SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Despite their relatively low efficiency, thermoelectric generators are used in a limited number of industrial applications where they are preferred to other energy conversion devices because of their high reliability, low maintenance and long life, in particular when considering harsh environments.
The U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, and Advanced Manufacturing Office (AMO) hosted the TRANSSFORM (Transformative, Resilient, Adaptive, Nimble, Sustainable, Smart, Flexible, Optimal, Robust, and Model-based1 – A Bold Vision for the Future of U.S. Manufacturing) Workshop on September 8–10, 2021. The objective of the workshop was to hear from stakeholders about how manufacturing digitalization can improve manufacturing competitiveness and reduce energy consumption and emissions production from U.S. manufacturing, and what research, development, and demonstrations are needed to have the desired impact.
Controlling ion desolvation, transport, and charge transfer at the electrode-electrolyte interface (EEI) is critical to enabling the rational design of efficient and selective separation of targeted heavy metals and decontamination of industrial waste water. One challenge is to sufficiently resolve and interrogate the intermediate transformation steps between solvated metal cations and bare cations for electroreduction at the EEI and establish pathways to modulate these steps to achieve efficient energy transfer for targeted reactive separations. Herein, we obtained a predictive understanding of the effect of adding a hydrophobic ionic liquid, 1-ethyl-3-methylimidazolium chloride (EMIMCl), to an aqueous electrolyte on modulating the desolvation and electrosorption of Pb 2+ cations using a combination of experimental and theoretical techniques. Here, we reveal formation of a compact interphase layer consisting of EMIMCl-Pb complex clusters under an applied electric field using operando electrochemical Raman spectroscopy, atomic force microscopy, and electrochemical impedance spectroscopy measurements combined with classical molecular dynamics simulations. The application of a larger negative potential is shown to result in formation of a well oriented layer with the positive imidazolium ring of the EMIMCl lying on the electrode and the long hydrophobic alkyl chain extending into the bulk electrolyte. This oriented layer is demonstrated to facilitate desolvation of incoming solvated Pb 2+ cations and decrease the charge transfer resistance for Pb electrodeposition, which has important implications for selective removal of Pb from contaminated mixtures. Overall, our studies open up new opportunities to modulate ion desolvation using hydrophobic ionic liquids in aqueous electrolytes for efficient heavy metal separation.
As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations. Altogether, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.
In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.
Digital twins (DTs) in complex industrial and engineering applications have potential benefits that include increased operational efficiencies, enhanced safety and reliability, improved security engineering, reduced errors, faster information sharing, and better predictions. The interest in DT technologies continues to grow, and many of these advanced technologies are expected to experience rapid and wide industry adoption in the near future. Some of the potential application areas for DTs in the nuclear industry are design, licensing, plant construction, training simulators, predictive operations and maintenance, autonomous operation and control, failure and degradation prediction, physical protection modeling and simulation, and safety and reliability analyses. The Office of Nuclear Regulatory Research at the U.S. Nuclear Regulatory Commission (NRC) has initiated a future-focused research project to assess the regulatory viability of DTs for nuclear power plants and other NRC-regulated activities, such as fuel cycle facilities and operations. This report explores the potential impact of DT technologies in nuclear applications on NRC-regulated activities of interest. This report describes a nuclear DT system and its capabilities for nuclear power plant applications, followed by identification and discussion of some regulated activities that merit special consideration and present opportunities in implementing DT-enabling technologies and capabilities.
Historically, heavy-duty off-road equipment has prioritized performance, reliability, and cost, with limited emphasis on efficiency. While increasing carbon dioxide (CO 2 ) regulations have driven a focused effort for efficiency in on-road vehicles, off-road machines have remained largely overlooked, leaving significant opportunities for efficiency improvements and emissions reductions. To address this overlooked industry, Michigan Technological University’s Advanced Power Systems Research Center (APS LABS) worked with Pettibone to determine a unique electrified architecture for Pettibone’s heavy-duty off-road material handler, the Cary-Lift 204i, with a goal of 20% fuel savings. The base Cary-Lift uses a diesel engine with no electrification.
Stable and efficient X-ray scintillators are crucial for medical diagnostics, industrial, and defense applications. However, conventional scintillator technologies face a trade-off between stability, optimal performance, and sustainability. Herein, we introduce 3D-printed Cs 4 MnBi 2 Cl 12 (Pero1) and Cs 4 Cd 0.68 Mn 0.32 Bi 2 Cl 12 (Pero2) perovskite microcrystals embedded within a polylactic acid (PLA) polymer composite as X-ray scintillators, combining efficiency, stability, and sustainability. The orange luminescent perovskite powder phosphors exhibited poor water stability, which was successfully addressed through incorporation into PLA via filament extrusion and fused deposition modeling (FDM) 3D printing. The resulting composite films demonstrated remarkable water stability while maintaining uniform orange emission throughout the polymer matrix, as confirmed by 3D topography scanning and X-ray fluorescence mapping. Structural characterization revealed minimal chemical interaction between the perovskite and PLA matrix, with the composites retaining their crystalline properties. The PLA-Pero2 composite exhibited superior optical properties, with a photoluminescence quantum yield of 47%, nearly 17 times higher than that of PLA-Pero1 (2.8%), attributed to the effective suppression of non-radiative decay pathways through Cd 2+ doping. Under hard X-ray irradiation at synchrotron beamlines, both composites exhibited excellent radioluminescence, with emission peaks at 605 nm, a linear response across a wide X-ray flux range, and remarkable radiation stability, showing less than 3% intensity degradation after 600 seconds of continuous high-dose exposure. The PLA-Pero2 composite achieved a spatial resolution of 5 line pairs per millimeter and a contrast ratio of 0.255. These performance metrics, combined with the polymer's biodegradability and scalability through additive manufacturing, position PLA-based composites as a more sustainable alternative to conventional petroleum-based polymer scintillators for next-generation medical imaging, radiation monitoring, and industrial radiography applications.
NREL's team of world-leading experts in III-V technologies develops advanced multijunction cell technologies and transfers the resulting intellectual property to industry.
The economic viability of microalgal-derived products relies on rapid CO 2 transfer in a cost-effective manner. Many industrial gas streams contain concentrated CO 2 that, if converted to useful products, would lower greenhouse gas emissions and valorize the wasted CO 2 . Membrane carbonation (MC) uses non-porous hollow-fiber gas-transfer membranes to deliver CO 2 without bubble formation, which makes it possible to achieve a high carbon-transfer efficiency (CTE). However, inert gasses in the industrial streams (e.g., N 2 , O 2 , and H 2 O) can significantly lower the CO 2 -delivery rate. The means to overcome the buildup of inert gases in the membrane lumen is to manage the distal end of the membranes to sweep out inert gases while not wasting significant CO 2 . A MC-venting strategy was evaluated for CO 2 inputs from 5% to 100%. Abiotic tests using a restricted exit flow could achieve >95% CTE abiotic for industrial CO 2 streams. When integrated with semi-continuous cultivation of a marine coccolithophore, Emiliania huxleyi , CO 2 delivery and venting were on-demand based on a pH set points and pH-actuated feed and venting valves. MC using the venting strategy achieved 100% CTE biotic when delivering 100% and 50% CO 2 , which was better than 50% CTE biotic obtained from pH-controlled sparging of 100% CO 2 -sparging. E. huxleyi consistently fixed ~80% of the delivered CO 2 into biomass, and the remaining ~20% to calcite coccoliths. Finally, the compact size of MC modules, stable pH control, and no shear forces from bubble agitation during the CO 2 delivery made MC an ideal match for cultivation of coccolithophores, which are sensitive to shear forces and pH fluctuations.
High-temperature heat pumps are a prospective technology for electrifying and decarbonizing industrial drying processes. This study investigates the technological potential of HTHPs in replacing fossil-fuel burners or steam boilers in industrial spray dryers. The performance of HTHPs with three configurations was investigated, including high high-temperature heat pump boosted with an electric air heater, a steam-generating high-temperature heat pump with multi-stage mechanical vapor compressions, and transcritical high-temperature heat pumps. R1336mzz(Z) is used as the refrigerant in both subcritical and transcritical high-temperature heat pumps, and R717 is used as the working fluid in the mechanical vapor recompression and the heat transfer fluids. Thermodynamic models are built to evaluate the energy efficiency of HTHPs. A case study was carried out on an industrial spray dryer integrated with a waste heat recuperator, where the HTHPs are used to recover precooled air with a dew point of 40℃ and supply heat for drying air at 200℃. For a high-temperature heat pump boosted with an electric air heater, a higher system-level COP is achieved with a high supply temperature from high-temperature heat pump. For a high-temperature heat pump with mechanical vapor recompressions, larger temperature drops in the flashing tank provide higher heat capacities but almost constant values of the coefficient of performance. For a transcritical high-temperature heat pump, an optimum discharge pressure exists for the temperature profiles within the gas cooler and the coefficient of performance. The transcritical high-temperature heat pump provides the best energy efficiency among the three configurations. The theoretical results prove the technical feasibility of high-temperature heat pumps in industrial air-drying applications. .
This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.
The recent development of hydride vapor phase epitaxy (HVPE) is potentially promising as a route to lower the cost of high-efficiency III-V solar cells for space applications. HVPE produces the same materials and device structures as the industry-standard organometallic vapor phase epitaxy (OMVPE) process, and although HVPE has shown promising device efficiencies, it lags the OMVPE growth technique in technical maturity. For example, there are no reports of the performance of HVPE-grown devices in radiation environments. There is an expectation that high-quality (that is, single crystal and low-defect) III-V materials will behave similarly regardless of the growth method. However, it is important to verify assumptions as facts. In this project, we produced materials and devices using both HVPE and OMVPE at NREL and irradiated them using 1 MeV electrons to ascertain the effect of radiation on the materials. We also performed initial work on producing more radiation-hard structures, including devices with the pn junction at the front of the device, and devices with a graded doping profile, both of which help with radiation tolerance. Measurements of the devices post radiation exposure showed that the open-circuit voltage (V OC ) of the HVPE-grown solar cells degraded least on a percent basis, although they also started from a lower baseline than the OMVPE-grown devices. All solar cells reached approximately the same V OC after irradiation, indicating that the dose used was sufficient to degrade all device equally. The short-circuit current in the HVPE devices did degrade more than the other solar cells, and this was attributed to a higher-than-expected doping density in the base layer of that cell. The results of these experiments, while by no means comprehensive, do not show any material difference in the radiation effects in OMVPE- and HVPE-grown materials and devices.
Microgravity research at NASA has been an undertaking that has included both science and commercial approaches since the late 80s and early 90s. The Fluid Physics and Transport Phenomena community has been developed, through NASA's science grants, into a valuable base of expertise in microgravity science. This was achieved through both ground and flight scientific research. Commercial microgravity research has been primarily promoted thorough NASA sponsored Centers for Space Commercialization which develop cost sharing partnerships with industry. As an example, the Center for Advanced Microgravity Materials Processing (CAMMP)at Northeastern University has been working with cost sharing industry partners in developing Zeolites and zeo-type materials as an efficient storage medium for hydrogen fuel. Greater commercial interest is emerging. The U.S. Congress has passed the Commercial Space Act of 1998 to encourage the development of a commercial space industry in the United States. The Act has provisions for the commercialization of the International Space Station (ISS). Increased efforts have been made by NASA to enable industrial ventures on-board the ISS. A Web site has been established at http://commercial/nasa/gov which includes two important special announcements. One is an open request for entrepreneurial offers related to the commercial development and use of the ISS. The second is a price structure and schedule for U.S. resources and accommodations. The purpose of the presentation is to make the Fluid Physics and Transport Phenomena community, which understands the importance of microgravity experimentation, aware of important aspects of ISS commercial development. It is a desire that this awareness will be translated into a recognition of Fluid Physics and Transport Phenomena application opportunities coordinated through the broad contacts of this community with industry.
The Ultra-Efficient Engine Technology (UEET) Program includes seven key projects that work with industry to develop and hand off revolutionary propulsion technologies that will enable future-generation vehicles over a wide range of flight speeds. A new program office, the Ultra-Efficient Engine Technology (UEET) Program Office, was formed at the NASA Glenn Research Center to manage an important National propulsion program for NASA. The Glenn-managed UEET Program, which began on October 1, 1999, includes participation from three other NASA centers (Ames, Goddard, and Langley), as well as five engine companies (GE Aircraft Engines, Pratt & Whitney, Honeywell, Allison/Rolls Royce, and Williams International) and two airplane manufacturers (the Boeing Company and Lockheed Martin Corporation). This 6-year, nearly $300 million program will address local air-quality concerns by developing technologies to significantly reduce nitrogen oxide (NOx) emissions. In addition, it will provide critical propulsion technologies to dramatically increase performance as measured in fuel burn reduction that will enable reductions of carbon dioxide (CO2) emissions. This is necessary to address the potential climate impact of long-term aviation growth.
Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.
III-nitrides have fomented a revolution in the lighting industry and are poised to make a huge impact in the field of power electronics. In the III-nitride family, the crystal growth and use of hexagonal BN (h-BN) as an ultrawide bandgap (UWBG) semiconductor are much less developed. Bulk crystals of h-BN produced by the high-temperature/high-pressure and the metal flux solution methods possess very high crystalline and optical qualities but are impractical to serve as substrates or for device implementation as their sizes are typically in millimeters. The development of crystal growth technologies for producing thick epitaxial films (or quasi-bulk or semi-bulk crystals) in large wafer sizes with high crystalline quality is a prerequisite for utilizing h-BN as an UWBG electronic material. Compared to traditional III-nitrides, BN has another unique application as solid-state neutron detectors, which however, also require the development of quasi-bulk crystals to provide high detection efficiencies because the theoretical efficiency (ηi) relates to the detector thickness (d) by ηi=1−e−dλ, where λ denotes the thermal neutron absorption length which is 47 μm (237 μm) for 10B-enriched (natural) h-BN. We provide an overview and recent progress toward the development of h-BN quasi-bulk crystals via hydride vapor phase epitaxy (HVPE) growth and the attainment of thermal neutron detectors based on 100 μm thick 10B-enriched h-BN with a record efficiency of 60%. The thermal neutron detection efficiency was shown to enhance at elevated temperatures. Benchmarking the crystalline and optical qualities of h-BN quasi-bulk crystals with the state-of-the-art mm-sized bulk crystal flakes and 0.5 μm thick epitaxial films identified that reducing the density of native defects such as vacancies remains the most critical task for h-BN quasi-bulk crystal growth by HVPE.
Geothermal reservoir characterization, construction, and operations are technology-intensive activities that contribute significantly to the cost of delivering renewable electricity. The technologies involved, such as downhole tools and drilling equipment, are similar to those used in oil and gas exploration and production must often be adapted for use in the corrosive, high-temperature geothermal reservoir environment. Low production volume of geothermal tools presents a major challenge in meeting the industry's technology needs. Production of specialized tools for geothermal subsurface applications is often cost-prohibitive. Reduced inventory of subsurface well construction, characterization, and production tools causes geothermal reservoir development efficiency and sophistication to lag behind that of the oil and gas industry. Advances in additive manufacturing provide opportunities to advance geothermal technology while reducing lead time and costs associated with production of low-volume, complex parts. Additionally, this paper performs an initial techno-economic analysis comparing the cost of conventional production techniques and additive manufacturing for geothermal downhole applications. An analysis of representative downhole tools is used to create a framework for estimating fabrication costs of subtractive and additive techniques, including post-print machining required to meet final tolerances. The framework is used to explore several manufacturing scenarios and identify the dominant factors driving manufacturing time and cost. The current feasibility of additive manufacturing for geothermal downhole tool applications is assessed and issues for future development to better meet the needs of the geothermal industry are identified.