothesis. The internal model hypothesis is based on the idea that the nervous system knows the dynamics of the sensory systems and implements this knowledge as an internal dynamic model. The GIF resolution hypothesis is based on the idea that the nervous system knows that gravity minus linear acceleration equals GIF and implements this knowledge by resolving the otolith measurement of GIF into central estimates of gravity and linear acceleration, such that the central estimate of gravity minus the central estimate of acceleration equals the otolith measurement of GIF. The compensatory VOR hypothesis is based on the idea that the VOR compensates for the central estimates of angular velocity and linear velocity, which sum in a near-linear manner. During constant velocity eccentric rotation, the model correctly predicts that: (1) the peak horizontal response is greater while "facing-motion" than with "back-to-motion"; (2) the axis of eye rotation shifts toward alignment with GIF; and (3) a continuous vertical response, slow phase downward, exists prior to deceleration. The model also correctly predicts that a torsional response during the roll rotation is the only velocity response observed during roll rotations about a naso-occipital axis. The success of this model in predicting the observed experimental responses suggests that the model captures the essence of the complex sensory interactions engendered by eccentric rotation and roll tilt.

NASA Discipline Neuroscience↗
NASA NTRS1986

Intuitive reasoning about abstract and familiar physics problems

Previous research has demonstrated that many people have misconceptions about basic properties of motion. Two experiments examined whether people are more likely to produce dynamically correct predictions about basic motion problems involving situations with which they are familiar, and whether solving such problems enhances performance on a subsequent abstract problem. In experiment 1, college students were asked to predict the trajectories of objects exiting a curved tube. Subjects were more accurate on the familiar version of the problem, and there was no evidence of transfer to the abstract problem. In experiment 2, two familiar problems were provided in an attempt to enhance subjects' tendency to extract the general structure of the problems. Once again, they gave more correct responses to the familiar problems but failed to generalize to the abstract problem. Formal physics training was associated with correct predictions for the abstract problem but was unrelated to performance on the familiar problems.

Kaiser, Mary Kister↗
DOE OSTI2021

Three-Dimensional CFD Investigation of Pre-Spark Heat Release in a Boosted SI Engine

Low-temperature heat release (LTHR) in spark-ignited internal combustion engines is a critical step toward the occurrence of auto-ignition, which can lead to an undesirable phenomenon known as engine knock. As such, correct predictions of LTHR are of utmost importance to improve the understanding of knock and enable techniques aimed at controlling it. While LTHR is typically obscured by the deflagration following the spark ignition, extremely late ignition timings can lead to LTHR occurrence prior to the spark, i.e., pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection SI engine was numerically investigated using three-dimensional computational fluid dynamics (CFD). A hybrid approach was used, based on the G-equation model for representing the turbulent flame front and the multi-zone well-stirred reactor model for tracking the chemical reactions within the unburnt region. A recently developed best practice was also employed which keeps the well-stirred reactor model active throughout the entire simulation. This allowed for correct predictions of the previous cycle trapped residuals which have a considerable effect on the onset of LTHR. Multi-cycle simulations were conducted using Co-Optima alkylate and E30 fuels. The predicted in-cylinder pressure and heat release rate agreed well with the experimental data and served as validation for the CFD model. Following the initial validation, the dynamics of PSHR was discussed and a series of parameters of interest were assessed. First, the effect of exhaust valve temperature was investigated, qualitatively highlighting the importance of boundary conditions uncertainty. Further analyses were carried out on the effects of fuel properties, including laminar flame speed (LFS) and heat of vaporization (HOV). The results indicate that PSHR phasing is slightly advanced with lower LFS as more trapped unburnt fuel is made available for the next cycle. A similar trend was observed with lower HOV as less intense spray cooling led to higher mixture temperatures, i.e., higher mixture reactivity. Finally, a comparison of Co-Optima alkylate and E30 fuels was made using the pressure-temperature trajectory framework. It was shown that the differences between Co-Optima alkylate and E30 in PSHR tendency are correlated with both HOV and chemical effects.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1976

Basic corrections to predictions of solar cell performance required by nonlinearities

The superposition principle is used to derive the approximation that the current-voltage characteristic of an illuminated solar cell is the dark current-voltage characteristic shifted by the short-circuit photocurrent. The derivation requires the linearity of the boundary value problems that underlie the electrical characteristics. The shifting approximation is invalid if considerable photocurrent and considerable dark current both occur within the junction space-charge region; it is invalid also if sizable series resistance is present or if high-injection concentrations of holes and electrons exist within the quasi-neutral regions.

Lindholm, F. A.↗
NASA NTRS1995

Thermal emission from particulate surfaces: A comparison of scattering models with measured spectra

Emissivity spectra of particulate mineral samples are highly dependent on particle size when that size is comparable to the wavelength of light emitted (5-50 micrometers for the midinfrared). Proper geologic interpretation of data from planetary infrared spectrometers will require that these particle size effects be well understood. To address this issue, samples of quartz powders were produced with narrow, well-characterized particle size distributions. Mean particle diameters in these samples ranged from 15 to 227 micrometers. Emission spectra of these powders allow the first detailed comparison of the complex spectral variations with particle size observed in laboratory data with the predictions of radiative transfer models. Four such models are considered here. Hapke's relectance theory (converted to emissivity via Kirchoff's law) is the first model tested. Hapke's more recently published emission theory is also employed. The third model, the 'Mie/Conel' model, uses Mie single scattering with a two-stream approximation for multiple scattering. This model, like the first, is a converted reflec- tance model. Mie scattering assumes particles are both spherical and well separated, which is not true for the quartz powders, but includes diffraction effects. The fourth model uses the Mie solution for single scattering by spheres and inputs those results into the multiple scattering formalism of Hapke's emission theory. The results of the four models are considered in relation to the values of the optical constants n and k. We have grouped these as class 1 (k large), class 2 (k moderate, n is approximately 2), class 3 (k small, n is approximately 2), and class 4 (k small, n is approximately 1). In general, the Mie/Hapke hybrid model does best at predicting variations with grain size. In particular, it predicts changes of the correct pattern, although incorrect magnitude, for class 1 bands, where large increases in emissivity with decreasing grain size are observed. This model also does an excellent job on moderate (class 2) and very weak and intraband (class 3) regions, and correctly predicts the emission maximum and its invariance with grain size near the Christiansen frequency (class 4). The Mie/Hapke hybrid model also has the fewest free parameters of the four models examined, while maintaining the most physical treatment of the radiative transfer. The Mie/Conel model performs as well as the Mie/Hapke hybrid model in strong bands (class 1) but does not accurately model the behavior of moderate (class 2) and very weak (class 3) bands.

Moersch, J. E.↗
NASA NTRS2013

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel: 6-Ply In-Autoclave Facesheets - Part 2

Four honeycomb sandwich panel types, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle (HLLV), were manufactured and tested under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3- by 5-ft panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center (LaRC). Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3- by 5-ft panel. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panels composed of 6-ply, IM7/977-3 facesheets (referred to as Panels B-1 and B-2). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yield good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, several imperfection studies were performed to investigate the influence of geometric imperfections, fiber angle misalignments, and three-dimensional (3-D) effects on the compressive response of the panel.

Pineda, Evan J.↗
NASA NTRS2014

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel: Comparison of In- and Out-of-Autoclave Facesheet Configurations

Four honeycomb sandwich panels, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle, were manufactured and tested under the NASA Composites for Exploration and the NASA Constellation Ares V programs. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.0 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3 ft. by 5 ft. panels were cut from the 1/16th barrel sections and tested under compressive loading. Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3 ft. by 5 ft. panel. To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yielded good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, several imperfection studies were performed to investigate the influence of geometric imperfections, fiber angle misalignments, and three-dimensional effects on the compressive response of the panel.

epoxy matrix composites↗
NASA NTRS2019

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel Part 4: Six-ply Out-of-Autoclave Facesheets

Four honeycomb sandwich panel types, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle (HLLV), were manufactured and tested under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3 ft. by 5 ft. panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center (LaRC). Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3 ft. by 5 ft. panel. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panels composed of 6-ply, T40-800b/5320-1 facesheets (referred to as Panels D). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yield good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, three-dimensional (3D) effects on the compressive response of the panel were studied.

David E Myers↗
DOE OSTI2020

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Design, analysis, and testing of a metal matrix composite web/flange intersection

An experimental and analytical program to study the local design details of a typical T-shaped web/flange intersection made from a metal matrix composite is described. Loads creating flange bending were applied to specimens having different designs and boundary conditions. Finite element analyses were conducted on models of the test specimens to predict the structural response. The analyses correctly predict failure load, mode, and location in the fillet material in the intersection region of the web and the flange when specimen quality is good. The test program shows the importance of fabrication quality in the intersection region. The full-scale test program that led to the investigation of this local detail is also described.

Biggers, S. B.↗