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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy Efficiency as a Foundational Technology Pillar for Industrial Decarbonization

The U.S. government aims to achieve net-zero greenhouse gas emissions by 2050 to reduce the severe impacts of climate change. The U.S. industrial sector will become a focal point for decarbonization since it accounts for 33% of the nation’s primary energy use and 30% of its energy-related CO2 emissions. Industrial emissions are also expected to increase by 15% through 2050, making the industrial sector a logical target for decarbonization efforts. Energy efficiency technology pathways provide low-cost, foundational routes to decarbonization that can be implemented immediately. Energy efficiency technology pathways, such as strategic energy management, system efficiency, smart manufacturing, material efficiency, and combined heat and power, are well established and would immediately reduce energy use and emissions. However, their role in the aggressive net-zero decarbonization pathway for the industrial sector is still unclear. This study aims to address energy efficiency pathways for decarbonization, and reviews studies related to these technologies for industrial decarbonization through 2050. This study identifies different strategies for the industrial sector in general and that are specific to six energy-intensive industries: iron and steel; chemical; food and beverage; petroleum refining; pulp and paper; and cement. Finally, a path toward the successful implementation of energy efficiency technologies is outlined.

Strategic energy management↗
DOE OSTI2022

NCSU Industrial Assessment Center

Renewable energy technologies are becoming more attractive and gaining more attention as the world becomes increasingly aware of the negative impacts of global warming and greenhouse gas (GHG) emissions. Two such emerging technologies are geothermal heat pumps (GHP) and solar thermal heating which can be used for heating and cooling of buildings. GHP systems are a well-studied technology that have been proven to be more efficient than traditional air-source heat pumps for space conditioning of buildings. Solar thermal heating has become more common as a way to heat domestic hot water (DHW) in homes, but the two technologies are not often combined. This study presents the findings of combining an open-loop GHP with 100 ft2 of flat plate solar thermal panels and a 5,655-gallon sensible water storage tank for space heating and DHW heating needs of a residential home in Bozeman, MT, USA. The goal of this system was to use energy from the solar panels and storage tank first to decrease pumping energy from the GHP well. The open loop system was designed to be a backup system to the solar thermal panels and sensible storage tank. An actual home was modeled to determine potential energy and cost savings of such a system compared to a traditional propane boiler. Then, a case study was performed on the installed system from January to June 2021. Results obtained from April 2nd to June 5th, 2021 indicated that the combination system supplied all necessary heat, and that heat was supplied from the thermal storage tank 55% of the time. This represents a savings of 330 kWh of energy and $36 compared to operating just the open loop geothermal system. From April 2nd to June 5th the combination system consumed a total of 2,510 kWh of energy and cost $274 to operate. Based on data obtained, there is significant potential for energy savings compared to a propane boiler, however only small potential for cost savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Establishment of an Industrial Assessment Center (IAC) site at Arizona State University (ASU)

In this document we report the overall accomplishments of the IAC site at ASU. The report is organized in three different areas: conduct industrial assessments; provide training, research, and educational opportunities to IAC students; and other activities. The report includes those assessments made between September 2016 to August 2021.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

University of Miami Industrial Assessment Center (Final Scientific/Technical Report)

The University of Miami Industrial Assessment Center (MIIAC) is located at the Department of Industrial & Systems Engineering in the College of Engineering at University of Miami. The Center has conducted 402 energy assessments with estimated Total Energy Savings over $\$57.3$ million, Total Non- Energy Cost Savings of $\$23.7$ million, and Total Energy Saving as a 26% of Energy Costs of $\$311$ million. A total of 6.01 TBtu of energy savings were recommended as a result of energy efficiency measures resulting in $\$81$ million in cost savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

NC State University Industrial Assessment Center. Final Report

This report documents work from the Industrial Assessment Center program at NC State University. The program is funded by the US Department of Energy Advanced Manufacturing Office. The IAC provides energy conservation assessments for small and medium sized manufacturers in NC, SC, and VA. Students are involved in all aspects of the assessment including the on-site visit, interacting with plant personnel, collecting data, performing engineering analyses on potential energy efficiency measures, and producing a technical report for the client.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties (CRADA 511)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and ESI North America Inc. (“ESI” or “Participant”) to apply computation and data analytics to the challenge of light weighting with a focus on the battery enclosures of electric vehicles (EVs). EVs use heavy batteries to increase range and power. A complex-shaped battery enclosure is required to meet a host of challenging performance requirements. The ability to virtually develop composite parts such as battery enclosure with tailored properties to meet required performance will be highly valuable to the automotive industry. However, efficient simulation of composite-manufacturing processes remains a challenging issue since simulation involves multiscale models in space and time, highly non-linear and anisotropic behavior, strongly coupled multi-physics, and complex geometries. This work will advance the state of the art by reducing the computational burden of composite optimization by using simulation data from a limited number of configurations off-line and then developing a reduced order model (ROM) using data analytics and machine learning (ML). Develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts.

42 ENGINEERING↗
DOE OSTI2023

2022 Louisiana State University (Final Report)

LSU-IAC started in LSU’s College of Engineering in 2016 with funding from the U.S. Department of Energy’s IAC program. Due to the Covid 19 pandemic, the grant end date was extended from 8/31/2021 to 2/28/2022. In the 5-year performance period, LSU-IAC has conducted a total of 67 industrial assessments and trained a total number of 41 students. We have recommended 408 improvement measures with potential energy savings of 58,951,388 kWh and energy cost savings of $6,817,783 if implemented. Of the 41 students trained, 16 received DOE certificates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fire Risk Investigation in 3D (FRI3D) Software and Process for Integrated Fire Modeling

Modeling and implementing fire safety for nuclear power plants is a costly activity. Because of the complexity of fire phenomena and multiple operational procedures, it is difficult to computationally provide assurance that mitigation methods are adequate for critical areas using current analysis methods in a cost-effective manner. The nuclear industry needs efficient methods to provide more accurate modeling and optimize mitigation methods to improve nuclear power plant understanding for fire scenarios. This report describes the work done over the last two year on the Fire Risk Investigation in 3D (FRI3D) software. This software simplifies the modeling process by importing existing plant models and data, incorporating a 3D modeling environment and coupling with the fire simulation software CFAST. This eliminates the manual labor and errors associated with much of the fire modeling process. The report lays out the status and path of FRID software for the Risk Informed Safety Analysis pathway of the Light Water Reactor Sustainability project at Idaho National Laboratory and industry collaborators to best help reduce costs and improve realism in fire probabilistic risk analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Public – Private Partnership to Promote Efficient Manufacturing and Workforce Development (IAC Final Technical Report)

Through delivering ninety (90) energy assessments to small- and medium-sized manufacturing facilities in and around Tennessee, significant understandings were gained through providing detailed energy assessment reports to manufacturers and training seventy-seven (77) engineering students in which thirty-five (35) certified IAC students were recognized by DOE. The robust involvement of Tennessee Valley Authority (TVA), local utility and Tennessee Tech’s Cybersecurity Education, Research, and Outreach Center (CEROC) in majority of assessments made the technical operation very effective where leaded to 40.5% implementation rate of energy recommendations. Other benefits of the award to the public are a dynamic modeling tool capable of analyzing existing cooling towers for energy use optimization was developed and presented to DOE IAC, providing technical assistance to non-participating manufacturers in the form of workshop and training, publishing fifteen (15) peer-reviewed conference and journal papers as well as a book chapter in the area of industrial energy efficiency field, assisting the TVA Magnolia Combined Cycle Power Plant to become the First 50001 Ready Certified Power Plant in the U.S. through briefing them on ISO 50001 and 50001 Ready as well as providing an energy assessment to the plant, assisted the new IAC center at Clemson University with on-site and remote support, and provided assessment and training in unique areas of water, waste water, cyber security and smart manufacturing to our clients, students and community.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Industrial Assessment Center Program Final Progress Report

During September 1, 2016 to March 31, 2022 period, the University of Massachusetts’ Industrial Assessment Center completed 59 industrial assessments at plants across New England. Total annual sales for those plants, which employed 8,861 people, was close to $\$ 2.59$ billion. Our 317 recommendations amounted to potential savings of $\$ 6,866,009$, which represented 14.11% of the total energy costs these facilities incurred. By the time this report was prepared implementation information was available for all plants. A total of 132 recommendations, or 41.64%, were implemented, with annual savings totaling $\$ 1,856,921$, or 3.82% of total plant energy costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Modeling and Understanding of Rear Junction Double-Side Passivated Contact Solar Cells with Selective Area TOPCon on Front

Device modeling is performed to propose > 25% efficient industry-compatible rear junction double-side passivated contacts solar cell structure with full area p-TOPCon on the rear and selective area n-TOPCon under the front grid pattern (selective TOPCon). Here, this design enables the use of thicker TOPCon (>100nm) on the front for traditional screen-printed contacts without incurring metal-induced damage, high parasitic absorption loss, and compromise in lateral transport or carrier collection on the front. Rear junction design with appropriate bulk lifetime and resistivity combination eliminate the need for heavy doping in the front field region because carriers can flow through the bulk Si without appreciable FF loss. High VOC is maintained because high-quality Si surface passivation in the field region by Al 2 O 3 /SiN gives J 0 comparable to the TOPCon. Our device modeling specifies the practically achievable properties and parameters for each region, including full area rear p-TOPCon, selective area front n-TOPCon, bulk and contacts, to achieve 25.4% efficiency screen-printed bifacial rear junction selective TOPCon cells.

14 SOLAR ENERGY↗
DOE OSTI2022

UF Industrial Assessment Center - Budget Periods 2017-2021. Final Report

This report summarizes the work performed at the University of Florida’s Industrial Assessment Center (UF-IAC) during the five budget periods (BPs) in the years 2017 through 2021 under DOE Award No. DE-EE0007707. The objective of the work is to perform industrial assessments to small- and medium-sized manufacturing facilities in the State of Florida and to train students in industrial energy management and in performing industrial assessments. Specific details about the activities of the UF-IAC (referred to as the Center) during that time can be found in the 20 quarterly reports that were submitted throughout the duration of the contract. The project start and end dates are September 1, 2016 and August 31, 2021, respectively. The project was granted a six-month no-cost extension till February 28, 2022, to accommodate unexpected delays due to the COVID-19 pandemic. This project was performed by the University of Florida (PI: Dr. SA Sherif). The scope of work includes six milestone whose description and outcomes are listed in the Statement of Project Objectives (SOPO).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

ABSTRACT In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novel method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification

Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. Herein, this paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.

42 ENGINEERING↗
DOE OSTI2023

Manufacturing Water‐Based Low‐Tortuosity Electrodes for Fast‐Charge through Pattern Integrated Stamping

Achieving high energy density and fast charging of lithium‐ion batteries can accelerate the promotion of electric vehicles. However, the increased mass loading causes poor charge transfer, impedes the electrochemical reaction kinetics, and limits the battery charging rate. Herein, this work demonstrated a novel pattern integrated stamping process for creating channels in the electrode, which benefits ion transport and increases the rate performance of the electrode. Meanwhile, the pressure applied during the stamping process improved the contact between electrode and current collector and also enhanced the mechanical stability of the electrode. Compared to the conventional bar‐coated electrode with the same thickness of 155 μm (delivered a discharge capacity of 16 mAh g −1 at the rate of 3 C), the stamped low‐tortuosity LiFePO 4 electrode delivered 101 mAh g −1 capacity. Additionally, water was employed as a solvent in this study. Owing to its eco‐friendliness, high scalability, and minimal waste generation, this novel stamping technique inspire a new method for the industrial‐level efficient roll to roll fabrication of fast‐charge electrodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Boosting the performances of protonic solid oxide fuel cells for co-production of propylene and electricity from propane by integrating thermo- and electro- catalysis

Protonic solid oxide fuel cells (p-SOFC) integrated with clean thermal energy sources are promising platforms for decarbonized chemical production in addition to power generation, such as on-purpose propylene production from propane dehydrogenation (PDH). The catalytic performance of the conventional nickel-cermet-based anode materials in p-SOFC for propane conversion is restrained by their low active surface area and proneness to coking. In this work, by integration of a highly efficient industry-relevant thermal catalyst PtGa/ZSM-5 for PDH reaction, we demonstrate that both the electrochemical and catalytic performance of the propane-fueled p-SOFC can be effectively enhanced. The PtGa catalyst integrated p-SOFC exhibits a peak power density of 93 mW cm -2 at 600°C, which is greater by about 100% and 50% than that without catalyst or with a perovskite-based (Pr 0.3 Sr 0.7 ) 0.9 Ni 0.1 Ti 0.9 O 3 (PSNT) catalyst layer, respectively. The PDH activity and olefin selectivity of the PtGa catalyst is also significantly higher than that of the PSNT catalyst. In addition, much improved coke tolerance and propylene selectivity (over 90%) compared to the catalyst-free Ni-cermet anode materials were achieved by integrating the industrial catalyst layer. The propane conversion can be further improved by an applied current density, whereas the olefin selectivity is almost unaltered. The excellent performance of the PtGa catalyst integrated p-SOFC is attributed to the high surface area, intrinsically high catalytic activity, selectivity, and anti-coking properties of the catalytic layer for propane conversion. In conclusion, this work provides a general approach and a case study for boosting the performances of p-SOFCs in chemical production by integrating thermo- and electro- catalysis.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Catalytic Reaction Triggered by Magnetic Induction Heating Mechanistically Distinguishes Itself from the Standard Thermal Reaction

As a recent advancement in chemical engineering, magnetic induction heating (MIH) is utilized to initiate the intended reactions by enabling the self-heating of the ferromagnetic catalyst particles. While MIH can be energy-efficient and industrially scalable, its full potential has been underappreciated in catalysis because of the perception that MIH is merely an alternative heating approach. Unexpectedly, we show that the MIH-triggered reaction could go beyond standard thermal catalysis. Specifically, by probing the representative Pt/Fe 3 O 4 catalysts with CO oxidation in both thermal and MIH modes with consistent temperature profiles and catalyst structures, we found that the MIH mode boosts the reactivity more than 25 times by modifying Pt-FeO x interfacial synergies and promoting facile oxidation of the adsorbed carbonyl species by atomic oxygen. Further, as we preliminarily observed, this beneficial MIH-catalysis can be translational to other thermal reactions, potentially paving the way to launch MIH-catalysis as a distinct reaction category.

CO oxidation↗