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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Embedded Web Technology: Applying World Wide Web Standards to Embedded Systems

Embedded Systems have traditionally been developed in a highly customized manner. The user interface hardware and software along with the interface to the embedded system are typically unique to the system for which they are built, resulting in extra cost to the system in terms of development time and maintenance effort. World Wide Web standards have been developed in the passed ten years with the goal of allowing servers and clients to intemperate seamlessly. The client and server systems can consist of differing hardware and software platforms but the World Wide Web standards allow them to interface without knowing about the details of system at the other end of the interface. Embedded Web Technology is the merging of Embedded Systems with the World Wide Web. Embedded Web Technology decreases the cost of developing and maintaining the user interface by allowing the user to interface to the embedded system through a web browser running on a standard personal computer. Embedded Web Technology can also be used to simplify an Embedded System's internal network.

Ponyik, Joseph G.↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗
NASA NTRS2006

Effect of Embedded Piezoelectric Sensors on Fracture Toughness and Fatigue Resistance of Composite Laminates Under Mode I Loading

Double-cantilevered beam (DCB) specimens of a glass/epoxy composite material with embedded piezoelectric sensors were tested both statically and under fatigue loading to determine the effect of the embedded material on the Mode I fracture toughness and fatigue resistance compared to baseline data without the embedded elements. A material known as LaRC-Macrofiber Composite (LaRC-MFC (TradeMark)), or MFC, was embedded at the midplane of the specimen during the layup. Specimens were manufactured with the embedded MFC material either at the loaded end of the specimen to simulate an initial delamination; or with the MFC material located at the delaminating interface, with a Teflon film at the loaded end to simulate an initial delamination. There were three types of specimens with the embedded material at the delaminating interface: co-cured with no added adhesive; cured with a paste adhesive applied to the embedded element; or cured with a film adhesive added to the embedded material. Tests were conducted with the sensors in both the passive and active states. Results were compared to baseline data for the same material without embedded elements. Interlaminar fracture toughness values (G(sub Ic)) for the passive condition showed little change when the MFC was at the insert end. Passive results varied when the MFC was at the delaminating interface. For the co-cured case and with the paste adhesive, G(sub Ic) decreased compared to the baseline toughness, whereas, for the film adhesive case, G(sub Ic) was significantly greater than the baseline toughness, but the failure was always catastrophic. When the MFC was in the active state, G(sub Ic) was generally lower compared to the passive results. Fatigue tests showed little effect of the embedded material whether it was active or passive compared to baseline values.

Murri, Gretchen B.↗
DOE OSTI2024

Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.

Ti, Changpeng↗
NASA NTRS2009

Embedded passives emulator FY2003 interim report

The National Aeronautics and Space Administration Electronic Parts and Packaging (NEPP) Program has been evaluating state-of-the-art Embedded Passive Technology for the printed circuit board (PCB) industry for the past several years and has conducted its own testing on embedded passive coupons (resistors and capacitors). This document is a progress report of the work performed through fiscal year 2008 by NASA and the Navy to convert an existing PWB (printed wiring board) design to a design that incorporates embedded passive components. The work is being performed through a memorandum of understanding (MOU) between the NEPP Program (NASA) and the Naval Surface Warfare Center (NSWC) Crane Emerging Critical Interconnect Technology (ECIT) Program. NSWC Crane’s ECIT Program has funding to convert two PCB designs from the military and aerospace industry to a PCB design with embedded passives. ECIT will provide a design team that will take a well characterized design and convert it to an embedded design. They will also provide electrical modeling and layout expertise as well as build the PWB in their facility in Indiana. NEPP, through the Jet Propulsion Laboratory (JPL), will assemble any passives deemed not suitable for embedding and the active components and electrically test the new design to the existing (non-embedded) design

Gerke, David↗
DOE OSTI2024

OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model

With the sharp increasing volume of user data, Deep Learning Recommendation Model (DLRM) becomes an indispensable infrastructure in large technology companies. However, large-scale DLRM on the multi-GPU platform is still inefficient due to unbalanced workload partitioning and intensive inter-GPU communication. To this end, we propose OPER, an OPtimality guided Embedding table placement for large-scale Recommendation model training and inference. OPER explores the potential of mitigating remote memory access latency in DLRM through fine-grained embedding table placement. Specifically, OPER proposes a theoretical modeling that builds up the relationship between EMT placement and the embedding communication latency in both training and inference. OPER proves the NP hardness of finding the optimal embedding table placement and proposes a heuristic algorithm that yields near optimal placement. OPER implements a SHMEM-based embedding table training system and a unified embedding index mapping to support fine-grained embedding table sharding and placement. Comprehensive experiments reveal that OPER achieves on average 3.4× and 5.1× speedup on training and inference respectively over state-of-the-art DLRM frameworks.

Wang, Zheng↗
NASA NTRS2015

Embedded Thermal Control for Subsystems for Next Generation Spacecraft Applications

Thermal Fluids and Analysis Workshop, Silver Spring MD NCTS 21070-15. NASA, the Defense Department and commercial interests are actively engaged in developing miniaturized spacecraft systems and scientific instruments to leverage smaller cheaper spacecraft form factors such as CubeSats. This paper outlines research and development efforts among Goddard Space Flight Center personnel and its several partners to develop innovative embedded thermal control subsystems. Embedded thermal control subsystems is a cross cutting enabling technology integrating advanced manufacturing techniques to develop multifunctional intelligent structures to reduce Size, Weight and Power (SWaP) consumption of both the thermal control subsystem and overall spacecraft. Embedded thermal control subsystems permit heat acquisition and rejection at higher temperatures than state of the art systems by employing both advanced heat transfer equipment (integrated heat exchangers) and high heat transfer phenomena. The Goddard Space Flight Center Thermal Engineering Branch has active investigations seeking to characterize advanced thermal control systems for near term spacecraft missions. The embedded thermal control subsystem development effort consists of fundamental research as well as development of breadboard and prototype hardware and spaceflight validation efforts. This paper will outline relevant fundamental investigations of micro-scale heat transfer and electrically driven liquid film boiling. The hardware development efforts focus upon silicon based high heat flux applications (electronic chips, power electronics etc.) and multifunctional structures. Flight validation efforts include variable gravity campaigns and a proposed CubeSat based flight demonstration of a breadboard embedded thermal control system. The CubeSat investigation is technology demonstration will characterize in long-term low earth orbit a breadboard embedded thermal subsystem and its individual components to develop optimized operational schema.

Spacecraft Systems↗
NASA NTRS1994

Post-embedding tem signal-to-noise ratio of S-100

We assessed the reactivity of purified S-100 antiserum in immuno-electron microscopy by counting the number of gold particles per microns 2 over inner ear tissues embedded in different media. Sections containing predominantly Schwann's cell cytoplasm and nucleus, afferent fiber axoplasm and myelin sheath of chick cochleae were reacted with anti-S-100 IgG, an antibody to a calcium binding protein of neuronal tissues, then labeled with anti-IgG-gold conjugate. This investigation was conducted because previously published procedures, unmodified, did not yield acceptable results. Preparation of all specimens was identical. Only the medium (PolyBed 812, Araldite or Spurr epoxies; and LR White, LR Gold or Lowicryl plastics) was changed. The medium was made the changing variable because antigens available in post-embedding immuno-electron microscopy are decreased by heat, either used and/or released during polymerization of the embedding medium. The results indicate that: (a) none of the embedding media above provided optimal signal-to-noise ratio for all parts of the nerve stained in the same section; (b) aggregation of gold particles over cells was highest in embedding media with high background labeling over areas devoid of tissue (noise); (c) aggregation occurred randomly throughout both cellular and acellular regions; and (d) particles aggregated less and were distributed more evenly in tissues from media yielding good ultrastructural integrity.

Non-NASA Center↗
DOE OSTI2025

Projector‐Based Quantum Embedding Study of Iron Complexes

Projection‐based embedding theory (PBET) is used to calculate and assess the challenging spin‐crossover energies for a selection of small Fe‐containing systems by embedding the metal center into the frozen potential of the ligands. MP2, CCSD, and CCSD(T) are embedded in potentials from the SCAN and r 2 SCAN functionals and compared with the canonical values for the constituent methods and previously reported reference values. Considering the PBET calculations as a correction for the underlying DFT, the embedding calculations are able to provided improvement for most cases. In some cases, the PBET methods are able to compensate for limitations in the wave function methods and produce results similar to more rigorous calculations from the literature. For the systems with spin‐crossover energies near zero, the current methodology fails to provide consistent improvement. In conclusion, the isolated recalculation of the electronic structure around the metal center when embedded into a DFT treatment of the ligand field shows promise as a pragmatic and lower cost treatment compared to the canonical treatment of the whole system of the difficult class of spin‐crossover complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Self-Sensing Composites via an Embedded 3D-Printed PVDF-MoS 2 Nanosensor for Structural Health Monitoring

Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.

PVDF-MoS2 sensor↗
NASA NTRS1986

Embedded mesh solution of the 2-D Euler equations - Evaluation of interface formulations

Solution of the steady 2-D Euler equations using mesh embedding, or local grid refinement, with a cell-centered finite volume scheme is investigated. Embedded regions which are topologically similar to the global grid are considered. An isoenergetic model for the governing equations is used in Jameson's finite volume multistage scheme with modifications to the boundary conditions and smoothing. A detailed study of the embedding interface flux and smoothing formulations is conducted. Taylor expansion analysis reveals that local second order spatial accuracy is not possible if a conservative interface flux formulation is used. The analysis also gives constraints for local first order accuracy. An energy stability analysis indicates that downwind weighting of interface fluxes causes local instabilities. Analysis shows that conservative interface smoothing formulations must have a locally convective component, but that correct interface formulations allow globally dissipative smoothing. Embedded mesh solutions obtained with this scheme are presented for a transonic airfoil. They show that if embedding interfaces are close to the shocks, then small modifications in the interface location can have large effects on converge and solution accuracy.

Allmaras, S. R.↗
NASA NTRS1991

The nature, origin and evolution of embedded star clusters

The recent development of imaging infrared array cameras has enabled the first systematic studies of embedded protoclusters in the galaxy. Initial investigations suggest that rich embedded clusters are quite numerous and that a significant fraction of all stars formed in the galaxy may begin their lives in such stellar systems. These clusters contain extremely young stellar objects and are important laboratories for star formation research. However, observational and theoretical considerations suggest that most embedded clusters do not survive emergence from molecular clouds as bound clusters. Understanding the origin, nature, and evolution of embedded clusters requires understanding the intimate physical relation between embedded clusters and the dense molecular cloud cores from which they form.

Lada, Charles J.↗
DOE OSTI2025

Explaining word embeddings with perfect fidelity: a case study in predicting research impact

The best-performing approaches for scholarly document quality prediction are based on embedding models. In addition to their performance when used in classifiers, embedding models can also provide predictions even for words that were not contained in the labelled training data for the classification model, which is important in the context of the ever-evolving research terminology. Although model-agnostic explanation methods, such as Local interpretable model-agnostic explanations, can be applied to explain machine learning classifiers trained on embedding models, these produce results with questionable correspondence to the model. We introduce a new feature importance method, Self-Model Entities Rated (SMER), for logistic regression-based classification models trained on word embeddings. We show that SMER has theoretically perfect fidelity with the explained model, as the average of logits of SMER scores for individual words (SMER explanation) exactly corresponds to the logit of the prediction of the explained model. Quantitative and qualitative evaluation is performed through five diverse experiments conducted on 50,000 research articles (papers) from the CORD-19 corpus. In conclusion, through an AOPC curve analysis, we experimentally demonstrate that SMER produces better explanations than LIME, SHAP and global tree surrogates.

Coarse-grained models↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗
DOE OSTI2024

Impacts of Control, Penetration, and Distribution of Embedded Storage Network in Bulk Power System

The current shift in generation mix from fossil fuel plants towards variable and intermittent renewable energy sources is poised to create a future grid with reduced physical inertia and mismatch between generation and demand. Embedded storage, which is a concept of a coordinated network of storage units sited at the interface between the transmission and distribution system, is proposed as a mechanism to provide a buffer between generation and demand. This paper proposes an automated framework to model and integrate embedded storage in large-scale power systems with industry-grade grid-following (GFL) and grid-forming (GFM) control technologies. More importantly, the developed framework is used to explore the impacts of embedded storage control, penetration, location, and capacity in providing fast frequency response to the grid under contingency events such as generator trips and faults. The framework and study are conducted using the transient-stability simulation tool PSS/E and a realistic model of the Puerto Rico grid as a chosen test system. The simulation results show that GFL and GFM embedded storage, distributed throughout the system, with sufficient penetration and capacity, can effectively improve primary frequency response of the system under the studied contingency events.

Battery Energy Storage, embedded storage, grid-for↗
DOE OSTI2024

Embedding of Optical Fibers with Electric Field Assisted Sintering

The Electric Field Assisted Sintering (EFAS) technique was used for embedding the fibers, involving rapid heating via an electric current and pressure, reducing fiber exposure to high temperatures. Stainless steel guide tubes were inserted at the fiber-matrix junctions to prevent fiber breakage during the sintering process. The study varied key EFAS parameters—temperature (800°C to 980°C), pressure (40-50 MPa), and hold time (5-10 minutes)—to assess how they influenced fiber embedding. After embedding, the fibers were inspected using optical frequency domain reflectometry (OFDR) to evaluate optical losses and strain along the fiber length. The OFDR scans confirmed that the fibers remained intact, and subsequent transmission tests demonstrated no macroscopic fractures. The embedded samples were also analyzed using scanning electron microscopy (SEM) and X-ray computed tomography (CT) scans. These tests confirmed good bonding between the fiber and matrix with no cracking, though some porosity was present in samples fabricated at lower temperatures. At higher temperatures (980°C), the fiber-matrix bonding was continuous and defect-free, with metal coatings aiding in the bonding process. Elemental analysis showed interdiffusion between the fiber coatings and matrix materials, particularly between the gold coating and stainless steel. Helium leak tests revealed that lower sintering temperatures resulted in matrix porosity, causing leaks, but samples fabricated at 980°C were leak-tight.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS2019

Electrical and Thermal Characterization of 3D Printed Thermoplastic Parts with Embedded Wires for High Current-Carrying Applications

Fabrication of parts exhibiting multi-functionality has recently been complemented by hybrid polymer extrusion additive manufacturing in combination with wire embedding technology. While much mechanical characterization has been performed on parts produced with fused deposition modeling, limited characterization has been performed when combined electrical and thermal loads are applied to 3D printed multi-material parts. As such, this work describes the design, fabrication, and testing of 3D printed thermoplastic coupons containing embedded copper wires that carried current. An automated fabrication process was used employing a hybrid additive manufacturing machine that dispensed polycarbonate thermoplastic and embedded bare copper wires. Testing included AC and DC hipot testing as well as thermal testing on as-fabricated and heat treated coupons to determine the effect of porosity in the substrate. The heat-treated parts contained reduced amounts of porosity, as corroborated through scanning electron microscopy, which led to 50 % increased breakdown strength and 30 to 40 % increased heat dissipation capabilities. The results of this research are describing a set of design protocol that can be used as a guideline for 3D printed embedded electronics to predict the electrical and thermal behavior.

Hipot testing↗