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Sauer, Jeremy

Publications and source records attributed to Sauer, Jeremy.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project (FY2019)

The overall goal of the Mesoscale-to-Microscale Coupling (MMC) project is to improve coupling between mesoscale and microscale simulations via improved guidance and new strategies for setting up simulations and the development of new tools that can be used across the community. Including the mesoscale forcing is critical to modeling the full energy transfer across scales in the atmosphere. The project-specific objectives include: Apply rigorous verification and validation (V&V) techniques to the new modeling tools that are developed as part of the project to ensure the accuracy of our codes and results and develop estimates of the relative uncertainty. Improve computational performance of the coupled MMC models through the development of methods that can be used to reduce turbulence spin-up time and hence the size of computational domains. Improve representation of the surface layer in microscale models to enhance simulations of hub-height wind speed. Develop guidance for the community describing the best ways to couple mesoscale and microscale models, including specific spatial scales at which the handoff to the microscale model should occur. Prepare documentation and a suite of software tools that can be used across the community. Transition MMC research to the offshore environment.

17 WIND ENERGY↗
DOE OSTI2017

Numerical investigation of aggregated fuel spatial pattern impacts on fire behavior

Here, landscape heterogeneity shapes species distributions, interactions, and fluctuations. Historically, in dry forest ecosystems, low canopy cover and heterogeneous fuel patterns often moderated disturbances like fire. Over the last century, however, increases in canopy cover and more homogeneous patterns have contributed to altered fire regimes with higher fire severity. Fire management strategies emphasize increasing within-stand heterogeneity with aggregated fuel patterns to alter potential fire behavior. Yet, little is known about how such patterns may affect fire behavior, or how sensitive fire behavior changes from fuel patterns are to winds and canopy cover. Here, we used a physics-based fire behavior model, FIRETEC, to explore the impacts of spatially aggregated fuel patterns on the mean and variability of stand-level fire behavior, and to test sensitivity of these effects to wind and canopy cover. Qualitative and quantitative approaches suggest that spatial fuel patterns can significantly affect fire behavior. Based on our results we propose three hypotheses: (1) aggregated spatial fuel patterns primarily affect fire behavior by increasing variability; (2) this variability should increase with spatial scale of aggregation; and (3) fire behavior sensitivity to spatial pattern effects should be more pronounced under moderate wind and fuel conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

FInd Gas Source

FIGS is a neural network software that ingests real time synchronized field data on environmental flow fields and turbulence and gas concentration variations at high frequency and uses an error minimization algorithm to locate the gas source and quantify its strength. The software can be interfaced with atmospheric, oceanic and subsurface instruments in a variety of platforms stationary or mobile (e.g. cars, UAVs, submersible vehicles or boreholes) and used to find gas sources by smart use of data and phenomenology. FIGS can be trained by phenomenological model of the flow fields in the environment of interest and/or be calibrated by controlled release. After initial deployment the FIGS learning will grow with time as it accumulates data on source quantification. FIGS can be installed on any computer from small beagle-bones for field deployment/end-use to PC/MACs/main-frame for training/analysis. FIGS has been trained (using LANL's high resolution atmospheric simulations) and calibrated, tested and evaluated in the field and shown to perform well in finding and quantifying methane leaks at 10-100m scales at well pads by ingesting atmospheric measurements. The code is applicable to gas and particle source location at large scales.

Travis, Bryan↗