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DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

NdPO 4 solubility and aqueous Neodymium speciation in supercritical fluids: An experimental study at 500–700 °C and 1.7 kbar

A key aspect in the formation of rare earth elements (REE) deposits is the role of REE transport as aqueous REE complexes in supercritical hydrothermal solutions, where the nature of the aqueous complex is controlled by solution composition, temperature and pressure. Despite chloride being considered as one of the most abundant transporting ligands in magmatic-hydrothermal fluids, experimental investigations on the stability of aqueous REE chloride complexes are scarce above 300 °C. In this study, synthetic NdPO 4 crystals were reacted with non-saline and saline (0, 0.05 and 0.5 mNaCl), acidic (0.01 mHCl) aqueous solutions in a series of solubility experiments conducted at 500–700 °C and 1.7 kbar, where the solubilities were determined using a stable Nd isotope ( 145 Nd isotope spike) dilution technique. NdPO 4 solubility ranges between 28 ppm and 10,858 ppm, where solubility increases with both temperature and salinity. At 500 °C, log mNdPO 4 increases from –3.93 to –1.60 and there is a strong correlation between NdPO 4 solubility and NaCl concentrations (slope of 1.2 ± 0.3), indicating stabilization of the Nd chloride aqueous complexes with a stoichiometry corresponding to NdCl 2+ . At 600 °C, this correlation is weaker (slope of 0.4, log mNdPO 4 increases from –2.63 to –1.88) indicating the stabilization of both Nd chloride and hydroxyl species controlling solubility. At 700 °C, NdPO 4 solubility is largely independent of NaCl concentration indicating that solubility is controlled by Nd hydroxyl complexes, where stoichiometry suggests the neutral Nd(OH) 3 0 species is dominant. The solubility product (Ksp) of NdPO4 is derived from experimental data with the relation: log K sp = -41.81 – 0.057T – 20987/T, with T temperature in Kelvin. Comparison of the measured Nd phosphate solubility to thermodynamic predictions using the available Helgeson-Kirkham-Flowers equation of state parameters for aqueous Nd complexes indicate that predictions are up to three orders of magnitude lower compared to experimental observations. This discrepancy is most pronounced in saline solutions, suggesting that thermodynamic properties of the REE chloride species in supercritical fluids require revision. Numerical simulations of fluid-rock interaction between acidic, saline fluids and a Strange Lake felsic mineral assemblage demonstrates that NdPO 4 solubility predictions from models are four to six orders of magnitude lower than those calculated based on empirical fits from experiments, which suggests that acidic, saline fluids may play an important role in mobilizing large amounts of light REE from 450 to 700 °C.

58 GEOSCIENCES↗

DOE OSTI2017

The order-disorder transition in Cu{sub 2}ZnSnS{sub 4}: A theoretical and experimental study

In this work the Cu/Zn order-disorder transition in Cu{sub 2}ZnSnS{sub 4} kesterites on Wyckoff positions 2c and 2d was investigated by a structural and electronic analysis in theory and experiment. For experimental investigations stoichiometric samples with different Cu/Zn order, annealed in the temperature range of 473–623 K and afterwards quenched, were used. The optical gaps were determined using the Derivation of Absorption Spectrum Fitting (DASF) method. Furthermore, the order-disorder transition was examined by DFT calculations for a closer analysis of the origins of the reduced band gap, showing a good agreement with experimental data with respect to structural and electronic properties. Our studies show a slight increase of lattice parameter c in the kesterite lattice with increasing disorder. Additionally, a reduced band gap was observed with increasing disorder, which is an effect of newly occurring binding motifs in the disordered kesterite structure. - Highlights: • Experimental and theoretical investigation on the order-disorder transition in kesterites. • Slight enlargements of lattice constants due to disorder in experiment and theory. • Strong band gap fluctuations with decreasing order. • Electronic structure deviations due to changing binding motifs. • Disorder as possible main source of low open-circuit voltages.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Experimental validation of a high fidelity Monte Carlo neutron transport model of the MIT graphite exponential pile

High-fidelity modeling and simulation were performed for the MIT graphite exponential pile (MGEP) using Monte Carlo neutron transport codes OpenMC and MCNP, and the results were validated by experimental data. The MGEP is being used as the test bed for the design of an autonomous control system for the pile's neutron flux distribution. The main contribution of this work is to generate the training data sets of neutron flux distributions with different locations of control rods that perturb the neutron flux profiles. First, code -to-code cross verification between OpenMC and MCNP was performed to ensure consistency of the numerical modeling within statistical uncertainties. To validate the accuracy of this high-fidelity model, a series of neutron flux measurements were conducted using a Helium-3 (He-3) neutron detector on a mobile platform that is placed inside the pile. Second, the neutron flux profiles were measured in four vertical layers of interest, and compared to the corresponding simulation results. The comparison results shows that the root mean square error is less than 2.5% in the two upper layers, and less than 4.5% in all four measured layers. Here the results validated the accuracy of the modeling and simulation. Finally, the relative change of the neutron flux profiles from moving control rods was analyzed, which identified the layer that has the best sensitivity regarding the control rods movements. Thus, this work identified and provided training data sets of both simulated and experimental neutron flux profiles in the most sensitive layer, paving the path forward to the real-time experimental demonstration of the autonomous control system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

An open experimental database for exploring inorganic materials

The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Note: On the generation of sub-300 keV flash-X-rays using rod-pinch diode: An experimental investigation

Generation of flash X-rays (FXRs) at less than 500 keV is described with emphasis on experimental investigation. The pulser is a Tesla transformer-Water transmission line based pulsed power generator operating in double resonance mode to power a rod-pinch diode. The configuration of aspect ratio reported here falls much below the normally reported ratios for the rod-pinch diode operation. Experimental investigation at such low pulsed voltage has revealed “flowering” of the anode tip and “pitting” of the perspex window. A possible explanation in terms of Lorentz body force is discussed rather than the pinch mechanism generally suggested in literature. The experimental investigation for the FXR generation is corroborated by measuring the radiation dose using CaSO{sub 4} (Dy) thermo luminescent dosimeters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Experimental study and numerical modelling of a 3 × 100G DP-QPSK superchannel

Crosstalk effects are studied experimentally in a DWDM line with 100G DP-QPSK channels at a frequency convergence of the channels (three channels with a frequency separation of 50, 37.5 and 33 GHz) in the C-band. Spans of 100 km in length are used, with the number of spans varying from one to six. The required optical signal-to-noise ratio (OSNR{sub R}) is determined experimentally for a different number of spans, interchannel intervals and input power. We propose a theoretical model based on the numerical modelling that describes the influence of adjacent channels at a strong convergence. The model is calibrated using the obtained experimental data. Based on the proposed model, we calculate the maximal value of the spectral efficiency in systems with a dense arrangement of 100G DP-QPSK channels (∼3.2 bit s{sup −1} Hz{sup −1}). (fibre-optic communication)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Bayesian experimental design and parameter estimation for ultrafast spin dynamics

Abstract Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently uncover key quantum spin fluctuation parameters from x-ray photon fluctuation spectroscopy (XPFS) data. Our method is compatible with existing theoretical simulation pipelines and can also be used in combination with fast machine learning surrogate models in the event that real-time simulations are unfeasible. Our numerical benchmarks demonstrate the superior performance in predicting model parameters and in delivering more informative measurements within limited experimental time. Our method can be adapted to many different types of experiments beyond XPFS and spin fluctuation studies, facilitating more efficient data collection and accelerating scientific discoveries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0–1 M) and nitric acid (0.1–10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2016

Experimental Testing of a Floating Oscillating Surge Wave Energy Converter at Hinsdale Wave Research Laboratory

In order to generate a public data set that can be used to validate Wave Energy Converter (WEC) numerical codes, such as WEC-Sim, Sandia National Laboratories led an experimental testing campaign of a 1:33 scale Floating Oscillating Surge Wave Energy Converter (FOSWEC) in the Directional Wave Basin at Oregon State University's Hinsdale Wave Research Laboratory. Testing was performed in two phases; Phase 1 testing was completed in November - December 2015, and Phase 2 testing was completed in May - June 2016. This experimental testing report details the selection and design of a FOSWEC, experimental setup and tests, and overview of the resulting dataset from Phase 1 and Phase 2 testing.

16 TIDAL AND WAVE POWER↗

DOE OSTI2016

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesian inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

DOE Microreactor Program: Summary of Experimental Capabilities Development and Activities

This document provides a summary of the experimental capabilities and activities developed and currently performed under the U.S. Department of Energy’s (DOE’s) Microreactor Program. These capabilities, along with other laboratory capabilities, are available to support developer and other stakeholder needs. Specific experimental capabilities described in this document include the single primary heat extraction and removal emulator (SPHERE), the microreactor agile non-nuclear experimental test bed (MAGNET), microreactor applications research validation and evaluation (MARVEL), yttrium hydride fabrication, the Hypatia critical experiment series, and a brief overview of the National Criticality Experiments Research Center (NCERC), which provides the platform to conduct experiments and training with critical assemblies and fissionable material.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

SPRUCE Bog Surface Elevation Assessments with SPRUCE Elevation Transect Instrument, Marcell Experimental Forest, Minnesota, 2013-2025

This data set reports plot-specific bog surface elevation measurements collected with the SPRUCE Elevation Transect (SET) instrument at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. The SET instrument is a modified version of the Cahoon et al. (2002) surface elevation table instrument. The device is mounted to fixed stands that are anchored in deep sediments, making it capable of identifying subtle changes in bog surface elevation over time. Measurements are reported as absolute elevation in meters above mean sea level (amsl) for two locations in each of the SPRUCE experimental treatment plots and additional ambient boardwalk plots: 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 19, 20 and 21. Measurements were collected from June 2013 through September 2025 (2013-06-02 to 2025-09-08). Changes in bog surface elevation may indicate alterations in the carbon and water cycles due to changing environmental conditions. This data set contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

SPRUCE NDVI Data from Selected SPRUCE Experimental Plots, 2016-2018

This dataset consists of observations of Normalized Difference Vegetation Index (NDVI) observations from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Measurements were collected via stationary sensors positioned 1.5 meter above unobstructed areas of the ground layer. Sensors were installed above undisturbed community composition plots, that are not instrumented or destructively sampled in any way, in six of the ten chambered SPRUCE experimental study plots located in the S1-Bog. Spectral data (630 and 800 nanometers) were collected at 10-minute intervals over a field of view of ~53 cm in diameter beginning May 6, 2016. Data collection ended at a different time for each sensor as batteries discharged – ranging from January 1, 2018 to April 1, 2018. The spectral sensors were manufactured by Decagon Inc., (now METER) for observation of ecosystem canopies. Sensors were deployed at the beginning of the 2016 growing season in +0, +4.5, and +9.0 C temperature treatments, with one each in an ambient and elevated carbon dioxide (CO2) plot, for a total of six sensors. These data have been analyzed and reported on in McPartland et al (2018). This dataset contains one data file in comma-separate values (*.csv) format and PDF (*.pdf) file containing plot maps. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

SPRUCE Fungal Necromass Litter Bag Decomposition Study in SPRUCE Experimental Plots, 2016-2018

This dataset provided the results of a 2-year fungal necromass litter bag decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Necromass from four mycorrhizal fungal species that naturally occur and associate with either the ectomycorrhizal (EM) tree hosts or ericoid mycorrhizal (ERM) shrubs in the S1 Bog site and vary in melanin content (Cenococcum geophilum Fr., Suillus grisellus, Meliniomyces bicolor, and Oideodendron griseum) were incubated in situ in SPRUCE experimental treatment chambers for 3, 12, and 24 months duration beginning in June of 2016 and ending in June of 2018. Analyses of the necromass included pre-incubation chemistry and post-incubation mass loss and Fourier transform infrared (FTIR) spectroscopy of remaining fungal necromass. These data were used in analyses in Fernandez et al (2019). This dataset contains three data files in comma-separate values (*.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

SPRUCE High-Resolution Minirhizotrons in an Experimentally-Warmed Peatland Provide an Unprecedented Glimpse at Fine Roots and their Fungal Partners: Supporting Data

Images were collected using first of their kind, non-destructive, high-resolution automated minirhizotrons (RhizoSystems, LLC) to assess the response of plant fine-root and fungal mycelium dynamics to elevated temperatures after 4-6 years of whole-ecosystem warming and exposure to elevated carbon dioxide concentrations (e[CO2]) in a peat bog where the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experiment is located. We focused on two SPRUCE experimental plots: Plot 10 has elevated temperature (+9°C) and plot 19 is a control (+0°C). Both have elevated carbon dioxide (e[CO2]). Changes in root and fungal abundance with warming were estimated from a timeseries of landscape-level mosaiced images for each plot by measuring the proportional abundance of five belowground classes: fine roots of vascular plants, ectomycorrhizas, fungal hyphae, fungal rhizomorphs, and fungal sporocarps. To examine root and fungal phenology responses to warming, the length per individual root or fungal structure (except fungal hyphae that did not grow linearly but rather increased in areal coverage) were measured per image area of a set of timeseries patch-level mosaiced images for each plot. The experimental work was conducted in a Picea mariana [black spruce] – Sphagnum spp. bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). This ecosystem, which is located at the southern margin of the boreal forest, is considered especially vulnerable to climate change and anticipated to be near its tipping point. These data were used in analyses published in Defrenne et al (2021). This dataset contains 5 data files in comma-separate values (*.csv) format and a compressed folder (*.zip) containing 383 JPEG (*.jpg) images. Data files contain landscape-level assessment of belowground class abundance, patch-level growth phenology, and environmental variables (originally published in Hanson et al., 2016 and Hanson et al 2020). Images were collected with automated minirhizotrons and analyzed for phenology. Additional metadata are provided: 5 data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

SPRUCE Redox-Active Subsurface Organic Matter, Marcell Experimental Forest, Minnesota, 2023

This dataset contains measurements that report on the effects of the SPRUCE experimental treatments on redox-active organic matter (RAOM) reduction (Valenzuela and Cervantes, 2021). Measurements occurred at the SPRUCE Experiment site in the Marcell Experimental Forest in northern Minnesota, USA. This work is also a follow-up to Rush et al. (2021a) which investigated effects of temperature on RAOM reduction after two years of experimental warming (Rush et al. 2021b). This follow-up dataset addresses two main questions; (i) How does warming and elevated carbon dioxide (CO2) directly affect in situ RAOM reduction, and subsequent methane (CH4) and CO2 production, across the peat depth profile? and (ii) How has long-term warming and elevated CO2 changed the total RAOM pool, and subsequent CH4 and CO2 production, across the peat depth profile? This dataset reports electron shuttling capacity (a proxy for RAOM reduction; Keller, and Takagi, 2013) and carbon dioxide (CO2) and methane (CH4) concentrations both in one-week in situ incubations (2023-05-31 to 2023-08-01) and 42-day laboratory incubations from peat collected in 2023 (2023-05-31 to 2023-06-26). Laboratory incubations also measured acetate concentrations. The 2023 laboratory incubations were also compared with laboratory incubations conducted on peat collected in 2016 (Rush et al. 2021b). This dataset contains three data files in comma-separate (.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Experimental and Computational Sonic Boom Assessment of Boeing N+2 Low Boom Models

Near-field pressure signatures were measured and computational predictions made for several sonic boom models representing Boeing's Quiet Experimental Validation Concept (QEVC) supersonic transport, as well as three axisymmetric calibration models. Boeing developed the QEVC under a NASA Research Announcement (NRA) contract for Experimental Systems Validations for N+2 Supersonic Commercial Transport Aircraft, which was led by the NASA High Speed Project under the Fundamental Aeronautics Program. The concept was designed to address environmental and performance goals given in the NRA, specifically for low sonic boom loudness levels and high cruise efficiency, for an aircraft anticipated to enter service in the 2020 timeframe. Wind tunnel tests were conducted on the aircraft and calibration models during Phases I and II of the NRA contract from 2011 to 2013 in the NASA Ames 9- by 7-Foot and NASA Glenn 8- by 6-Foot Supersonic Wind Tunnels. Sonic boom pressure signatures were acquired primarily at Mach 1.6 and 1.8, and force and moment data were acquired from Mach 0.8 to 1.8. The sonic boom test data were obtained using a 2-in. flat-top pressure rail and a 14-in. round-top tapered "reflection factor 1" (RF1) pressure rail. Both rails capture an entire pressure signature in one data point, and successive signatures at varying positions along or above the rail were used to improve data quality through spatial averaging. The sonic boom data obtained by the rails were validated with high-fidelity numerical simulations of off-body pressures using the CFD codes USM3D, Cart3D, and OVERFLOW. The test results from the RF1 rail showed good agreement between the computational and experimental data when a variety of testing techniques including spatial averaging of a series of pressure signatures were employed, however, reflections off the 2-in. flat-top rail caused distortions in the signatures that did not agree with the CFD predictions. The 9 x 7 and 8 x 6 wind tunnels generally produced comparable data.

pressure signature↗