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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Escape Ratio Contributes More Than Fluorescence Yield to SIF-GPP Relationship Over Crops and Rainforest

Solar-induced chlorophyll fluorescence (SIF) is an effective indicator to track the gross primary productivity (GPP). However, there is still a lack of a clear understanding for the contribution of physiological and structural factors to the SIF–GPP relationship at the canopy scale. To quantify the influence of different SIF components, particularly the photon escape ratio ($f$ esc ) and fluorescence yield (Φ F ), on the SIF–GPP relationship, this study evaluated the performance of various vegetation indices (VIs), SIF components, light use efficiency (LUE), and GPP over two typical biomes (crops and rainforest), using a range of satellite remote sensing products. In August of each year from 2018 to 2020, both SIF and GPP over United States (U.S.) Corn Belt are higher than those over the Amazon rainforest, attributed to the consistent pattern of higher $f$ esc and LUE over crops than over rainforest, as well as Φ F . Furthermore, the structural signals represented by $f$ esc (R = 0.41–0.64) can better capture the LUE variations than Φ F (R = 0.10–0.30) for each biome. In conclusion, this study highlights that $f$ esc , determined by canopy structure, has great potential to capture LUE and GPP changes within and across biomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Perspectives on Machine Learning-assisted Plasma Medicine: Towards Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma-surface interactions that can span across vastly different length-and time-scales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: (i) data-driven modeling of hard-to-model plasma-surface interactions and plasma treatment outcomes; (ii) learning data analytics for plasma and surface diagnostics in real-time; and (iii) developing predictive controllers that enable reliable and effective CAP treatments. Furthermore, this paper discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

electricity distribution market↗
DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗
DOE OSTI2023

Circular plastics technologies: pyrolysis of plastics to fuels and chemicals

Pyrolysis technologies are a staple in plastic chemical recycling because of the robustness to contamination and existing infrastructure. Pyrolysis is already considered to be a reasonably mature technology with numerous pilot plants operating to pyrolyze plastic waste into fuels and chemicals. Here this chapter will describe the pyrolysis process and important process parameters, the types of plastics that are suitable for pyrolysis recycling, the mechanism of pyrolytic degradation of various plastics, the products derived from different plastics, companies that have successfully scaled pyrolysis recycling, and recent innovations in the technology.

09 BIOMASS FUELS↗
DOE OSTI2020

Multi-Mechanism Flood Hazard Assessment: Critical Review of Current Practice and Approaches

This report documents the initial findings from the Nuclear Regulatory commission (NRC)-sponsored research project Methods for Estimating Joint Probabilities of Coincident and Correlated Flooding Mechanisms for Nuclear Power Plant Flood Hazard Assessments.1 This research project is a part of NRC’s Probabilistic Flood Hazard Assessment (PFHA) Research Program and will aid the development of guidance on the use of PFHA methods to evaluate infrastructure safety for existing and proposed US nuclear power plants (NPPs). More specifically, this project intends to provide technical background for the development of flood hazard curves for multi-mechanism floods (MMFs). MMFs are flood events caused by more than one flooding mechanism (e.g., flood events due to the simultaneous occurrence of precipitation-induced river flooding and storm surge). Project activities include three main tasks: Task 1—Survey of current concepts and methods in assessing MMF hazards; Task 2—Critical assessment of selected methods and approaches for quantifying probabilistic MMF hazard risk; Task 3—Development of example case studies to illustrate best practices for quantifying probabilistic MMF hazard risk The initial findings from Tasks 1 and 2 are documented in this report. Task 1 comprised a survey of approaches and methods that have been applied to understand and assess flood hazards due to MMFs. Task 2 involved a critical review of the selected approaches and methods. To that end, the scope of this report includes documentation of (1) a reconnaissance-level survey of the current state of concepts and practice for MMF hazard assessment; (2) a generalized MMF assessment framework to address the distinctions among various types of flood-forcing phenomena, flood mechanisms (grouped into three mechanism types), and flood severity metrics; (3) a wide-ranging survey of approaches and methods that have been applied to various flooding phenomena and settings; and (4) a critical assessment of MMF hazard assessment methods. Studies were identified involving MMFs related to coastal flooding mechanisms, fluvial (rivers/streams) flooding mechanisms, and associated combinations of coastal and fluvial flooding mechanisms. Studies were also identified that address MMFs involving coastal and fluvial flooding mechanisms as well as coastal flooding mechanisms along with extreme precipitation (without specific attribution to fluvial or pluvial mechanisms). The studies identified for review in this report included assessments at varying spatial scales (from local to global) with differing geographic regions of focus using both observed and synthetic data. The majority of studies identified and reviewed were site-specific assessments focusing on relatively short return periods. Studies considered a range of flood severity metrics, made differing assumptions regarding the occurrence of extrema, and used multiple statistical techniques; the use of copulas for the development of joint distributions was a particularly popular analysis technique. The literature review highlighted the differences among existing studies relative to terminology used, means of presenting results, framework and techniques employed, and level of sophistication regarding the number and types of variables considered. Despite the significant diversity in existing studies, the review identified several promising techniques that will be considered in future work under this project, including the development of joint distributions for MMFs using copula and Bayesian-motivated approaches.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Efficient and low-cost hydrogen production by wind power driven water electrolysis

The document describes and discusses the results of a techno-economic analysis for wind driven electrolysis systems. The costs obtained for different plants, including onshore and offshore locations and different plant sizes, are discussed. Large scale wind electrolysis plants show high potential to achieve the target of 2 $/kg. Initial results of experimental tests for a single cell electrolyzer, to be coupled directly with a wind turbine, are also presented and discussed.

08 HYDROGEN↗
DOE OSTI2022

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Princeton University (Abstract)

The National Energy Technology Laboratory (NETL) and Princeton University will collaborate in the development of microwave-plasma technology to convert methane into hydrogen and valuable solid carbon materials. The effort will combine NETL’s vast resources for microwave-enhanced process development, characterization, and design with Princeton University’s extensive expertise in the study of non-equilibrium plasmas employing supersonic nozzles. Lab-scale experiments will be conducted for different conditions to convert and compare their respective performance in hydrogen production. Computational modeling will be performed to verify results from experimental studies conducted in both labs.

08 HYDROGEN↗
DOE OSTI2023

Updates to Hydrogen Market Potential [Slides]

This short presentation provides an update to the 45 minute presentation given to the University of Houston Hydrogen Economy course on August 31, 2022. Since that time, the U.S. National Clean Hydrogen Strategy and Roadmap has been released and its hydrogen demand estimates differ from those in the H2@Scale Technical and Economic Potential Report - presented in 2022. This presentation provides estimates from the Clean Hydrogen Strategy and Roadmap.

08 HYDROGEN↗
DOE OSTI2024

Low-cost Manufacturing of Semitransparent CdTe PV for Building Integration

Solar has been demonstrated to be a robust renewable energy source, constituting a significant portion of the United States’ renewable energy portfolio. Despite its growth across residential, commercial, and utility sectors over the past decades, it remains a small fraction of the overall energy infrastructure. Challenges persist in fully harnessing solar power to meet the nation's escalating energy demands. Among these challenges lies the hurdle of efficiently distributing large quantities of solar-generated electricity to densely populated regions with the highest energy needs and costs. Traditional utility-scale arrays demand extensive land, a luxury often unavailable in metropolitan areas. Consequently, installations must be situated at a distance, necessitating additional infrastructure for electricity transmission to service areas. While metropolitan landscapes lack sprawling open spaces suitable for conventional utility-scale solar deployment, they offer a different resource: windows. Semitransparent photovoltaic window technology has the potential to not only bolster the grid's energy capacity, but to also provide HVAC and economic advantages to building owners. However, commercial availability of building-integrated photovoltaic windows remains limited. Silicon based photovoltaics currently dominate the solar market but adapting them for use in windows poses a variety of engineering and economic challenges such as relatively low power density, high costs associated with custom manufacturing, and aesthetic considerations. Addressing these challenges, this project explored the use of laser ablation patterning to manufacture cost-effective, high-efficiency, semitransparent Cadmium Telluride photovoltaic modules. Results showcased the potential of this methodology in developing photovoltaic windows and other innovative semitransparent PV applications. The ablation manufacturing technique demonstrated great versatility in achieving different patterns and levels of visible light transmission, and the power loss due to ablation was nearly directly proportional to the amount of material removed. Furthermore, the manufacturing process for Cadmium Telluride modules already has established advantages in material and energy efficiency, and the conversion of a standard submodule to semitransparent essentially requires a single additional process step, ensuring scalability. It is important to note that during this work, Toledo Solar experienced substantial organizational upheaval stemming from a lawsuit with First Solar. An external investigation led the board of directors to remove and replace the previous management team, and several other members of the staff elected to depart as well, including the then acting Principle Investigator on this project. The remaining Toledo Solar team attempted to recover from the disruption and deliver on the remaining tasks, but upon its own review, the Department of Energy ruled the project in default and terminated the contract in December 2023.

14 SOLAR ENERGY↗
DOE OSTI2025

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Paintable Reflective Coatings (CRADA Final Report)

As part of the Cyclotron Road LEEP, the Cypris Materials project team conducted research on the viability of a new reflective coating technology based on a novel advanced materials platform. Building off of nearly a decade of academic research spanning multiple institutions, research efforts were designed to tackle the familiar problem of maturing a project into a minimum viable product. Fundamental technologies spawned from academic research are fraught with both scientific and technical risk, as the barriers to a successful peer reviewed publication are much lower than a commercial product with a paying customer base. It is common, especially when maturing chemical or material innovations, to encounter a number of critical assumptions or features that support the performance of the technology in an unsustainable fashion. At the outset of the project, the platform technology of Cypris Materials was no different. Specific research thrusts regarding scale-up, process optimization, technoeconomic modeling, market specific weathering performance, and market specific optical performance were tackled during the CRADA project.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microbial Diversity Associated with the Pollen Stores of Captive-Bred Bumble Bee Colonies

The pollen stores of bumble bees host diverse microbiota that influence overall colony fitness. Yet, the taxonomic identity of these symbiotic microbes is relatively unknown. In this descriptive study, we characterized the microbial community of pollen provisions within captive-bred bumble bee hives obtained from two commercial suppliers located in North America. Findings from 16S rRNA and ITS gene-based analyses revealed that pollen provisions from the captive-bred hives shared several microbial taxa that have been previously detected among wild populations. While diverse microbes across phyla Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Ascomycota were detected in all commercial hives, significant differences were detected at finer-scale taxonomic resolution based on the supplier source. The causative agent of chalkbrood disease in honey bees, Ascosphaera apis, was detected in all hives obtained from one supplier source, although none of the hives showed symptoms of infection. The shared core microbiota across both commercial supplier sources consisted of two ubiquitous bee-associated groups, Lactobacillus and Wickerhamiella/Starmerella clade yeasts that potentially contribute to the beneficial function of the microbiome of bumble bee pollen provisions.

16S rRNA gene↗
DOE OSTI2022

Module-Fluidics: Building Blocks for Spatio-Temporal Microenvironment Control

Generating the desired solute concentration signal in micro-environments is vital to many applications ranging from micromixing to analyzing cellular response to a dynamic microenvironment. We propose a new modular design to generate targeted temporally varying concentration signals in microfluidic systems while minimizing perturbations to the flow field. The modularized design, here referred to as module-fluidics, similar in principle to interlocking toy bricks, is constructed from a combination of two building blocks and allows one to achieve versatility and flexibility in dynamically controlling input concentration. The building blocks are an oscillator and an integrator, and their combination enables the creation of controlled and complex concentration signals, with different user-defined time-scales. We show two basic connection patterns, in-series and in-parallel, to test the generation, integration, sampling and superposition of temporally-varying signals. All such signals can be fully characterized by analytic functions, in analogy with electric circuits, and allow one to perform design and optimization before fabrication. Such modularization offers a versatile and promising platform that allows one to create highly customizable time-dependent concentration inputs which can be targeted to the specific application of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Process-oriented evaluation of quasi-stationary Rossby waves and their impact on surface air temperature extremes in dynamical downscaling over North America

Quasi-stationary Rossby waves are a crucial component of the general circulation and play a significant role in regional water and energy cycles, as well as in extreme events. However, process-oriented evaluation for Rossby waves is rarely performed for dynamical downscaling simulations. To close this gap, we evaluate three classes of dynamical downscaling approaches, with a focus on quasi-stationary Rossby waves and their impact on surface air temperature over North America during Northern Hemisphere summer. The three classes of models differ in the way large-scale forcing is provided: a limited-area model (LAM) constrained only by lateral boundary conditions, represented by RegCM4 from the North American branch of the Coordinated Regional Downscaling Experiment (NA-CORDEX), a LAM with spectral nudging to maintain consistency in large-scale dynamics with the forcing data, represented by the Weather Research and Forecasting (WRF) model simulation in NA-CORDEX, and a global variable-resolution model with smoothly varying grid spacings, represented by the Community Atmosphere Model version 5.4, with the Model for Prediction Across Scales (MPAS) as its dynamical core (CAM-MPAS). With no constraints on the atmospheric dynamics, CAM-MPAS exhibits several mean biases in the upper-level circulations over the Pacific Coast region: a weaker subtropical jet, a northward-shifted mid-latitude jet, and an overestimated southerly flow. With the lateral boundary constraint alone, RegCM4 also exhibits weaker jets and overestimated southerly winds off the West Coast. Rossby ray theory reveals that those wind biases direct incoming Rossby waves northward. The erroneously routed Rossby waves distort the relationship between the accumulation of wave activity over the US West Coast and surface temperature anomalies over the Southern Great Plains, which emerges approximately 4 d after the convergence of wave-activity flux in the ERA-Interim reanalysis. Furthermore, the response of heatwaves to the extreme wave activity flux is not reproduced by the two models, a serious drawback as a dynamical downscaling framework is expected to connect large-scale forcing to local-scale phenomena. The WRF model employing spectral nudging is largely free from the aforementioned problems. A pair of sensitivity simulations suggests that spectral nudging is the key to improving the dynamics of quasi-stationary Rossby waves and their impact on surface air temperature. Our results also demonstrate the effectiveness of Rossby wave diagnostics that allow for realistic background flows for assessing the credibility of dynamical downscaling over North America, where incoming Rossby waves propagate through complex circulation patterns before traveling across the continent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

International collaboration in disposal research: comparative modeling of coupled processes in the DECOVALEX project

Abstract. This presentation gives an overview of an international research collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. The creation of the international DECOVALEX project, now running for more than 25 years, was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. DECOVALEX emphasizes joint analysis and comparative modeling of state-of-the-art field and laboratory experiments, across a range of host rock options and repository designs. Participating research teams are from radioactive waste management organizations, national research institutes, regulatory agencies, universities, and consulting groups, providing a wide range of perspectives and solutions to these complex problems. The presentation provides examples of the research contributions made collectively in past DECOVALEX tasks and also touches on the unique modeling challenges tackled in the ongoing project phase, referred to as DECOVALEX-2023. The current phase comprises 17 partner organizations, about 50 modeling teams, and 7 modeling tasks, which cover a broad portfolio from fundamental studies on gas migration to full-scale in situ heater experiments in different host rocks to performance assessment studies. Together, these examples illustrate that the insight and scientific knowledge gained within the DECOVALEX project would not have been possible if one group had studied these complex THMC modeling challenges alone rather than within a truly collaborative setting.

Birkholzer, Jens T.↗
DOE OSTI2022

Contech to Accelerate Cleantech: Seeding Emerging Innovation Programs for Construction Productivity and Energy Efficiency Integration; Preprint

Investments in U.S.-based start-ups that focus on advanced building construction technologies to increase construction productivity (contech) surged to approximately $3.1 billion in 2018 (as per Crunchbase data). More recently, emerging programs by government funding agencies, philanthropic foundations, and venture capitalists have been instrumental in supporting contech start-ups for innovations that increase productivity of energy efficiency integration and accelerate clean energy technologies (cleantech) for the buildings sector. These programs include R&D support and funding mechanisms for contech and cleantech. Traditionally, contech and cleantech are considered as two different innovation ecosystems. To enhance and scale up energy efficiency in buildings, creative programs that bring together contech and cleantech are critical. This paper provides a landscape assessment of the "contech-for-cleantech" innovation ecosystem in the U.S. and its impact in accelerating technology readiness and the development pipeline of "contech-for-cleantech". Technologies highlighted are robotics for retrofits, prefabrication of energy-efficient products, and advanced manufacturing of low-carbon net-zero buildings construction. Programs discussed include those led by (1) government funding agencies: American-Made Challenges with prizes like E-ROBOT for retrofits with robotics, (2) philanthropic foundations: Wells Fargo Innovation Incubator (IN2) that includes focus on energy efficiency and prefabrication, and (3) venture capitalists: Shadow Ventures Green Building Accelerator program which provides funding support to start-ups with ambitious plans for decarbonizing the built environment. This paper will also expand upon robust processes and criteria involved in judging and down-selection of start-ups through vetting and feedback from national lab researchers and industry experts in both cleantech and contech.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Use of Lignin-Based Admixture as Water Reducer (WRA) for Tailoring the Rheological Properties of Mortars for 3D-Printing

Efforts towards decarbonizing construction materials and industrial processes related to cement and concrete can be aided via multifaceted approaches that target alternative admixtures as well as precision control of fabrication. Chemical admixtures for water reduction have played a crucial role in the development of advanced mortar and concrete mixtures. Newer biomass processing techniques developed for aviation fuel production from corn stover biomass produce a highly reactive lignin byproduct that is suitable for chemical modifications to mimic the properties of commonly used polycarboxylate ethers (PCEs) with a smaller carbon footprint. These lignin-based plasticizers developed at NREL can be used in place of petrochemical-derived superplasticizers to tailor the rheological properties of cement-based systems for applications such as additive manufacturing while reducing the carbon intensity of the concrete mix. Lignosulfonates derived from paper pulping were historically used as water reducers only to be displaced with the rise of PCEs. The present study examines the use of a NREL produced lignin-based water reducing admixture (WRA), in cement pastes and mortar mixtures for 3D-printing at small- (mm) scale. The experimental program consisted of different formulations of oxidized lignin-based WRAs added in variable dosages to cement pastes with a fixed water-to-cement ratio. The objective is to achieve an appropriate workability, extrudability and buildability of mortar mixtures to produce 3D-printed specimens. The rheological characterization was performed to compare the initial yield stress and viscosity of the different mixtures with lignin-based admixture with respect to conventional PCE superplasticizers. The rheological characterization shows that the proposed material act as an effective water-reducer in cement systems, affecting the yield stress, plastic viscosity, and structuration rate of the mixtures evaluated. On the other hand, the effect on early hydration process of the cement pastes containing lignin-based admixtures is comparable to conventional superplasticizers.

3D-printing↗