Publications

Barter, Garrett B.; Reichmuth, David R.; Westbrook, Jessica W.; Malczynski, Leonard A.; West, Todd H.; Guzman, Katherine D.; Edwards, Donna M.

Abstract not provided.

Drennen, Thomas E.; Reichmuth, David R.; West, Todd H.

Abstract not provided.

Barter, Garrett B.; Edwards, Donna M.; Hines, Valerie A.; Reichmuth, David R.; Westbrook, Jessica W.; Malczynski, Leonard A.; Yoshimura, Ann S.; Peterson, Meghan P.; West, Todd H.; Manley, Dawn K.; Guzman, Katherine D.

This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.

Sa, Timothy J.; Reichmuth, David R.

Abstract not provided.

Proposed for publication in International Journal of Hydrogen Energy.

Reichmuth, David R.; Manley, Dawn K.

Abstract not provided.

Barter, Garrett B.; Malczynski, Leonard A.; Manley, Dawn K.; Reichmuth, David R.; West, Todd H.; Westbrook, Jessica W.

Abstract not provided.

Energy Policy

Barter, Garrett B.; Reichmuth, David R.; Westbrook, Jessica W.; Malczynski, Leonard A.; West, Todd H.; Manley, Dawn K.; Guzman, Katherine D.; Edwards, Donna M.

Abstract not provided.

Barter, Garrett B.; Malczynski, Leonard A.; Reichmuth, David R.; West, Todd H.; Westbrook, Jessica W.; Manley, Dawn K.

Abstract not provided.

Reichmuth, David R.; Drennen, Thomas E.; Keller, Jay O.

Abstract not provided.

Sa, Timothy J.; Reichmuth, David R.; Torres, Juan J.

Abstract not provided.

Reichmuth, David R.; Keller, Jay O.; Lutz, Andrew E.

Abstract not provided.

Lutz, Andrew E.; Sa, Timothy J.; Reichmuth, David R.; Colella, Whitney C.

Abstract not provided.

Reichmuth, David R.; Kozina, Carol L.; Sale, Kenneth L.

Abstract not provided.

Reichmuth, David R.; Kozina, Carol L.; Sale, Kenneth L.

Abstract not provided.

Reichmuth, David R.; Singh, Anup K.; Einfeld, Wayne E.

Abstract not provided.

Sapra, Rajat S.; Reichmuth, David R.; Sale, Kenneth L.; Kozina, Carol L.

Abstract not provided.

Reichmuth, David R.; Chirica, Gabriela C.; Kirby, Brian K.

Hyphendated LC-MS techniques are quickly becoming the standard tool for protemic analyses. For large homogeneous samples, bulk processing methods and capillary injection and separation techniques are suitable. However, for analysis of small or heterogeneous samples, techniques that can manipulate picoliter samples without dilution are required or samples will be lost or corrupted; further, static nanospray-type flowrates are required to maximize SNR. Microchip-level integration of sample injection with separation and mass spectrometry allow small-volume analytes to be processed on chip and immediately injected without dilution for analysis. An on-chip HPLC was fabricated using in situ polymerization of both fixed and mobile polymer monoliths. Integration of the chip with a nanospray MS emitter enables identification of peptides by the use of tandem MS. The chip is capable of analyzing of very small sample volumes (< 200 pl) in short times (< 3 min).