SEARCH · Search NASA
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.
Once a site location is selected for the installation of an electric vehicle (EV) charging station, the next step is to plan and design the specific station. Local and state governments can require and/ or incentivize accessible and safe design elements, so the station is constructed in an equitable manner.
When planning to incorporate electric vehicle (EV) infrastructure into your community, there are opportunities to weave in equity considerations. This will streamline the approval process for federal funding while ensuring no community member is left behind during this nationwide transition to low-emissions transportation options.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
A versatile drug delivery system comprised of plant-based nanocellulose can be used for providing high loading and extended release for virtually any drug. This system is made from nanocrystalline cellulose chemically cross-linked to form a hydrogel. This versatile hydrogel-based drug delivery system can be functionalized to support extended release of oral dosing, relieving patient burden and increasing quality of life.
Heat exchanger assemblies are provided that can include: a heat exchanger housing; at least one primary conduit operably coupled to the heat exchanger housing and configured to convey a primary heat exchange fluid; at least one secondary conduit operably coupled to the heat exchanger housing and configured to convey a secondary heat exchange fluid; at least one thermal interface between the primary and secondary fluids; and at least one sensor operably engaged with the thermal interface. Heat exchanger assemblies including molten salt, liquid metal, or water/steam as part of the heat exchange fluids of the heat exchanger assembly are provided. The heat exchanger assemblies can include: at least one thermal interface between primary and secondary heat exchange fluids of the heat exchanger assembly; and a sensor operably engaged with the at least one interface.
Waveguides and methods for manufacturing a waveguide that include forming a first channel in a first layer of dielectric material, the first channel comprising one or more walls; forming a second channel in a second layer of dielectric material, the second channel comprising one or more walls; depositing electrically conductive material on the one or more walls of the first channel; depositing electrically conductive material on the one or more walls of the second channel; arranging the first layer adjacent to the second layer to form a stack with the first channel axially aligned with and facing the second channel; and heating the stack so that the conductive material on the one or more walls of the first channel and the conductive material on the one or more walls of the second channel connect to form the waveguide.
A cooling system for an electric motor that includes a stator having a plurality of slot windings and a rotor, coaxial with the stator, having a plurality of magnets, includes a coolant inlet to the motor and a coolant outlet from the motor. A coolant pathway is in fluid communication with the inlet and the outlet. Heat is transferable from the slot windings to the coolant pathway. A coolant flows through the coolant pathway and is in a liquid phase as it enters the coolant inlet, changing into a gaseous phase as heat is transferred to the coolant from the slot windings. A cooling loop is in fluid communication with the coolant inlet and the coolant outlet. The cooling loop cools the coolant so that substantially all of the coolant is in the liquid phase when it enters the coolant inlet.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
This presentation deck highlights the research and development results of an DOE AMMTO project of refractory compact heat exchangers to be sent to GE Aerospace as a process of the IP licensing opportunity discussion.
Graphs represent real world relationships, and graph embedding projects nodes in a graph to a latent space that can help simplify downstream tasks. Recent development of graph convolutions in deep learning significantly improves the performance of many learning tasks on graphs. Unfortunately, prior embedding methods either do not embed graphs with node features, or fail to produce high-quality embeddings for downstream learning tasks that result in large performance gap in comparison to direct learning on graphs.We present a versatile and effective embedding method, Conv2Vec, for embedding graphs with or without node features. It is based on graph convolutions with objective functions motivated by concepts and structures from classical graph algorithms. Conv2Vec produce high-quality embedding for both plain graphs and graphs with node features for downstream tasks.We evaluate the embeddings generated by Conv2Vec with a transductive node classification task. With the generated embeddings and very simple machine learning approaches, we are able to achieve accuracies similar to those achieved by direct learning with graph convolutions. Interestingly, if we strip the node features from the graph and thus learning an embedding has to rely entirely on the graph topology, node classification with our embedding significantly outperforms direct learning with various graph convolutions. This suggests that structures from classical graph algorithms may play an important role in learning on graphs.
Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.
Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.
A hybrid manufacturing approach, integrating additive manufacturing (AM) with powder methodology via electric field-assisted sintering (EFAS), was developed for the fabrication of high-temperature compact heat exchangers (CHX) from refractory metals. The methodology employed additively manufactured sacrificial channel molds (SCMs) as shapeholders for CHX channels, which were embedded in metal powders using EFAS. Following embedding, the SCMs were chemically dissolved to form the internal channel network. SCMs were fabricated using both digital light processing (DLP) and direct ink writing (DIW) from chemically reactive, calcium-based ceramic feedstocks with varying ratios of Al2O3 reinforcement. The microstructure, phase composition, and dissolution behavior of both as-printed and embedded SCMs were investigated. The shrinkage behavior of the SCMs embedded in refractory metals, as well as the interfacial characteristics between the SCMs and metal matrix, were studied. The results showed that the SCMs containing sufficient chemical reactive ceramics dissolved effectively before and after embedding. The as-printed SCMs retained the phase composition of their feedstocks, but the embedded SCMs containing calcium-based ceramics and Al2O3 exhibited the formation of calcium aluminates due to high temperature exposure during embedding. Most SCMs exhibited a cellular Al2O3 network filled with Ca-rich ceramics. Shrinkage after embedding was strongly dependent on SCM density, with lower density SCMs exhibiting greater shrinkage. A thin SCM-affected zone was observed at the metal matrix surface, characterized by increased porosity compared to the bulk matrix. This effect was attributed to infiltration of the SCM materials into powder particle boundaries under pressure, followed by their removal during dissolution. This study demonstrates the feasibility of manufacturing CHXs from hard-to-process refractory metals for use in harsh environments.
To optimize the operation and functioning of high temperature systems such as slagging gasifiers, coal boilers and glass/steel melters, it is important to monitor corrosion and erosion of refractory used in such systems. Corrosion test strategies are generally based on continuous gravimetric and chemical reactivity monitoring at operational temperatures (750°-1500°C). Both thermocouples and failure sensors and arrays would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance demonstration of smart refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The main feature of this technology is that electroceramic based sensors are embedded into smart refractory without significantly impact to the intrinsic properties of the refractory. This technology circumvents the need to insert an isolated monolithic, stand-alone sensor into the refractory via an access port. This technological approach guarantees the integrity and the chemical stability of the materials used in the sensor fabrication within the harsh environment and does not introduce molten material (such as slag) penetration pathways within the refractory. One interesting and important aspect of this innovation is that these embedded sensors can be used to in situ monitoring processes such as chemical reactions and at the same time give information and a deeper understanding of the corrosion and erosion process of the refractory within the system. As stated above, the objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). Prior to the development of the high-temperature sensors, the oxides composites developed in this study were sintered up to 1600°C under oxidizing atmosphere in order to investigate densification, microstructural evolution, phase development, and their thermoelectrical performance as a function of the composition. The 4-point DC conductivity measurements were performed between 100°-1500°C. The sensors were fabricated from the composite materials by 3D-printing or screen-printing methods into the refractory brick during the consolidation process. An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV around 1400 °C. Corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory brick an in-house glass composition was prepared and pressed into pellets and delivered into a pre-cut cavity in the brick. Corrosion experiments results showed the glass penetrated the brick over a 90 h period, and the penetration of the glass through the brick could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process.