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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 OSTI2024

Pathways to commercial building plug and process load efficiency and control

Abstract To accomplish net-zero carbon emissions in the built environment by 2050, we must equitably decarbonize commercial buildings, including reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not associated with major building end uses like lighting and HVAC. Research shows PPL energy reduction strategies and control technologies have the potential to save energy. But even when implemented, these savings have rarely been achieved and there has not been widespread uptake in U.S. commercial buildings. We investigate why these technologies and strategies have not seen widespread adoption and identify behavior and technology pathways to increase PPL reduction in U.S. commercial buildings. We examined behaviors of commercial building stakeholders through 44 interviews and cross-referenced qualitative analysis findings with in-depth technical knowledge of existing PPL control technologies and reduction strategies. PPL control implementation must be paired with management strategies, such as occupant engagement and training, to achieve optimal savings, and best practices should be disseminated across the industry. We found that increasing access to cost and energy savings data will promote uptake of PPL control technologies and allow designers to better incorporate PPLs into building design. Improving access to funding for PPL energy efficiency projects and addressing the split-incentive problem will increase adoption of PPL efficiency and control. Code bodies should continue to include PPL monitoring and reduction measures in energy codes. Key building stakeholders, including cybersecurity and information technology teams, should be involved in PPL monitoring and reduction strategy processes for successful implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Temperature‐Dependent Aggregation Donor Polymers Enable Highly Efficient Sequentially Processed Organic Photovoltaics Without the Need of Orthogonal Solvents

Abstract The conventional method to prepare bulk‐heterojunction organic photovoltaics (OPVs) is a one‐step method from the blend solution of donor and acceptor materials, known as blend‐casting (BC). Recently, an alternative method was demonstrated to achieve high efficiencies (13%) comparable to state‐of‐the‐art BC devices. This two‐step‐coating method, known as “sequential processing,” (SqP) involves sequential deposition of the donor and then the acceptor from two orthogonal solvents. However, the requirement of orthogonal solvents to process the donor and acceptor constrains the choice of materials and processing solvents. In this paper, an improved version of SqP method without the need for using orthogonal solvents is reported. The success is based on donor polymers with strong temperature‐dependent aggregation properties whose solution can be processed at a high temperature, but the resulting film becomes completely insoluble at room temperature, which allows for the processing of overlying acceptors from a wide range of nonorthogonal solvents. With this approach, efficient SqP OPVs is demonstrated based on a range of donor/acceptor materials and processing solvents, and, in every single case, SqP OPVs can outperform their BC counterparts. The results broaden the solvent choices and open a much larger window to optimize the processing parameters of SqP method.

Arunagiri, Lingeswaran↗
DOE OSTI2019

Conjugation‐Curtailing of Benzodithionopyran‐Cored Molecular Acceptor Enables Efficient Air‐Processed Small Molecule Solar Cells

Abstract Small molecule solar cells (SMSCs) lag a long way behind polymer solar cells. A key limit is the less controllable morphology of small molecule materials, which can be aggravated when incorporating anisotropic nonfullerene acceptors. To fine‐tune the blending morphology within SMSCs, a π‐conjunction curtailing design is applied, which produces a efficient benzodithionopyran‐cored molecular acceptor for nonfullerene SMSCs (NF‐SMSCs). When blended with a molecular donor BDT3TR‐SF to fabricate NF‐SMSCs, the π‐conjunction curtailed molecular acceptor NBDTP‐M obtains an optimal power conversion efficiency (PCE) of up to 10.23%, which is much higher than that of NBDTTP‐M of longer π‐conjunction. It retains 93% of the PCE of devices fabricated in a glove box when all spin‐coating and post‐treating procedures are conducted in ambient air with relative humidity of 25%, which suggests the good air‐processing capability of π‐conjunction curtailed molecules. Detailed X‐ray scattering investigations indicate that the BDT3TR‐SF:NBDTP‐M blend exhibits a blend morphology featuring fine interpenetrating networks with smaller domains and higher phase purity, which results in more efficient charge generation, more balanced charge transport, and less recombination compared to the low‐performance BDT3TR‐SF:NBDTTP‐M blend. This work provides a guideline for molecular acceptors' design toward efficient, low‐cost, air‐processed NF‐SMSCs.

Wu, Hao↗
DOE OSTI2022

Energy Efficient Material Processing through Automated Process Monitoring and Controls

Smart manufacturing is bound to play a crucial role in reducing global energy consumption while accelerating economic development. This phenomenon is evident in advanced sensing technology developments, data analytics and machine learning/AI, automated controls, cloud computing, etc. The immediate opportunity for smart manufacturing is to improve the energy efficiency of manufacturing operations through innovations in processes and controls. The manufacturing industry still consumes about 30% of total global energy production, which is significant. This program focused on the recommendation of heterogeneous sensors to monitor the Chemical Vapor Infiltration (CVI) process parameters, states, and key performance indices and utilize cloud computing to sort and analyze the real-time data. Learning through data analytics helps guide the control parameters affecting energy usage. The target process in the program is the CVI process at the Honeywell South Bend facility, which is one of the most energy-intensive manufacturing industries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

An Anisotropic Yield and Damage Material Model to Improve the Contact Pressure Analysis in a Biomass Shredding System

Size reduction systems used in biomass processing break biomass into smaller pieces by utilizing the kinetic energy from the sharp rotating blades. Abrasive and/or erosive wear caused by biomass comminution results in blade wear of the sharp edged cutters, deteriorating the process efficiency. Here, this study aims to optimize the blade design and improve the system efficiency by attempting to understand the interactions between the blades and biomass particles. Since real-time monitoring of these interactions is impractical during operation, mechanical simulations offer a viable alternative for investigating the shredding process. Yet, the irregular geometry and complex mechanical properties of biomass—such as the anisotropic nature of woodchips and their nonlinear fracture behavior—pose significant challenges for accurately simulating contact pressure. In this work an anisotropic yield material model, along with a damage initiation and evolution function, is applied to the woodchip particle to study the contact pressure on shredder blade, offering a scientific basis for improved blade design and process efficiency. This approach can be extended to other biomass processing systems with similar anisotropic feedstocks, making it a valuable tool for advancing sustainable biomass utilization.

09 - BIOMASS FUELS↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Crystallization in High-Level Waste Glass: A Review of Glass Theory and Noteworthy Literature

There is a fundamental need to continue research aimed at understanding nepheline and spinel crystal formation in high-level waste (HLW) glass. Specifically, the formation of nepheline solids (K/NaAlSiO 4 ) during slow cooling of HLW glass can reduce the chemical durability of the glass, which can cause a decrease in the overall durability of the glass waste form. The accumulation of spinel solids ((Fe, Ni, Mn, Zn)(Fe, Cr) 2 O 4 ), while not detrimental to glass durability, can cause an array of processing problems inside HLW glass melters. In this review, the fundamental differences between glass and solid-crystals are explained using kinetic, thermodynamic, and viscosity arguments, and several highlights of glass-crystallization research, as it pertains to high-level waste vitrification, are described. In terms of mitigating spinel in the melter and both spinel and nepheline formation in the canister, the complexity of HLW glass and the intricate interplay between thermal, chemical, and kinetic factors further complicates this understanding. However, new experiments seeking to elucidate the contributing factors of crystal nucleation and growth in waste glass, and the compilation of data from older experiments, may go a long way towards helping to achieve higher waste loadings while developing more efficient processing strategies. Higher waste loadings and more efficient processing strategies will reduce the overall HLW Hanford Tank Waste Treatment and Immobilization Plant (WTP) vitrification facilities mission life.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Crystallization in High-Level Waste Glass: A Review of Glass Theory and Noteworthy Literature

There is a fundamental need to continue research aimed at understanding nepheline and spinel crystal formation in high-level waste (HLW) glass. Specifically, the formation of nepheline solids (K/NaAlSiO 4 ) during slow cooling of HLW glass can reduce the chemical durability of the glass, which can cause a decrease in the overall durability of the glass waste form. The accumulation of spinel solids ((Fe, Ni, Mn, Zn)(Fe,Cr) 2 O 4 ), while not detrimental to glass durability, can cause an array of processing problems inside of HLW glass melters. In this review, the fundamental differences between glass and solid-crystals are explained using kinetic, thermodynamic, and viscosity arguments, and several highlights of glass-crystallization research, as it pertains to high-level waste vitrification, are described. In terms of mitigating spinel in the melter and both spinel and nepheline formation in the canister, the complexity of HLW glass and the intricate interplay between thermal, chemical, and kinetic factors further complicates this understanding. However, new experiments seeking to elucidate the contributing factors of crystal nucleation and growth in waste glass, and the compilation of data from older experiments, may go a long way towards helping to achieve higher waste loadings while developing more efficient processing strategies. Higher waste loadings and more efficient processing strategies will reduce the overall HLW Hanford Tank Waste Treatment and Immobilization Plant (WTP) vitrification facilities mission life.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

A Step toward Process Electrification: Microwave-Assisted Co-Gasification of Waste Plastics and Biomass to Produce Clean Energy

Barely 9% of the 353 million metric tons of plastic waste produced worldwide over the last two decades has been recycled, with the majority of it being either landfilled or incinerated. Recycling and upcycling plastic waste is thus essential in mitigating environmental pollution. Gasification is the thermochemical conversion of solid carbon feedstock at high temperatures using a gasifying agent to produce hydrogen-rich syngas. Existing gasification technologies are energy-intensive and require high operating temperatures (>1000 °C), leaving them uncommercial. Microwave heating has sparked attention in a variety of chemical processes due to its specific advantages over traditional heating technologies, such as rapid and selective heating to improve process efficiency. NETL’s research team recently investigated the synergistic advantages of corn stover and mixed plastics in producing hydrogen-rich syngas via microwave heating. As a continuation of the research, this study provides an outlook over the non-catalytic versus catalytic effects of plastic-corn stover gasification via process electrification to enhance H2 production, minimize undesired tar, and improve process efficiency. Our aim is to provide complete electrification of the existing gasification process by using waste plastics as a starting block to produce valuable fuel and support the nation’s net zero goals.

Abedin, Ashraf↗
DOE OSTI2019

Tailor-made trees: engineering lignin for ease of processing and tomorrow’s bioeconomy

Lignocellulosic biomass represents an abundant source of cellulosic fibres and fermentable sugars. Yet, lignin, a polyphenolic constituent of secondary-thickened plant cell walls significantly contributes to biomass recalcitrance during industrial processing. Efforts to reduce plant total lignin content through genetic engineering have improved processing efficiency, but often incur an agronomic penalty. Alternatively, modifications that alter the composition of lignin and/or its interaction with other cell wall polymers display improved processing efficiency without compromising biomass yield. We propose that future efforts to improve woody feedstocks should focus on altering lignin composition and cell wall ultrastructure. Here, we describe potential future modifications to lignin and/or other cell wall characteristics that may serve as strategic targets in the production of trees that are tailor-made for specific pretreatments and end-product applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR) for learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS- SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can be properly distinguished, thus a robust modeling result can be achieved. Finally, a novel multi- objective evaluation index on modeling performance is developed by comprehensively considering the root mean square error of modeling and the correlation coefficient on trend fitting, based on which the NSGA-II algorithm is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently, which indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

blast cavities, Control↗
NASA NTRS1998

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

Maly, K.↗
DOE OSTI2024

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

09 BIOMASS FUELS↗
DOE OSTI2020

Eco-friendly, solution-processable and efficient low-energy lighting phosphors: copper halide based hybrid semiconductors Cu 4 X 6 (L) 2 (X = Br, I) composed of covalent, ionic and coordinate bonds

A series of copper halide based inorganic–organic hybrid semiconductors have been synthesized. Structural analysis confirms that all compounds are composed of one-dimensional Cu 4 X 6 2- anionic chains that coordinate to cationic ligands via Cu–N dative bonds. Different coordination affinities of the ligands lead to two types of ligand arrangements with various structural distortions. All compounds are highly resistant to heat and moisture as a result of the combination of coordinate and ionic bonds. Low energy emission with high efficiency is achieved for these compounds and the emission energy (~552–615 nm) and color (yellow-orange) can be tuned by varying the ligand and halogen element. The electronic structure and luminescence mechanism are examined by both experimental and theoretical methods. More importantly, all compounds demonstrate good solubility in polar aprotic solvents, a desired property that is absent in all other CuX hybrid families of extended structures, which is attributed primarily to the ionic nature of this material class. Furthermore, the good solution-processability, and cost effective and easily scalable synthesis coupled with high quantum efficiencies and framework stability make these hybrid materials promising phosphors for general lighting applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Computed Tomography and Thermography Increases CMC Material and Process Development Efficiency and Testing Effectiveness

Nondestructive characterization techniques have been used to steer development and testing of CMCs. Computed tomography is used to determine the volumetric integrity of the CMC plates and components. Thermography is used to determine the near surface integrity of the CMC plates and components. For process and material development, information such as density uniformity, part delamination, and dimensional tolerance conformity is generated. The information from the thermography and computed tomography is correlated and then specimen cutting maps are superimposed on the thermography images. This enables for tighter data and potential explanation of off nominal test data. Examples of nondestructive characterization utilization to make decisions in process and material development and testing are presented.

Effinger, Michael↗
DOE OSTI2013

Impact of Mixed Feedstocks and Feedstock Densification on Ionic Liquid Pretreatment Efficiency

Background: Lignocellulosic biorefineries must be able to efficiently process the regional feedstocks that are available at cost-competitive prices year round. These feedstocks typically have low energy densities and vary significantly in composition. One potential solution to these issues is blending and/or densifying the feedstocks in order to create a uniform feedstock. Results/discussion: We have mixed four feedstocks - switchgrass, lodgepole pine, corn stover, and eucalyptus - in flour and pellet form and processed them using the ionic liquid 1-ethyl-3-methylimidazolium acetate. Sugar yields from both the mixed flour and pelletized feedstocks reach 90% within 24 hours of saccharification. Conclusions: Mixed feedstocks, in either flour or pellet form, are efficiently processed using this pretreatment process, and demonstrate that this approach has significant potential.

09 BIOMASS FUELS↗
NASA NTRS2012

Application of System Operational Effectiveness Methodology to Space Launch Vehicle Development and Operations

The Department of Defense (DoD) defined System Operational Effectiveness (SOE) model provides an exceptional framework for an affordable approach to the development and operation of space launch vehicles and their supporting infrastructure. The SOE model provides a focal point from which to direct and measure technical effectiveness and process efficiencies of space launch vehicles. The application of the SOE model to a space launch vehicle's development and operation effort leads to very specific approaches and measures that require consideration during the design phase. This paper provides a mapping of the SOE model to the development of space launch vehicles for human exploration by addressing the SOE model key points of measurement including System Performance, System Availability, Technical Effectiveness, Process Efficiency, System Effectiveness, Life Cycle Cost, and Affordable Operational Effectiveness. In addition, the application of the SOE model to the launch vehicle development process is defined providing the unique aspects of space launch vehicle production and operations in lieu of the traditional broader SOE context that examines large quantities of fielded systems. The tailoring and application of the SOE model to space launch vehicles provides some key insights into the operational design drivers, capability phasing, and operational support systems.

Watson, Michael D.↗