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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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Defossilizing the chemical industry using air-to-chemical processes offers a promising solution to driving down the emission trajectory to net-zero by 2050.
The vast majority of global goods and materials we use on a daily basis are manufactured from fossil fuel precursors - chemical building blocks strung together using combinations of heat, pressure, and chemical catalysis. NREL, along with energy industry partners, is exploring the use of renewable, affordable electricity as the driving force for the conversion of low-energy molecules - such as water and carbon dioxide (CO2) - to generate other molecules that could be used as chemicals, materials, fuels, or energy storage.Through this research, NREL and its partners are working together to determine the most efficient and cost-effective solutions for energy storage as well as safer ways to create chemicals and plastics that do not rely solely on fossil fuels.
The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.
De novo genome assembly is a fundamental problem in the field of bioinformatics, that aims to assemble the DNA sequence of an unknown genome from numerous short DNA fragments (aka reads) obtained from it. With the advent of high-throughput sequencing technologies, billions of reads can be generated in a matter of hours, necessitating efficient parallelization of the assembly process. While multiple parallel solutions have been proposed in the past, conducting a large- scale assembly at scale remains a challenging problem because of the inherent complexities associated with data movement, and irregular access footprints of memory and I/O operations. In this paper, we present a novel algorithm, called PaKman, to address the problem of performing large-scale genome assemblies on a distributed memory parallel computer. Our approach focuses on improving performance through a combination of novel data structures and algorithmic strategies for reducing the communication and I/O footprint during the assembly process. A key aspect of our algorithm is its graph data structure, which comprises fat nodes (or what we call "macro-nodes") that reduce the communication burden during contig generation. We present an extensive performance and qualitative evaluation of our algorithm, including comparisons to other state-of-the-art parallel assemblers. Our results demonstrate the ability to achieve near-linear speedups on up to 8K cores (tested); outperform state-of-the-art distributed memory and shared memory tools in performance while delivering comparable (if not better) quality; and reduce time to solution significantly. For instance, PaKman is able to complete an assembly of the full human genome in just over a minute on 8K cores.
Electric utilities, regulators, and stakeholders face increasing risks of severe storms, freezes, floods, and heat waves damaging grid infrastructure and causing power outages—and increasing risks of utility equipment igniting wildfires. At the same time, customer electricity rates have risen substantially in recent years, due in part to replacing aging infrastructure and improving resilience to natural hazards and physical threats. To address these challenges, utilities are beginning to move beyond traditional, siloed planning processes to balance resilience with other fundamental grid objectives such as affordability, reliability, safety, and serving new loads. This study presents a framework for states and utilities that want to advance integration of resilience and distribution planning processes to improve planning efficiency, better prioritize cost-effective grid expenditures, and balance planning objectives. The framework includes 7 key integration points between these planning processes: -Strategy process -Data -Threat assessments -Solution identification and prioritization -Optimization opportunities -Consideration of other grid needs -Metrics Lawrence Berkeley National Laboratory reviewed utility distribution system plans and interviewed subject matter experts to identify emerging practices for each of the 7 integration points. This report presents these practices, which can be used as a guide toward more holistic planning and cohesive investment strategies. It also includes 3 case studies to provide practical examples of how utilities apply such integrated planning processes: two pole hardening programs and one microgrid planning effort. The report concludes by identifying opportunities for future research.
Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).
Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks, and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work, we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The developed transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows to efficiently simulate flow through fracture networks with simple cubic structure and arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize fracture networks in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable fluid and geometrical parameter ranges, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).
The MC 2 -3 code is a multigroup cross section generation code for fast reactor applications, developed by Argonne National Laboratory (ANL) under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. In this work, first, the cross section libraries for ENDF/B-VII.1 and ENDF/B-VIII.0 data were generated using the updated ETOE-2 code. Preliminary verification and validation tests of the ENDF/B-VII.1 MC 2 -3 library were performed with the selected fast reactor benchmark problems. Processing and verifying the libraries suggested that significant efforts would be required for thoroughly verifying the ENDF/B-VII.1 MC 2 -3 library and successfully processing the ENDF/B-VIII.0 MC 2 -3 library. Secondly, the cross section generation capability of MC 2 -3 was updated with the intermediate group lattice calculation and the equivalent Dancoff-factor cell (EDC) method to significantly improve the performance of a twodimensional assembly calculation using the method of characteristics (MOC). This effort was made useful in implementing and verifying the EDC method in the Griffin cross section API. Finally, we analyzed a SFR problem, for which unusually large deviations in core eigenvalues from Monte Carlo solutions were reported, by generating multigroup cross sections with MC 2 -3, performing core calculations with DIF3D-VARIANT, and analyzing cross section and eigenvalue results against Monte Carlo solutions. The analysis confirmed that the MC 2 -3/DIF3D solutions were in good agreement with Monte Carlo solutions, providing the correct process of accurately generating broad-group cross sections with MC 2 -3
We report that terrestrial and aquatic elemental cycles are tightly linked in upland fluvial networks. Biotic and abiotic mineral weathering, microbially mediated degradation of organic matter, and anthropogenic influences all result in the movement of solutes (e.g., carbon, metals, and nutrients) through these catchments, with implications for downstream water quality. Within the river channel, the region of hyporheic mixing represents a hot spot of microbial activity, exerting significant control over solute cycling. To investigate how snowmelt-driven seasonal changes in river discharge affect microbial community assembly and carbon biogeochemistry, depth-resolved pore water samples were recovered from multiple locations around a representative meander on the East River near Crested Butte, CO, USA. Vertical temperature sensor arrays were also installed in the streambed to enable seepage flux estimates. Snowmelt-driven high river discharge led to an expanding zone of vertical hyporheic mixing and introduced dissolved oxygen into the streambed that stimulated aerobic microbial respiration. These physicochemical processes contributed to microbial communities undergoing homogenizing selection, in contrast to other ecosystems where lower permeability may limit the extent of mixing. Conversely, lower river discharge conditions led to a greater influence of upwelling groundwater within the streambed and a decrease in microbial respiration rates. Finally, associated with these processes, microbial communities throughout the streambed exhibited increasing dissimilarity between each other, suggesting that the earlier onset of snowmelt and longer periods of base flow may lead to changes in the composition (and associated function) of streambed microbiomes, with consequent implications for the processing and export of solutes from upland catchments.
During the vitrification of Hanford Site nuclear waste at the Waste Treatment and Immobilization Plant (WTP), which is a part of the safe and efficient retrieval, treatment, and disposal mission of the U.S. Department of Energy Office of River Protection, the offgas condensate generated from the waste-to-glass conversion is currently planned to be concentrated by evaporation in the Effluent Management Facility (EMF). This concentrated condensate can then be recycled back to the incoming waste and vitrified. To test the recycle process, an apparatus was designed and built to mimic the EMF evaporator and was then used to concentrate a volume of condensate produced during the vitrification of a sample of Hanford tank 241-AP-107 (referred to herein as AP-107) waste in a continuous laboratory-scale melter (CLSM). The concentrated condensate was added to an additional sample of AP-107 waste, to mimic one round of the recycle process, and the combined solution was vitrified, producing a second round of recycle condensate. In the current study, the EMF test apparatus was used to concentrate the second-round recycle condensate under evaporation conditions (at 45 °C and 1.4 psia) designed to emulate EMF operation. The condensate was successfully concentrated by a factor of ~10 while retaining over 95 % of the technetium-99 ( 99 Tc), Cs, and I inventories in the concentrate. Another portion of AP-107 waste was retrieved by Washington River Protection Solutions, LLC (WRPS) and transferred to Pacific Northwest National Laboratory (PNNL), where it was pretreated and then combined with the second-round recycle AP-107 condensate concentrate and glass-forming chemicals (GFCs) to form the two-time recycle AP-107 melter feed, approximating a second round to the recycling action to be performed at the WTP. A portion of AP-105 waste was also retrieved by WRPS and provided to PNNL for pretreatment and combining with GFCs to form AP-105 melter feed. The two-time recycle AP-107 and AP-105 melter feeds were processed consecutively in the CLSM. The CLSM run proceeded for 13.63 hours, producing 9.70 kg of glass for an average glass production rate of 1464 kg m 2 d -1 during the two-time recycle AP-107 feed charging and 1568 kg m 2 d -1 during the AP-105 feed charging. The rate during AP-107 charging was essentially equivalent to the rate when processing no-recycle AP-107 feed and lower than that achieved when processing one-time recycle AP-107 feed. However, all rates were within the potential range of variability when processing melter feeds with similar composition in the CLSM. Likewise, the rate during AP-105 charging was higher than the previous rate processing AP-105, but within the potential CLSM range. The cold-cap characteristics changed from the typically thin AP-107 cold cap to a foamy-edged cold cap as previously seen with AP-105 shortly after transitioning to the AP-105 melter feed. The glass produced during the CLSM run was within 10 % of its target composition for the primary glass components. The CaO and Li 2 O targets varied by more than 1 wt% between the two-time recycle AP-107 and AP-105 glass targets and it took about 2 turnovers of the CLSM glass inventory to reach a relative chemical steady state in the glass for CaO and Li 2 O after the melter feed inputs were switched. The 99 Tc and total cesium content in the melter feeds were maintained at concentrations expected to be experienced at the WTP. During the CLSM run, while processing the two-time recycle AP-107 melter feed at a relative chemical steady state, the 99 Tc/Cs ratio was 10, and 34% of 99 Tc and 74% of Cs were retained in the glass. These values were higher than those measured in the CLSM run with one-time recycle AP-107 melter feed. After the transition to processing the AP-105 melter feed, when the production reached a relative chemical steady state, the 99 Tc/Cs ratio was 77 while 44% of 99 Tc was retained in the glass. The C's retention during this time frame reached 200% due to the excess C's in the glass after the target content decreased to 15% of its initial level in the two-time recycle AP-107 melter feed to the lower target in the Ap-105 melter feed. While iodine was below inductively coupled plasma mass spectrometry analytical reporting limits in the melter feed and glass samples, it was detected in quantities above the analytical reporting limits in the liquid and filter samples collected from the CLSM offgas treatment system. The behavior of iodine in the CLSM offgas treatment system followed a similar pattern to those of 99 Tc and Cs.
During the vitrification of Hanford Site nuclear waste at the Waste Treatment and Immobilization Plant (WTP), which is a part of the safe and efficient retrieval, treatment, and disposal mission of the U.S. Department of Energy Office of River Protection, the offgas condensate generated from the waste-to-glass conversion is currently planned to be concentrated by evaporation in the Effluent Management Facility (EMF). This concentrated condensate can then be recycled back to the incoming waste and vitrified. To test the recycle process, an apparatus was designed and built to mimic the EMF evaporator and was then used to concentrate a volume of condensate produced during the vitrification of a sample of Hanford tank 241-AP-107 (referred to herein as AP-107) waste in a continuous laboratory-scale melter (CLSM). The concentrated condensate was added to an additional sample of AP-107 waste, to mimic one round of the recycle process, and the combined solution was vitrified, producing a second round of recycle condensate. In the current study, the EMF test apparatus was used to concentrate the second-round recycle condensate under evaporation conditions (at 45 °C and 1.4 psia) designed to emulate EMF operation. The condensate was successfully concentrated by a factor of ~10 while retaining over 95 % of the technetium-99 (99Tc), Cs, and I inventories in the concentrate. Another portion of AP-107 waste was retrieved by Washington River Protection Solutions, LLC (WRPS) and transferred to Pacific Northwest National Laboratory (PNNL), where it was pretreated and then combined with the second-round recycle AP-107 condensate concentrate and glass-forming chemicals (GFCs) to form the two-time recycle AP-107 melter feed, approximating a second round to the recycling action to be performed at the WTP. A portion of AP-105 waste was also retrieved by WRPS and provided to PNNL for pretreatment and combining with GFCs to form AP-105 melter feed. The two-time recycle AP-107 and AP-105 melter feeds were processed consecutively in the CLSM. The CLSM run proceeded for 13.63 hours, producing 9.70 kg of glass for an average glass production rate of 1464 kg m 2 d -1 during the two-time recycle AP-107 feed charging and 1568 kg m 2 d -1 during the AP-105 feed charging. The rate during AP-107 charging was essentially equivalent to the rate when processing no-recycle AP-107 feed and lower than that achieved when processing one-time recycle AP-107 feed. However, all rates were within the potential range of variability when processing melter feeds with similar composition in the CLSM. Likewise, the rate during AP-105 charging was higher than the previous rate processing AP-105, but within the potential CLSM range. The cold-cap characteristics changed from the typically thin AP-107 cold cap to a foamy-edged cold cap as previously seen with AP-105 shortly after transitioning to the AP-105 melter feed. The glass produced during the CLSM run was within 10 % of its target composition for the primary glass components. The CaO and Li 2 O targets varied by more than 1 wt% between the two-time recycle AP-107 and AP-105 glass targets and it took about 2 turnovers of the CLSM glass inventory to reach a relative chemical steady state in the glass for CaO and Li 2 O after the melter feed inputs were switched.
Many aqueous electrolytes are only stable in the presence of another electrolyte, such as electrolytes that are only soluble in strong acid or base. An example is sodium aluminate [NaAl(OH) 4 ], which is only stable in aqueous NaOH. This complicates developing thermodynamic parameters because it is difficult to separate the contributions of individual electrolytes in multicomponent solutions to measured bulk thermodynamic properties. The present study develops a method to determine the liquid phase parameters from solubility data for Zavitsas’ Hydration model, a model that incorporates hydration parameters into the activities of dissolved species. Furthermore, this study uses gibbsite [Al(OH) 3 ] solubility data in aqueous NaOH solution to develop Zavitsas' model parameters for NaAl(OH) 4 simultaneously with the equilibrium constants. A good fit of the solubility data was found, showing that Zavitsas’ model is effective for this system.
As part of a submitted paper, which is collection of previously published analytical solutions to coupled thermo-hydro-mechanical problems in subsurface flow and transport, we have prepared a collection of python scripts to compute and plot those analytic solutions. All code to be released implements existing methods; there are no novel algorithms nor any major innovations to existing software.
In this study, we consider discrete one-dimensional nonlinear equations and present the procedure of lifting them to Z -graded graphs. We identify conditions that allow one to lift one-dimensional solutions to solutions on graphs. In particular, we prove the existence of solitons for static potentials on graded fractal graphs. We also show that even for a simple example of a topologically interesting graph, the corresponding non-trivial Lax pairs and associated unitary transformations do not lift to a Lax pair on the Z -graded graph.
In this webinar hosted by the U.S. Department of Energy's WINDExchange initiative, experts from the National Renewable Energy Laboratory introduce attendees to key concepts in end-of-service processes for wind energy technologies and infrastructure, including decommissioning, repowering, and recycling. As wind energy projects across the United States become older, decisions about end-of-service options and component processing are of growing importance to project owners and operators, state and local decision-makers, and neighboring communities. There is also concern about the blade waste that must be processed, with a number of recycling solutions being developed to meet this need. This webinar introduces the processes and activities involved at the end of a wind energy project's lifespan; discusses the impacts these activities have on communities, landowners, the wind energy industry, and other stakeholders; explains different blade recycling methods; and discusses the future of recycling in the wind energy industry.
Additive manufacturing (AM) is a revolutionary technology. One of the key AM categories, metal powder-based fusion processes, has many advantages compared to conventional methods for fabricating structural materials, such as permitting increased geometric complexity. While single material metal powder AM has advanced significantly in the past decade, multi-material AM is gradually attracting more attention owing to the recent breakthrough in multi-material feedstock delivery and the growing interest of fabricating functionally graded components. Multi-material AM offers an alternative route for applications that require location dependent material properties and high geometrical complexity. The AM community has invented several ways to achieve compositional gradients and discrete boundaries in two and three dimensions using mechanical spreading, nozzle-based, electrophotographic, and hybrid techniques. This article reviews the current state of laser powder bed fusion based multi-material AM of metals with focuses on the characteristics of the material interface as well as the properties and performance of the AM built functionally graded materials. We show the common challenges and issues related to material transitions, such as defects, segregation, phase separation, and the efficacy of some potential solutions including material and process optimizations. Additionally, this study evaluates the applicability and limitations of the existing testing standards and methods for measuring mechanical performance of functionally graded materials. Finally, we discuss mechanical testing development opportunities, which can help multi-material AM move towards higher technological maturity. In general, we find that the link between gradient microstructure and mechanical properties is not well understood or studied and suggest several mechanical tests that may better inform this knowledge gap.
Photovoltaic cells based on metal-halide perovskites have exceeded the performance of other thin film technologies and rival the performance of devices based on archetypical silicon. Attractively, the perovskite active layer can be processed via a variety of solution- and vapor-based methods. Herein, emphasis is on the use of vapor transport codeposition (VTD) to process efficient n–i–p photovoltaic cells based on methylammonium lead iodide (MAPbI 3 ). VTD utilizes a hot-walled reactor operated under moderate vacuum in the range of 0.5–10 Torr. The organic and metal-halide precursors are heated with the resulting vapor transported by a N 2 carrier gas to a cooled substrate where they condense and react to form a perovskite film. The efficiency of photovoltaic devices based on VTD-processed MAPbI 3 is found to be highest in films with excess lead iodide content, with champion devices realizing exceeding 12%.
The purpose of this project was to utilize computational tools to understand the relationships between processing, microstructure, and properties for additively manufactured (AM) aluminum alloys for automotive applications, and to provide an engineering solution for helping to optimize process conditions. The project leverages ORNL developments in computational modeling, including AM process modeling, phase-field based microstructure evolution predictions, and data analytics techniques for mapping process conditions to material outcomes. The project utilized an Al-Cu-Mn-Zr alloy as a model material for studying formation of defects and microstructural features in response to variations in process conditions. Based on both pre-existing experimental data and simulation results, statistical process maps were constructed to identify regions of process space with minimal defect formation and advantageous microstructures and properties. The software tools used for this purpose were successful disseminated to GM, who were able to successful compile the relevant HPC codes within their own computing ecosystem and perform initial calculations to reproduce ORNL results.