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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Design of Novel Flash Ironmaking Reactors for Greatly Reduced Energy Consumption and CO2 Emissions

The development of a novel ironmaking technology based on fine iron ore concentrate in a flash reactor is summarized. The design of potential industrial reactors for flash ironmaking based on the computational fluid dynamics technique is described. Overall, this simulation work has shown that the size of the reactor used in the novel flash ironmaking technology (FIT) can be quite reasonable vis-à-vis the blast furnaces. A flash reactor of 12 m diameter and 35 m height with a single burner operating at atmospheric pressure would produce 1.0 million tons of iron per year. The height can be further reduced by either using multiple burners, preheating the feed gas, or both. The computational fluid dynamics (CFD)-based design of potential industrial reactors for flash ironmaking pointed to a number of features that should be incorporated. The flow field should be designed in such a way that a larger portion of the reactor is used for the reduction reaction but at the same time excessive collision of particles with the wall must be avoided. Further, a large diameter-to-height ratio that still allows a high reduction degree should be used from the viewpoint of decreased heat loss. This may require the incorporation of multiple burners and solid feeding ports.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Method and system for dehumidification and atmospheric water extraction with minimal energy consumption

Methods, systems and devices for managing humidity within an HVAC system including a nanostructured desiccant porous material configured to adsorb water from an inlet stream at a first air pressure and to release water from that material when subjected to a second air pressure when the second air pressure is lower than the first air pressure is located within a particular location so as to allow for the passage of wet air over the materials and allow adsorption of the water on to the material. When coupled with a vacuum pump water can be collected and released from the materials and the system, regenerating the material for future use and removing water from a stream at a significantly lower cost than existing processes.

McGrail, Bernard P.↗
DOE OSTI2020

Background Data and Statistics on Low-Income Energy Use and Burden for the Weatherization Assistance Program: Update for Fiscal Year 2020

This report provides updated estimates of the energy consumptions, energy expenditures, and energy burdens of the population eligible to be served through the US Department of Energy (DOE) Weatherization Assistance Program (WAP) and by proxy, those clients actually served by the program. WAP continues to serve a population with an energy burden that is much greater than the rest of the county. The mean energy burden for the WAP-eligible population across the United States for FY 2020 is estimated to be 13.9% compared to 3.0% for households living above the 200% poverty threshold. The mean energy burden for households living below the 100% federal poverty line was estimated to be 30.9%. These energy burdens for the WAP-eligible population have remained steady at this high level over the last decade.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

CBETA: The first multi-pass superconducting linear accelerator with energy recovery

Energy recovery has been achieved in a multipass linear accelerator, demonstrating a technology for more compact particle accelerators operating at higher currents and reduced energy consumption. Energy delivered to the beam during the first four passes through the accelerating structure was recovered during four subsequent decelerating passes. High-energy efficiency was achieved by the use of superconducting accelerating cavities and permanent magnets. The fixed-field alternating-gradient optical system used for the return loop successfully transported electron bunches of 42, 78, 114, and 150 MeV in a common vacuum chamber. This new kind of accelerator, an eight-pass energy recovery linac, has the potential to accelerate much higher current than existing linear accelerators while maintaining small beam dimensions and consuming much less energy per electron.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Analysis of Energy Savings and CO2 Emission Reduction Contribution for Industrial Facilities in USA

Abstract Energy audits can identify energy consumptions, energy costs of the facility and evolve to develop measures to maximize efficiency, optimize supply energy, and eliminate waste. This paper investigates the potential energy savings at 20 different industrial sectors with 152 assessments for various facilities in Wisconsin, USA. On average, eight energy recommendations were suggested and applied in each facility. This paper provides a detailed guideline for each industry in terms of eight different energy categories: heating, ventilation, and air conditioning (HVAC) systems, heat recovery systems, electrical demand management, and utility bills, motors, compressors, waste management, and productivity enhancement, lighting, besides building envelope. In total, the energy savings were as follows: 98 million kWh in the shape of electricity, 561 billion British thermal units (BTUs) gas savings, 44 million gallons water savings, and 2-million-pound solid waste savings. Based on these savings, a 100-thousand-ton reduction in carbon dioxide emissions was obtained.

Energy & Fuels↗
DOE OSTI2023

Levelized Cost of Avoided CO2 tool (LCAC tool) v0.9

The levelized cost of avoided CO2 (LCAC) tool facilitates techno-economic calculations for various decarbonization technologies. It comprises two components to accomplish this: an input workbook based on MS Excel and a web-based output tool. The workbook acts as the input sheet, using the user-provided decarbonization measures and their impacts on lifetime energy consumption, energy costs, and CO2 emissions to compute LCAC (expressed in $/metric ton of avoided CO2). The workbook is uploaded to a web-based tool to generate an LCAC curve. This graphical representation illustrates the economic comparison of all decarbonization measures for purposes such as implementation prioritization. We are unaware of any publicly available tools with similar capabilities.

Ali Qamar, Muhammad↗
DOE OSTI2024

Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer

In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.

Wojda, Rafal↗
DOE OSTI2025

Effects of ambient temperature on electric vehicle range considering battery Performance, powertrain Efficiency, and HVAC load

Here, this study investigates the impact of ambient temperature on the range of electric vehicles (EVs) by analyzing its effects on usable battery energy (UBE), heating, ventilation, and air conditioning (HVAC) energy consumption, and powertrain energy losses. Chassis dynamometer tests within a thermal chamber were conducted under various temperature conditions to investigate these impacts. The results indicate that lower temperatures lead to a decrease in UBE for lithium-ion batteries in EVs. At −18 °C, the UBE exhibited reductions of 4---8 % compared to the UBE at 22 °C. Battery thermal management strategies significantly affected the UBE loss, with different strategies resulting in distinct UBE reductions. HVAC energy consumption, especially for interior heating, proved to be the most dominant variable affecting EV driving range. Larger discrepancies between the HVAC target temperature (22 °C) and the ambient temperature increased HVAC energy usage. The type of HVAC system also influenced energy consumption, where EVs equipped with heat pumps demonstrated lower energy consumption for heating compared to those relying solely on resistance heaters. Ambient temperature also influenced motor energy consumption due to increased frictions, powertrain losses and tire rolling resistance at lower temperatures; consequently, regenerative braking energy decreased in cold conditions. Combining these effects influenced the overall energy consumption and driving range of EVs. At −18 °C, the driving range saw a substantial decrease of up to 60 % compared to 22 °C, while a slight decrease was observed at 35 °C.

Ambient Temperature↗
DOE OSTI2022

Experimental driving performance evaluation of battery-powered medium and heavy duty all-electric vehicles

As the push to reduce air pollutants and carbon dioxide for global warming continues, there is increasing interest in the development and deployment of battery electric vehicles for heavy-duty applications as one of the critical and challenging steps of a broader effort to fully electrify the transportation sector. This study evaluated and quantified the energy consumption rates and energy regeneration rates for three heavy-duty battery electric vehicles (a step van, a yard tractor, and a class 8 truck) using driving cycle tests with a heavy-duty vehicle chassis dynamometer. Information in this area is currently very limited, and yet it is critical to understanding how effective heavy-duty battery electric vehicles will be for a wide range of applications as the electrification of the heavy-duty vehicle market continues to expand. Energy consumption rates ranged from 0.37 and 2.71 kWh/km for most vehicle/cycle combinations. On a diesel liter energy-equivalent basis, the heavy-duty vehicles had approximately 3 to 6 times better energy consumption rates compared to comparable diesel vehicles, with weight normalized energy consumption rates also comparing favorably to the current and future greenhouse gas/fuel economy standards for equivalent diesel vehicles. Based on battery capacities ranging from 80 to 215 kWh, the vehicle ranges varied from 38.18 km to 171.2 km, with ranges of 115.9 km to 171.2 km for most types of driving and lower ranges of 38.18 to 42.20 km for sustained grade cycles. Overall, the advantage of improved energy consumption rates coupled with improvements that can be expected for battery technology and vehicle range, provide promise for expanded use of heavy-duty vehicles going into the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

The interactive indoor-outdoor building energy modeling for enhancing the predictions of urban microclimates and building energy demands

There is a lack of an urban building energy modeling framework that considers the influence of surrounding buildings and local urban climate on building thermal performance. This can lead to inaccurate results since the thermal performance of individual buildings is heavily influenced by their surrounding built and climatic environment. This study establishes an interactive indoor-outdoor building energy modeling method to enhance the predictions of urban microclimates and building energy demands by coupling an urban physics model with a physics-based building energy model. Validation of the interactive coupling scheme uses field measurement datasets. Parametric simulation and analysis are conducted to understand the influence of the roof-to-canyon width ratio, canyon orientation, and ground vegetation fraction on canyon temperature, building energy consumption, and energy demand. Furthermore, the impacts of building energy model complexity (e.g., detailed vs. simplified building models) and coupling approaches on canyon temperature and building energy profiles are demonstrated using two case study buildings. In comparison with the one-way coupling approach, cooling energy consumption predicted with the dynamic two-way coupling approach varies by 3.5% and 0.5% for the detailed medium office building model and high-rise building model, respectively, and peak cooling demand varies by 8.4% and 7.0% for the detailed medium office building model and high-rise building model, respectively. Here this study also suggests that adopting a complex two-way coupling approach with environmental data exchange at various elevations is necessary for modeling tall buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling Needs to Support the Reconstruction Strategy of Ukraine

Ukraine has been facing difficult times. Russia started a full-scale invasion on February 24, 2022, and has caused huge human and infrastructure losses. As a result of the invasion, more than 8 million people left their homes. The Russian army has targeted Ukrainian energy sources, destroying or damaging over 50% of thermal power generation capacity (coal and gas), 30% of solar generation, and 90% of wind generation. The Russians are also threatening Ukrainian energy security, as they currently control the Zaporizhzhia nuclear power plant – Europe’s largest. In addition, Russia has destroyed 121,000 buildings, including 5,300 multistory buildings - more buildings than Ukraine built during the past five years. The occupiers destroyed or damaged 343 district heat boiler stations (destroyed 12 / damaged 331) and 8 CHPs (destroyed 4/ damaged 4). There is no doubt that Ukraine will be able to protect its sovereignty and restore its territorial integrity; however, this will be a long process. The Government of Ukraine needs to simultaneously focus on many issues, from waging war to the restoration of electricity, water, and heat supply in the territories close to the front line. In addition to these time-sensitive issues, the government also needs to plan a long term multi-sector strategy for the reconstruction of Ukraine’s economy. Ukraine initiated a broad-based political process for recovery at the International Ukraine Recovery Conference (URC 2022) in Lugano, Switzerland. Prior to URC 2022, Ukraine applied for EU membership on February 28 and was granted the status of candidate country by the European Council on June 23, 2022. More than 40 nations are fully committed to supporting Ukraine throughout its path from early to long-term recovery. In addition, these countries link a successful recovery to Ukraine’s European future (URC, 2022a). The reconstruction process will be long. The government also recognizes that it is not enough to build energy infrastructure back – it should be built back better. The government is committed to building a more sustainable and cleaner energy sector. Energy modeling can provide important insights into paths to decarbonize Ukraine’s economy while rebuilding it after the war. Pacific Northwest National Laboratory (PNNL) and the Institute for Economics and Forecasting of the National Academy of Sciences of Ukraine (IEF) work on modeling decarbonization scenarios in the energy sector of Ukraine. The teams focus on the heat sector, which includes district heat, autonomous and individual heating, and energy consumption by final energy consumption sectors, including buildings. Since the teams use world-class integrated assessment models, other sectors will be analyzed as well. The purpose of the document is to describe the needs of key Ukrainian ministries in modeling the reconstruction process. The scope of this document is limited to modeling energy consumption by the power and heat sectors, final energy consumption by buildings, and GHG emissions from these two sectors. The rest of this policy memo is organized as follows. After this introduction, Chapter 2 discusses key provisions of the reconstruction strategy with a focus on energy, buildings, heat supply, environment, and European integration aspirations. Section 3 provides an overview of recent developments and key decisions of the Government of Ukraine in the area of energy efficiency and decarbonization. Finally, Section 4 provides key suggestions on how PNNL and IEF can help the Government of Ukraine in developing strategic documents.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗