Abstract. Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Many applications use mobile wind measurements without knowledge of the limitations and accuracy of the mobile measurement system. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external airflow field affect measurement accuracy of vehicle-mounted anemometers. Computational fluid dynamic (CFD) simulations were generated in ANSYS Fluent to model the external flow field of a research truck under varying vehicle speed and wind yaw angle. The CFD simulations provided a quantitative description of fluid flow surrounding the vehicle and demonstrated that the change in wind speed magnitude from the inlet increased as the wind yaw angle between the inlet and the vehicle's longitudinal axis increased. The CFD results were used to develop empirical speed correction factors at specified yaw angles and to derive an aerodynamics-based correction function calibrated for wind yaw angle and anemometer placement. For comparison with CFD, we designed field tests on a square, 12.8 km route in flat, treeless terrain with stationary sonic anemometers positioned at each corner. The route was driven in replicate under varying wind conditions and vehicle speeds. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. From the field trials, we observed that vehicle-based wind speed measurements differed in average magnitude in each of the upwind, downwind, and crosswind directions. The difference from stationary anemometers increased as the yaw angle between the wind direction and the truck's longitudinal axis increased, confirming the vehicle's impact on the surrounding flow field and validating the trends in CFD. To further explore the accuracy of CFD, we applied the function derived from the simulations to the field data and again compared these with stationary measurements. From this study, we were able to make recommendations for anemometer placement, demonstrate the importance of applying aerodynamics-based correction factors to vehicle-based wind measurements, and identify ways to improve the empirical aerodynamic-based correction factors.

We would like to thank Anonymous Reviewer #2 for their time in suggesting changes that will improve our manuscript. Our responses to the review comments are provided below in blue. General Comments: The reasons for focusing on installation of an instrument atop of a pickup cap are not provided and not clear. Much of the initial discussion focuses on the work of Straka et C1 AMTD Interactive comment Printer-friendly version Discussion paper al. (1996) and others that chose to put the anemometer out front of the vehicle to avoid the vehicle's flow field. And the authors show in their results (e.g. Fig. 3) that such a location would indeed be preferable. The authors need to be much more clear about the reasons to choosing to focus only on one location on top of a cap. Problems with wind direction and speed data from a mobile instrument occur when the vehicle is experiencing acceleration (either changes in speed or direction). Data under such conditions should be removed from the analysis. However this issue is not mentioned by the authors, even though it has a significant influence on both methods and results. I can only assume the authors have left these data in, and it helps to explain some of the large scatter in Fig. 7. This issue needs to be fully addressed. The authors provide corrected wind data in Figs. 7 and 8 but readers (including myself) will want to see uncorrected data in these plots as well. This will have the side benefit of making the plots larger and more legible.

Abstract. Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Many applications use mobile wind measurements without knowledge of the limitations and accuracy of the mobile measurement system. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external air flow field affect measurement accuracy of vehicle-mounted anemometers. Computational Fluid Dynamic (CFD) simulations were generated in Ansys FLUENT to model the external flow field of a research truck under varying vehicle speed and wind yaw angle. The CFD simulations provided a quantitative description of fluid flow surrounding the vehicle, and demonstrated that the change in windspeed magnitude from the inlet increased as the wind yaw angle between the inlet and the vehicle's longitudinal axis increased. The CFD results were used to develop empirical speed correction factors at specified yaw angles, and to derive an aerodynamics-based correction function calibrated for wind yaw angle and anemometer placement. For comparison with CFD, we designed field tests on a square, 12.8 km route in flat, treeless terrain with stationary sonic anemometers positioned at each corner. The route was driven in replicate under varying wind conditions and vehicle speeds. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. From the field trials, we observed that vehicle-based windspeed measurements differed in average magnitude in each of the upwind, downwind, and crosswind directions. The difference from stationary anemometers increased as the yaw angle between the wind direction and the truck's longitudinal axis increased, confirming the vehicle's impact on the surrounding flow field and validating the trends in CFD. To further explore the accuracy of CFD, we applied the function derived from the simulations to the field data, and again compared these with stationary measurements. From this study, we were able to make recommendations for anemometer placement, demonstrate the importance of applying aerodynamics-based correction factors to vehicle-based wind measurements, and identify ways to improve the empirical aerodynamic-based correction factors.

Until recently, criticality safety assessment codes had a minimum temperature at which calculations can be performed. Where criticality assessment has been required for lower temperatures, indirect methods, including reasoned argument or extrapolation, have been required to assess reactivity changes associated with these temperatures. The ANSWERS Software Service MONK® version 10B Monte Carlo criticality code, is capable of performing criticality calculations at any temperature, within the temperature limits of the underlying nuclear data in the BINGO continuous energy library. The temperature range of the nuclear data has been extended below the traditional lower limit of 293.6 K to 193 K in a prototype BINGO library, primarily based on JEFF-3.1.2 data. The temperature range of the thermal bound scattering data of the key moderator materials was extended by reprocessing the NJOY LEAPR inputs used to produce bound data for JEFF-3.1.2 and ENDF/B-VIII.0. To give confidence in the low temperature nuclear data, a series of MONK and MCBEND calculations have been performed and results compared against external data sources. MCBEND is a Monte Carlo code for shielding and dosimetry and shares commonalities to its sister code MONK including the BINGO nuclear data library. Good agreement has been achieved between calculated and experimental cross sections for ice, k-effective results for low temperature criticality benchmarks and calculated and experimentally determined eigenvalues for thermal neutron diffusion in ice. To quantify the differences between ice and water bound scattering data a number of MONK criticality calculations were performed for nuclear fuel transport flask configurations. The results obtained demonstrate good agreement with extrapolation methods. There is a discernible difference in the use of ice and water data.

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