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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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Learn how electric school buses can work for your district by understanding the basics of electrification. Understand what is needed to electrify your fleet, how much it costs, and how the process will effect your greenhouse gas emission impacts.
Polymer additive manufacturing (AM) technology is rapidly growing and its transition to become an advanced manufacturing technique now offers possibilities for unconventional applications. One of these potential applications is on site additive manufacturing of power poles. During natural incidents such as storms and hurricanes, power lines can be severely damaged and there may not be sufficient spare power poles to replace. In such situations, additive manufacturing of power poles on site can offer a fast, temporary solution during emergencies. The ability of freeform manufacturing of parts with complex geometries makes large scale AM attractive for various applications; however, due to residual stress build up and need for different mechanical requirements for different applications, reinforcement of the AM feedstock is necessary. In addition to carbon and glass fibers, bio-derived fibers can also be used as a reinforcing phase, both to utilize the natural resources at the emergency area and in terms of long-term sustainability. In this project, design and additive manufacturing of a 13.1 m power pole was demonstrated. Three different feedstock material options (polystyrene, cellulose ester and polycarbonate reinforced with glass and bamboo fibers) and two different pole designs were investigated. As-printed part tensile properties both in printing and interlayer directions were measured and a power pole was printed as six pieces and put together successfully. Finally, a conceptual design of a portable power pole manufacturing approach was proposed.
The continuously growing demand for batteries used within automotive, aviation, and grid applications has exacerbated the need to supplement critical battery material feedstocks, such as those for anode and cathode active materials. New or supplementary material sources, however, universally comprise unique material properties that can significantly affect the lifetime and performance of resultant batteries. As such, the influence of composition, microstructure, and morphology on electrochemical performance should be characterized quickly and accurately to accelerate commercialization of new material sources. This work introduces a tiered framework to quickly assess new material viability and understand the influence of physicochemical properties on battery performance. The Tier 1 testing described here is rapid and lower-effort to quickly recognize materials with fundamental flaws and potentially disqualify them. Later testing would require more time and effort but provide higher fidelity information with a goal of validating materials for specific applications. A case study examining various commercial sources of LiFePO4 (LFP) is presented, using Tier 1 of the protocol to identify rapid electrochemical and physicochemical signals that correlate with performance and to provide early go/no-go decisions on LFP materials without the need for long-term cycling data.
Knowledge of the distribution of soil thermal properties is important for understanding subsurface hydrological and biogeochemical processes. This study describes and evaluates quick thermal profiling (QTP), a new measurement technique aimed at providing rapid, depth-resolved measurements of soil thermal inertia at numerous locations across the landscape. A cylindrical probe with temperature sensors at multiple depths is quickly inserted into the ground, and soil thermal inertia is estimated from how quickly the probe temperature equilibrates with the soil. To this end, a finite volume heat transfer model is used to generate temperature equilibration time series across combinations of controlling factors, and a gridded search inversion approach is applied to infer soil thermal inertia. Field tests in the Arctic indicate that QTP measurements have a minimum uncertainty of 0.14 J m −2 K −1 s −1/2 and covary with dual-probe heat pulse thermal analyzer measurements (concordance correlation coefficient = 0.56) with a root-mean-square error of 0.40 J m −2 K −1 s −1/2 . Besides demonstrating the value of QTP for estimating thermal inertia, this study identifies various sources of measurement uncertainty, particularly probe-soil contact resistance and frictional heating. Further, analysis of soil samples indicates that thermal inertia can be used to estimate thermal conductivity and dry bulk density in the studied area, although such inferences are highly site-specific. Overall, the QTP method holds promise to generate thermal inertia data products and to complement other characterization approaches for advancing understanding of soil properties across far more locations than is currently possible.
Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.
Extreme precipitation events often produce severe flooding and pose significant threats to our society. However, their subseasonal impacts on the terrestrial and atmospheric systems are not well understood. Here, we use a new land–atmosphere coupled water tracer tool embedded in the Weather Research and Forecasting (WRF) Model to “tag” precipitation produced by a series of extreme mesoscale convective systems (MCSs) in May 2015 over the southern Great Plains to reveal their contributions to different terrestrial water storages and moisture recycling during May–July. During May, we find that precipitation from earlier MCSs can contribute to 20%–50% of total evapotranspiration (ET) in later May and contribute ∼10% of local precipitation for later MCSs. After May, the water “tagged” to preceding May MCS events has the most active contribution to moisture recycling during the first 5 days in June, but this contribution diminishes quickly afterward. Both periods indicate a quick turnover of tagged precipitation, which may be representative of the southern Great Plains region. The quick turnover is closely associated with the direct evaporation from the soil surface due to the predominant shrubland and grassland coverage over this region. Over some forested areas, however, the tracer-ET flux is dominated by transpiration that stably supplies a small fraction (<1%) of moisture to the atmosphere throughout June and July. In conclusion, the tracer-contributed plume (through evaporation and transpiration) in atmospheric moisture can extend 500–700 km downstream between May and July, highlighting the far-reaching contribution to moisture recycling and precipitation from the tagged MCS events in the southern Great Plains.
A challenge for algal biofuel development is finding strains of algae best suited for producing copious amounts of oil quickly and under industrial-scale conditions. Furthermore, maintaining pond health is also challenging—deteriorating health in a small fraction of algal cells within a pond can quickly lead to the death of the entire pond’s population. To overcome these challenges, Los Alamos National Laboratory (LANL) scientist Christina Steadman and team designed a process to optimize algal physiology and metabolism in cultures by interrogating specific characteristics of the algae cells. This process uses flow cytometry methods and solves two problems: the LANL team can quickly detect potential pond problems, and use this process to streamline the screening of potential algae candidates for biofuel production.
The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner.
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension
An approximation method and system are provided for more quickly controlling a prosthetic or other device by reducing computational processing time in a muscle model that can be used to control the prosthetic. For a given muscle, the approximation method can quickly compute polynomial structures for a muscle length and for each associated moment arms, which may be used to generate a torque for a joint position of a physics model. The physics model, in turn, produces a next joint position and velocity data for driving a prosthetic. The approximation method expands the polynomial structures as long as expansion is possible and sufficiently beneficial. The computations can be performed quickly by expanding the polynomial structures in a way that constrains the muscle length polynomial to the moment arm polynomial structures, and vice versa.
Determination of the lithium ion content in cathode materials, which is currently performed by inductively coupled plasma mass spectrometry (ICP-MS), involves tedious procedures and error-prone preparation. This work introduces a new methodology to quickly determine the lithium content in spent cathode materials (LiNix Mny Coz O2, x + y + z = 1, NMC) using a thermalgravimetric analysis, which bypasses the use of ICP-MS and significantly reduces the cost and time required for determining the lithium content. Based on this new method, a new approach is developed to identify spent cathode materials suitable for direct relithiation recycling. Finally, these new methods have been applied to investigate the effectiveness of direct electrochemical-relithiation on recycling the spent cathodes, and provide insights for future development of direct battery materials recycling.
ABSTRACT Artificially propagated Pacific lamprey ( Entosphenus tridentatus ) are produced for restoration and for use in dam passage studies to reduce the demand for wild fish. Such uses require that animals are representative of their wild counterparts. Previous work indicated that this is true for Pacific lamprey larvae and juveniles reared in the hatchery with respect to the length of sustained swimming. However, more subtle differences in behaviour and performance that lamprey need to survive have not been assessed. In this study, artificially propagated and wild fish were compared in laboratory tests under no‐flow conditions to examine light avoidance, burrowing speed, burst swim speed, volitional routine swim speed and time to come to rest. Most larvae burrowed in less than a minute, and we found highly significant differences ( p < 0.001) between artificially propagated and wild larvae burrowing times, a critical escape behaviour. This could have implications for studies of larval entrainment at irrigation diversion canals or in turbine boils at dams. Interestingly, all of the wild juveniles tested came to rest quickly after introduction to the chamber (1.5 min), while artificially propagated lamprey swam robotically near the surface and 48% did not come to rest in the first 10 min (median time to rest = 9.5 min). In contrast, wild juveniles quickly (median = 1.47 min) sought areas near the tank bottom and attached strongly with their oral disc. Such behavioural differences could have important survival consequences for artificially propagated lamprey as they approach turbine intakes, bypass screens and irrigation diversion headgates. This study highlights the need to conduct behavioural assays that examine subtleties of fish behaviour that can be missed with traditional swim tunnel comparisons.
Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.
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Abstract Legacy radioactive waste from nuclear weapons material processing is the biggest environmental challenge in the state of South Carolina, and one of the biggest in the United States. Although substantial progress has been made in processing and vitrifying high‐level radioactive waste at the Savannah River site, approximately 35 million gallons remains to be treated and dispositioned. In this article, we show the development of a new defoamer for use in the processing of high‐level radioactive waste. The application of the new defoamer is not only more effective at controlling foam but will shorten the batch processing time leading to safer and more efficient processing. This has the potential to help shorten the overall site mission lifetime, saving the federal government hundreds of millions of dollars. The novel aspect of the new defoamer is its effectiveness and stability in the harsh conditions needed for processing high‐level radioactive waste: high temperatures, pH 4–13, strong oxidizing and reducing agents, and numerous metal catalysts. The defoamer will replace an existing antifoam agent that decomposes to form multiple flammable gases. Because the existing antifoam agent decomposes quickly, processing changes increased processing time. Statement of novelty We demonstrate that a new defoamer has been developed for use in processing high‐level radioactive waste. This novel defoamer is effective and stable in the harsh processing conditions with no detectable flammable by‐products. Because the existing antifoam agent decomposes quickly, processing changes, including lowering acid addition rates and evaporation rates, increased processing time. The new defoamer is not only more effective at controlling foam but will shorten the batch processing time and lead to safer and more efficient processing by eliminating the production of flammable gases. Statement of industrial relevance Foaming is of great concern in many industrial processes involving three‐phase gas/liquid/fine‐solids systems, like in water evaporation, mineral floatation, air sparging in situ remediation techniques, and in those found in the paper industry.
ABSTRACT Hydrologic connectivity is defined as the connection among stores of water within a watershed and controls the flux of water and solutes from the subsurface to the stream. Hydrologic connectivity is difficult to quantify because it is goverened by heterogeniety in subsurface storage and permeability and responds to seasonal changes in precipitation inputs and subsurface moisture conditions. How interannual climate variability impacts hydrologic connectivity, and thus stream flow generation and chemistry, remains unclear. Using a rare, four‐year synoptic stream chemistry dataset, we evaluated shifts in stream chemistry and stream flow source of Coal Creek, a montane, headwater tributary of the Upper Colorado River. We leveraged compositional principal component analysis and end‐member mixing to evaluate how seasonal and interannual variation in subsurface moisture conditions impacts stream chemistry. Overall, three main findings emerged from this work. First, three geochemically distinct end members were identified that constrained stream flow chemistry: reach inflows, and quick and slow flow groundwater contributions. Reach inflows were impacted by historic base and precious metal mine inputs. Bedrock fractures facilitated much of the transport of quick flow groundwater and higher‐storage subsurface features (e.g., alluvial fans) facilitated the transport of slow flow groundwater. Second, the contributions of different end members to the stream changed over the summer. In early summer, stream flow was composed of all three end members, while in late summer, it was composed predominantly of reach inflows and slow flow groundwater. Finally, we observed minimal differences in proportional composition in stream chemistry across all four years, indicating seasonal variability in subsurface moisture and spatial heterogeneity in landscape and geologic features had a greater influence than interannual climate fluctuation on hydrologic connectivity and stream water chemistry. These findings indicate that mechanisms controlling solute transport (e.g., hydrologic connectivity and flow path activation) may be resilient (i.e., able to rebound after perturbations) to predicted increases in climate variability. By establishing a framework for assessing compositional stream chemistry across variable hydrologic and subsurface moisture conditions, our study offers a method to evaluate watershed biogeochemical resilience to variations in hydrometeorological conditions.
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