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The field of automotive engineering has devoted much research to reduce fuel consumption to attain sustainable energy usage. Friction reductions in powertrain components can improve engine fuel economy. Quantitative accounting of friction is complex because it is affected by many physical aspects such as oil viscosity, temperature, surface roughness and component rotation speed. The purpose of this paper is two-fold: first, to develop a useful tool for evaluating the friction in engine and accessories based on test data; second, to exercise the tool to evaluate the fuel economy gain in a drive cycle for several friction reduction technologies.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

The transportation sector in the United States is a major contributor to global energy consumption and carbon dioxide emission. To assess the future potentials of different technologies in addressing these two issues, we used a family of simulation programs to predict fuel consumption for passenger cars in 2020. The selected technology combinations that have good market potential and could be in mass production include: advanced gasoline and diesel internal combustion engine vehicles with automatically-shifting clutched transmissions, gasoline, diesel, and compressed natural gas hybrid electric vehicles with continuously variable transmissions, direct hydrogen, gasoline and methanol reformer fuel cell hybrid electric vehicles with direct ratio drive, and battery electric vehicle with direct ratio drive.

An energy-efficient lyophilization technique is being developed to recover water from highly contaminated spacecraft waste streams. In the lyophilization process, water in an aqueous waste is frozen and then sublimed, separating the waste into a dried solid material and liquid water. This technology is ideally suited to applications such as the Mars Reference Mission, where water recovery rates approaching 100% are desirable but production of CO2 is not. Candidate wastes include feces, concentrated brines from water processors, and other solid wastes that contain water. To operate in microgravity, and to minimize power consumption, thermoelectric heat pumps can be used in place of traditional fluid cycle heat pumps. A mathematical model of a thermoelectric lyophilizer is described and used to generate energy use and processing rate estimates.

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models were developed to simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allowed only changes in light energy and used a less accurate linear approximation. For constant nominal environmental conditions, the simulation outputs of the new MEC models are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have more realistic exponential canopy growth, and have corrected harvest dates for potato and tomato.

Commercial aviation relies almost entirely on subsonic fixed wing aircraft to constantly move people and goods from one place to another across the globe. While air travel is an effective means of transportation providing an unmatched combination of speed and range, future subsonic aircraft must improve substantially to meet efficiency and environmental targets. The NASA Fundamental Aeronautics Subsonic Fixed Wing (SFW) Project addresses the comprehensive challenge of enabling revolutionary energy-efficiency improvements in subsonic transport aircraft combined with dramatic reductions in harmful emissions and perceived noise to facilitate sustained growth of the air transportation system. Advanced technologies, and the development of unconventional aircraft systems, offer the potential to achieve these improvements.

A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network.

The CELSS Antarctic Analog Project (CAAP) is a joint NSF and NASA project tor the development, deployment and operation of CELSS technologies at the Amundsen-Scott South Pole Station. CAAP is implemented through the joint NSF/NASA Antarctic Space Analog Program (ASAP), initiated to support the pursuit of future NASA missions and to promote the transfer of space technologies to the NSF. As a joint endeavor, the CAAP represents an example of a working dual agency cooperative project. NASA goals are operational testing of CELSS technologies and the conduct of scientific study to facilitate technology selection, system design and methods development required for the operation of a CELSS. Although not fully closed, food production, water purification, and waste recycle and reduction provided by CAAP will improve the quality of life for the South Pole inhabitants, reduce logistics dependence, and minimize environmental impacts associated with human presence on the polar plateau.

This paper discusses the development of a Vapor Phase Catalytic Ammonia Reduction (VPCAR) teststand and the results of an experimental program designed to evaluate the potential of the technology as a water purification process. In the experimental program the technology is evaluated based upon product water purity, water recovery rate, and power consumption. The experimental work demonstrates that the technology produces high purity product water and attains high water recovery rates at a relatively high specific power consumption. The experimental program was conducted in 3 phases. In phase I an Igepon™ soap and water mixture was used to evaluate the performance of an innovative Wiped-Film Rotating-Disk evaporator and associated demister. In phase II a phenol-water solution was used to evaluate the performance of the high temperature catalytic oxidation reactor.

The paper analyzes energy use and emissions per GJ of various fuels delivered to the vehicle fuel tank, covering extraction, fuel production, transportation, storage, and distribution phases of the life cycle of alternative fuels. Drawing on a number of existing studies, the modeling issues and approaches, main results and insights are summarized. The range of estimates in various studies is large; however, common patterns can be observed. The analysis indicates, that conventional gasoline fuel cycle has robust advantages with respect to energy efficiency, conventional pollutant emissions, and most importantly, existing infrastructure compared to alternative fuels. Fossil fuel based alternatives like CNG, NG–Methanol, NG–FTL do not result in significant improvement in fuel cycle environmental performance. Biofuels offer the benefits of lower and even negative GHG emissions, sustainability, and domestic fuel production.

An analysis method for characterizing fuel behavior during spark-ignition engine starting has been developed and applied to several sets of start-up data. The data sets were acquired from modern production vehicles during room temperature engine start-up. Two different engines, two control schemes, and two engine temperatures (cold and hot) were investigated. A cycle-by-cycle mass balance for the fuel was used to compare the amount of fuel injected with the amount burned or exhausted as unburned hydrocarbons. The difference was measured as “fuel unaccounted for”. The calculation for the amount of fuel burned used an energy release analysis of the cylinder pressure data. The results include an overview of starting behavior and a fuel accounting for each data set Overall, starting occurred quickly with combustion quality, manifold pressure, and engine speed beginning to stabilize by the seventh cycle, on average.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.