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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. Furthermore, the impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The role of the iron and steel sector in achieving net zero U.S. CO 2 emissions by 2050

The U.S. steel sector is a hard-to-abate sector because of its heavy dependence on fossil fuels and its high capital requirements. In 2015, the sector was one of the major carbon emitters, contributing 10 % of the U.S. industrial CO 2 emissions. The ability to decarbonize the U.S. iron and steel sector directly affects the ability of the U.S. to achieve economy-wide net zero CO 2 by 2050. In this paper, we use the Global Change Analysis Model (GCAM) to analyze different U.S. steel sector decarbonization pathways under varying technology, policy, and demand futures. These pathways provide insights on how various low-carbon steelmaking technologies such as those using carbon capture and storage (CCS), hydrogen, or scrap could help reduce U.S. steel emissions by mid-century. In our primary decarbonization pathway, we find that nearly all of the conventional fossil-based steelmaking capacity is fully integrated with CCS by 2050. However, without CCS availability, almost all of the conventional fossil-based steelmaking is phased-out by 2050 and is replaced by hydrogen-based production. Scrap-based production continues to remain vital across both of these decarbonization pathways. Furthermore, we find that demand reduction could help reduce the required levels of CCS and hydrogen-based production in the decarbonization pathways. Implementation of advanced energy efficiency measures could help substantially reduce the sector's energy usage. Finally, we observe that addressing the embodied carbon transfer associated with steel imports will be crucial for fully decarbonizing the U.S. steel sector.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Benefits of Dual Fuel Heat Pump Grid-responsive Control: A Model-based Control Optimization Approach Using Building and Equipment Co-simulation

Conventional dual fuel heat pumps lack the intelligent control mechanisms to efficiently manage the switch between heat pump and furnace, leading to sub-optimal energy usage and, in some cases, increased operating costs. To resolve this gap, this study applies optimized control on hybrid heat pumps. With a focus on equipment control strategies, we compare the performances of five spacing heating equipment, including a conventional heat pump (HP), a conventional furnace, a dual fuel heat pump (DFHP) with conventional control, a dual fuel heat pump with smart control, and a novel seamlessly fuel flexible heat pump (SFFHP). While DFHP runs on either gas or electricity at any given moment, SFFHP concurrently consumes gas and electricity by continuously optimizing the proportion of each. In this research, a co-simulation framework is developed by integrating a building envelope model with a physics-based heat pump simulation model to analyze the benefits of grid-responsive controls of DFHP and SFFHP. The model-based optimal controls adjust the operation of the heat pump and gas furnace based on utility price signals and marginal grid emission to minimize utility cost and CO 2 emissions for multiple climate zones, different utility tariffs, and marginal grid emission scenarios. Case studies in Chicago and Los Angeles demonstrate that SFFHP and DFHP, with model-based optimal control, can deliver significant reductions in peak demand, utility cost, and CO 2 emission. In Chicago, SFFHP and smart controlled DFHP yield up to 64.7% and 61.7% utility cost reduction and up to 15.7% and 8.5% CO 2 emission reduction compared to the gas furnace. In Los Angeles, SFFHP and smart controlled DFHP achieve up to 43.6% and 40.1% utility cost reduction and up to 13.8% and 14.1% CO2 emission reduction compared to conventional heat pumps. In conclusion, by leveraging the fuel flexibility nature of dual fuel heat pumps, the model-based control optimization approach makes dual fuel heat pump an attractive option for demand response programs.

Control↗
DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗
DOE OSTI2024

Impacts of caking on corn stover – An assessment of moisture content and consolidating pressure

Caking or time consolidation of powders is a serious problem which can hinder productivity and overall feasibility of various industrial processes. Here this study focuses on the impacts moisture content and consolidating pressures can have on 2 mm corn stover samples after undergoing a drying treatment at different times. Individually, moisture content and consolidating pressure did not exhibit any significant changes throughout the modified variable flow rate tests using the FT4 powder rheometer. However, applying both variables simultaneously yielded higher-than-usual energy outputs from the corn stover, indicative of biomass agglomeration due to the moisture and its subsequent evaporation while under pressure via the induced consolidating pressure. However, despite the increase in energy usage, time dried did not have a direct effect and demonstrated no trend.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of a micro-combined heat and power powered by an opposed-piston engine in building applications

Residential homes and light commercial buildings usually require substantial heat and electricity simultaneously. A combined heat and power system enables more efficient and environmentally friendly energy usage than that achieved when heat and electricity are produced in separate processes. However, due to financial and space constraints, residential and light commercial buildings often limit the use of traditional large-scale industrial equipment. Here we develop a micro–combined heat and power system powered by an opposed-piston engine to simultaneously generate electricity and provide heat to residential homes or light commercial buildings. The developed prototype attains the maximum AC electrical efficiency of 35.2%. The electrical efficiency breaks the typical upper boundary of 30% for micro–combined heat and power systems using small internal combustion engines (i.e., <10 kW). Moreover, the developed prototype enables maximum combined electrical and thermal efficiencies greater than 93%. The prototype is optimally designed for natural gas but can also run renewable biogas and hydrogen, supporting the transition from current conventional fossil fuels to zero carbon emissions in the future. The analysis of the unit’s decarbonization and cost-saving potential indicate that, except for specific locations, the developed prototype might excel in achieving decarbonization and cost savings primarily in US northern and middle climate zones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Physics for neuromorphic computing

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. Here, we discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

device physics↗
DOE OSTI2024

Modeling with uncertainty quantification reveals the essentials of a non-canonical algal carbon-concentrating mechanism

The thermoacidophilic red alga Cyanidioschyzon merolae survives its challenging environment likely in part by operating a carbon-concentrating mechanism (CCM). Here, we demonstrated that C. merolae 's cellular affinity for CO 2 is stronger than the affinity of its rubisco for CO 2 . This finding provided additional evidence that C. merolae operates a CCM while lacking the structures and functions characteristic of CCMs in other organisms. To test how such a CCM could function, we created a mathematical compartmental model of a simple CCM, distinct from those we have seen previously described in detail. The results of our modeling supported the feasibility of this proposed minimal and non-canonical CCM in C. merolae . To facilitate the robust modeling of this process, we measured and incorporated physiological and enzymatic parameters into the model. Additionally, we trained a surrogate machine-learning model to emulate the mechanistic model and characterized the effects of model parameters on key outputs. This parameter exploration enabled us to identify model features that influenced whether the model met the experimentally derived criteria for functional carbon concentration and efficient energy usage. Such parameters included cytosolic pH, bicarbonate pumping cost and kinetics, cell radius, carboxylation velocity, number of thylakoid membranes, and CO 2 membrane permeability. Our exploration thus suggested that a non-canonical CCM could exist in C. merolae and illuminated the essential features generally necessary for CCMs to function.

Steensma, Anne K. [Michigan State Univ., East Lans↗
DOE OSTI2024

Empirically Categorizing the Built Environment in Relation to Height

Buildings are a core component of the urban environment and affect human populations, energy usage, city development, city planning, and urban heat islands. Buildings span an enormous range of sizes, from a 2m tall shelter to the Burj Khalifa; and at the same time there are widely recognized categories of similar buildings, with homes, office buildings, or skyscrapers as some examples. Currently, there is no consistent method to quantitatively determine how a building should be categorized by its height, or how many categories there should be within the built environment. Additionally, these categories vary spatially, leading to multiple definitions at local scales of what it means to be a tall, medium, or short building. Here, we find across 17.59 million buildings in the United States, Germany, and Japan, that applying a K-nearest neighbor approach to quantitatively bin the built environment outperforms the current state-of-the-art, subjective domain knowledge. This was evidenced as our method of leveraging a K-nearest neighbor improved upon the existing approach of using domain knowledge by 10% with respect to precision, recall, F1-score and accuracy. Our results showcase the finding that it is possible to generate a global and consistent approach to categorizing the built environment in relation to height. This is significant in that there is now a quantitative way to categorize the built environment based on building height at a global scale, allowing researchers a consistent platform for comparison and collaboration across various applications.

Stipek, Clinton↗
DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗
DOE OSTI2025

mDNS to support local price server discovery with OpenADR 3 (mDNS for OpenADR 3) v1.0

This software contains a template VEN with local VTN service discovery over mDNS. It provides common starter code for an OpenADR3.0 VEN that advertises itself over mDNS, conducts local VTN service discovery over mDNS, connects to the VTN over HTTP(S), and regularly polls and acts on energy prices and events hosted on the VTN. The software is written to be easily modified to accommodate different VEN appliances, VEN-VTN networking protocols, user interfaces, and default responses, given the wide range of possible use cases for local price server discovery. OpenADR3.0 is an open communications standard from the OpenADR Alliance that is designed to provide two-way information exchange regarding e.g., dynamic price and event signals to utility applications, so that customers can modify their energy usage to save money and reduce their carbon footprint.

Nordman, Bruce [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Dynamic Facade Dashboard v0.1.0

The dashboard is a useful tool for early-stage building design decision-making and communication, as it can help users quickly compare the energy and non-energy related performance of various automated, integrated facade systems using a library of pre-computed data. Users can explore the impacts of various design choices by selecting different facade glazing and shading systems, facade control strategies, and lighting control strategies across multiple climate zones. The dashboard instantly visualizes key metrics, including energy usage in HVAC and lighting, peak cooling and heating load, and daylight availability, allowing immediate trade-off analysis to optimize building efficiency and comfort.

Yu, Tammie [Lawrence Berkeley National Laboratory ↗
DOE OSTI2021

National Park Service Bus Electrification Study: 2020 Report

This report summarizes important considerations for implementing BEBs in the three national park fleets, detailing information about current buses at each fleet, electric bus demonstration vehicles, as well as performance evaluations of BEBs in Zion, Bryce, and Yosemite. Results include in-use data collection results reporting metrics such as average bus speed, energy usage per trip, and daily distance traveled, as well as effects of high heating, ventilation, and air conditioning (HVAC) system use to both heat and cool the buses, emissions estimations before and after use of electric buses, operating costs, electric vehicle infrastructure, maintenance, and bus driver user experience survey information. Analysis results from this project will help the NPS understand how BEBs and future expansion of BEBs could assist in meeting their bottom line and operational goals and assist NPS in choosing appropriate locations for future BEB deployments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Increasing Z-Strength and Testing the Capabilities of Twin Screw Extruders in Large Format Polymer Additive Manufacturing

During the first phase of this project, small and large Strangpresse extruders were assessed at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility (MDF). This phase resulted in Strangpresse exclusively licensing some of ORNL’s previously developed extruder technology for high-volume thermoplastic additive manufacturing (AM). Phase II of this project will focus on further development and optimization of Strangpresse extruders for increased efficiency and increased throughput. The objective of this project is to develop a new extruder with a higher output and less energy usage than current AM thermoplastic extruders and to develop and evaluate a new tamper mechanism for increasing Z-strength resulting in an advanced and optimized extruder and tamper system.

42 ENGINEERING↗
DOE OSTI2023

Near-Term Electricity Requirement and Emission Implications for Sustainable Aviation Fuel Production with CO 2 -to-Fuels Technologies

Aviation contributed approximately 10% of the U.S. transportation sector's greenhouse gas (GHG) emissions and about 3% of the nation's total GHG production before the coronavirus disease (COVID-19) pandemic (EPA 2022). Unlike the ground transportation sector, which can be decarbonized by using batteries and hydrogen fuel cell powertrain technologies, the technical and economic challenges of aviation electrification open the opportunity for CO 2 utilization (CO2U) using clean power sources. The U.S. government set a goal to produce 3 billion gallons per year of sustainable aviation fuels (SAF) by 2030 and scale up the production to 35 billion gallons per year by 2050 (Bioenergy Technologies Office 2022). In this report, we examine three potential locations, in California, Iowa, and Louisiana, for SAF production using two production pathways that are expected to be available by 2030. We analyze the electricity cost to satisfy 10% of the SAF production potential for the select locations. Specifically, we consider (1) retail electricity cost from the default local utility in each location; (2) physical power purchase agreement (PPA) for renewable power and battery storage hybrid systems with preset prices; (3) financial PPA from a dedicated renewable plant; and (4) estimated real-time pricing (RTP) from the wholesale power market with utility delivery adders. Retail rates are available for the three potential locations; however, the developer should negotiate with the local utility in Iowa for new tariff riders, because the load of the proposed SAF plant (1.4 GW) is significantly higher than the current industrial tariff structure requirement (200 kW). The developers are not able to claim federal credits (i.e., the Inflation Reduction Act of 2022) when sourcing electricity from local utilities. Developers could consider prioritizing physical and financial PPAs as purchase options. While physical PPAs can be imported out of state, this structure imposes availability issues as developers must locate in the same electricity grid regions. Financial PPAs do not have the same location restrictions and provide the same cost savings as physical PPAs, but financial PPAs impose financial risks in the event of system curtailment and grid interruption. RTPs are available for customers in California with flexible load and hourly load management capability; however, utilities in our studied regions in Louisiana and Iowa only offer time-of-use and curtailment programs to incentivize lower energy usage in peak hours. These utilities do not currently offer RTP programs, and the developer may need to have further negotiations with the local utilities to access RTP programs. The results show that purchased electricity prices may range from 2.6 cents/kWh to 7.1 cents/kWh for the three studied plants.

02 PETROLEUM↗
DOE OSTI2023

A Digital System-On-Chip CO 2 Sensor (Final Report)

The overall goal of the project was to build a low-cost digital CO 2 sensor made of N5’s patented sensing architecture using a digital application specific integrated circuit (ASIC) to reduce the amount of energy usage in residential and commercial buildings. The proposed small form-factor CO 2 monitoring device contains a molecular sieve membrane filter capable of monitoring indoor air quality which indicates the content of CO 2 inside a residential or commercial building.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of the Performance of Hydrofluoroolefins, Hydrochlorofluoroolefins, and Halogen-Free Foam Blowing Agents in Cellular Plastic Foams

Oak Ridge National Laboratory has conducted a literature search to identify relevant information available on alternative materials and technologies that are proposed for foam blowing agents applications. For comparison, similar information was collected for available hydrofluorocarbons (HFC), hydrofluoroolefin (HFO), and hydrochlorofluoroolefins (HCFO) materials. The publicly available literature was analyzed and assessed for technical accuracy for each alternative, and a gap analysis was conducted to understand if additional studies are required to fully assess the viability of an alternative. In assessing these alternatives, this project focused on relevant performance and safety attributes. The performance attributes collected include insulation value per unit thickness, which impacts energy usage of buildings and insulated equipment such as refrigerators. We also assessed the impact of blowing agents on foam mechanical properties and flammability characteristics, and the ability of current processing equipment and techniques to utilize these alternatives. Diffusion of blowing agents through foam cells and its impact on foam aging was also examined. The applications covered in this study are spray foam, boardstock, panels, and appliances. Some of the most relevant findings are summarized below.

36 MATERIALS SCIENCE↗