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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A coupled discontinuous Galerkin-Finite Volume framework for solving gas dynamics over embedded geometries

Herein, we present a computational framework for solving the equations of inviscid gas dynamics using structured grids with embedded geometries. The novelty of the proposed approach is the use of high-order discontinuous Galerkin (dG) schemes and a shock-capturing Finite Volume (FV) scheme coupled via an hp adaptive mesh refinement (hp-AMR) strategy that offers high-order accurate resolution of the embedded geometries. The hp-AMR strategy is based on a multi-level block-structured domain partition in which each level is represented by block-structured Cartesian grids and the embedded geometry is represented implicitly by a level set function. The intersection of the embedded geometry with the grids produces the implicitly-defined mesh that consists of a collection of regular rectangular cells plus a relatively small number of irregular curved elements in the vicinity of the embedded boundaries. High-order quadrature rules for implicitly-defined domains enable high-order accuracy resolution of the curved elements with a cell-merging strategy to address the small-cell problem. The hp-AMR algorithm treats the system with a second-order finite volume scheme at the finest level to dynamically track the evolution of solution discontinuities while using dG schemes at coarser levels to provide high-order accuracy in smooth regions of the flow. On the dG levels, the methodology supports different orders of basis functions on different levels. The space-discretized governing equations are then advanced explicitly in time using high-order Runge-Kutta algorithms. Numerical tests are presented for two-dimensional and three-dimensional problems involving an ideal gas. The results are compared with both analytical solutions and experimental observations and demonstrate that the framework provides high-order accuracy for smooth flows and accurately captures solution discontinuities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Scalable semidefinite programming approach to variational embedding for quantum many-body problems

In quantum embedding theories, a quantum many-body system is divided into localized clusters of sites which are treated with an accurate ‘high-level’ theory and glued together self-consistently by a less accurate ‘low-level’ theory at the global scale. The recently introduced variational embedding approach for quantum many-body problems combines the insights of semidefinite relaxation and quantum embedding theory to provide a lower bound on the ground-state energy that improves as the cluster size is increased. The variational embedding method is formulated as a semidefinite program (SDP), which can suffer from poor computational scaling when treated with black-box solvers. Here, we exploit the interpretation of this SDP as an embedding method to develop an algorithm which alternates parallelizable local updates of the high-level quantities with updates that enforce the low-level global constraints. Moreover, we show how translation invariance in lattice systems can be exploited to reduce the complexity of projecting a key matrix to the positive semidefinite cone.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds

Quantum-mechanics-(QM)-based simulations now routinely aid in understanding and even discovering new chemistries involving molecules and materials exhibiting desired functionalities. Ab initio correlated wavefunction (CW) theories systematically improve QM methods, with many exhibiting high accuracy. However, execution of CW methods requires expensive computations that typically scale poorly with system size. Divide-and-conquer approaches partition large systems into smaller fragments; a lower level of theory treats fragment interactions while a preferred higher level of theory describes the important fragment. These methods offer ways to incorporate CWs into chemical simulations of large systems, e.g., biomolecules, surfaces, large inorganic clusters, bulk crystals, etc. Here we propose a partitioning protocol that utilizes capping atoms to saturate severed covalent bonds at fragment interfaces and density functional embedding theory (DFET) to describe fragment interactions. The capping groups in each fragment provide an ad-hoc potential that approximates the effects of the environment. An embedding potential optimized via DFET then serves as an augmentation of the capping group to simulate the effects of the environment. We concurrently use an auxiliary fragment (a separate system comprised of only the combined capping groups) to account for, and thereby correct, the electron density contributions of all the capping groups added to all of the fragments. This method depends only on the capped-subsystem and auxiliary-fragment electron densities, forgoing, as with the original DFET developed for metallic systems, orbital-based projector approaches that determine a nonlocal action of the embedding potential onto the fragment electrons. By using an auxiliary fragment, the method maintains a purely electron-density-dependent embedding potential, substantially lessening the cost and leading to simpler implementation. In this work, we demonstrate the utility of our capped-DFET and ensuing capped embedded CW method in two contrasting systems, namely, an organic molecule and an ionic metal oxide cluster.

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↗
DOE OSTI2022

Benchmarking embedded chain breaking in quantum annealing*

Quantum annealing solves combinatorial optimization problems by finding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of the quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Antenna With Embedded Die in Glass Interposer for 6G Wireless Applications

This article presents the antenna-integrated glass interposer for D-band 6G wireless applications using dieembedding technology. Here, we implement the die-embedded package on glass substrates and characterize the electrical performance in the D-band. The electrical characterization employs embedded test dies with the 50-Ω ground–signal–ground (GSG) ports and coplanar waveguides. We achieve low-loss die-to-package transitions by using staggered dielectric vias, which are compared with the transitions of wire-bonding and flip-chip assembly. This article provides detailed information on the design, modeling, fabrication, and characterization of the die-to-package interconnects. This article also demonstrates the integration of microstrip patch antenna array and embedded dies in the D-band. The results show superior electrical performance provided by the die-embedded glass interposer. The die-to-package interconnect exhibits good matching (less than —10-dB S11) and low loss (0.2-dB loss) in the D-band. The integrated 1 × 8 patch antenna array shows 11.6-dB broadside gain and good matching with the embedded die. In addition, by using a temporary carrier, the antenna-integrated glass interposer also has great potential for further heterogeneous integration and thermal management.

42 ENGINEERING↗
DOE OSTI2022

A PCB-Embedded 1.2 kV SiC MOSFET Half-Bridge Package for a 22 kW AC–DC Converter

This article presents the design and analysis of a double-side-cooled printed circuit board (PCB) embedded silicon carbide (SiC) MOSFET half-bridge package with low loop inductances and an integrated gate driver. The 1.2 kV SiC MOSFET dies used in the half-bridge package are embedded in the PCB using AT&S's patented technique. The dies are cooled and electrically connected to traces in the PCB through copper-filled microvias. The design methodology accounts for both electrical and thermal performance, limiting the power-loop inductance to 2.3 nH and the maximum package temperature to less than the 175 °C limit. The integration of the gate drive circuitry allows for a high power density and 2.2 nH gate-loop inductances. At 0.12 K/W, the measured junction-to-case thermal resistance with double-sided cooling is 57% lower than that of a TO-247 package. Under similar operating conditions, the PCB-embedded half-bridge package also achieves a 5.6 times lower voltage overshoot and a 0.5% higher peak efficiency than a TO-247-based half-bridge. This article reports the first demonstration of PCB-embedded 1.2 kV SiC MOSFET packages in buck, boost, and ac–dc converters. Furthermore, the prototype three-phase ac–dc converter for an electric vehicle on-board charger is composed of six PCB-embedded half-bridge packages and achieves an efficiency of 98.2% and a power density of 182 W/in 3 .

42 ENGINEERING↗
DOE OSTI2022

Conformal conserved currents in embedding space

We study conformal conserved currents in arbitrary irreducible representations of the Lorentz group using the embedding space formalism. With the help of the operator product expansion, we first show that conservation conditions can be fully investigated by considering only two- and three-point correlation functions. We then find an explicitly conformally-covariant differential operator in embedding space that implements conservation based on the standard position space operator product expansion differential operator ∂μ, although the latter does not uplift to embedding space covariantly. The differential operator in embedding space that imposes conservation is the same differential operator $D_{ijA}$ used in the operator product expansion in embedding space. We provide several examples including conserved currents in irreducible representations that are not symmetric and traceless. With an eye on four-point conformal bootstrap equations for four conserved vector currents $\langle JJJJ\rangle$ and four energy-momentum tensors $\langle TTTT\rangle$, we mostly focus on conservation conditions for $\langle JJ\mathcal{O}\rangle$ and $\langle TT\mathcal{O}\rangle$. Finally, we reproduce and extend the consequences of conformal Ward identities at coincident points by determining three-point coefficients in terms of charges.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Relativistic two-component projection-based quantum embedding for open-shell systems

In this work, we present a relativistic quantum embedding formalism capable of variationally treating relativistic effects, including scalar-relativity and spin–orbit coupling. We extend density functional theory (DFT)-in-DFT projection-based quantum embedding to a relativistic two-component formalism, where the full spin magnetization vector form is retained throughout the embedding treatment. To benchmark various relativistic embedding schemes, spin–orbit splitting of the nominally t 2g valence manifold of W(CO) 6 , exchange coupling of [(H 3 N) 4 Cr(OH) 2 Cr(NH 3 ) 4 ] 4+ , and the dissociation potential curve of WF 6 are investigated. The relativistic embedding formalism introduced in this work is well suited for efficient modeling of open-shell systems containing late transition metal, lanthanide, and actinide molecular complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding

In this work, we present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi 4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF 4 ionic liquid were considered as "simple" and "complex" solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF 4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

eCC++ : A Compiler Construction Framework for Embedded Domain-Specific Languages

eCC++ is a new compiler construction framework for embedding domain-specific programming languages within C++. That is, the host language is C++, the guest language is the DSL to be embedded in C++, and eCC++ is the tool that enables the embedding. The eCC++ framework is composed of three main components: a front-end, an API for program verification based on a functional and declarative intermediate representation (IR), and a Multi-Level Intermediate Representation (MLIR) code generator. The eCC++ front-end consists of a library of C++ classes and operators that can be used to define the guest language. Guest sources are compiled with any standard C++ compiler, and when run, the resulting executable generates an eCC++ IR representation of the program, which can be verified within the eCC++ framework. Finally, eCC++ allows for high-level and domain-specific optimizations before generating MLIR. In summary, eCC++ aims to act as a generic front-end that enables embedding guest languages into C++, and provides necessary compiler technology for program verification, targeting the existing capabilities in the MLIR infrastructure. The paper evaluates the eCC++ expressiveness and usability describing the process of embedding GraphIt, a high-performance graph language in C++.

Gonzalez Tallada, Marc↗
DOE OSTI2022

Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.

Liu, Minzhao↗
DOE OSTI2020

Graphite-Embedded High-Performance Insulated Metal Substrate for Wide-Bandgap Power Modules

Emerging wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (MOSFETs) and gallium nitride high-electron-mobility transistors can handle high power in reduced semiconductor areas better than conventional Si-based devices owing to superior material properties. With increased power loss density in a WBG-based converter and reduced die size in power modules, thermal management of power devices must be optimized for high performance. Here, we present a graphite-embedded insulated metal substrate (thermally-annealed-pyrolytic-graphite-embedded insulated metal substrate—IMSwTPG) designed for WBG power modules. Theoretical thermal performance analysis of graphite-embedded metal cores is presented, with design details for IMSwTPG with embedded graphite to replace a direct-bonded copper (DBC) substrate. The proposed IMSwTPG is compared with an aluminum nitride-based DBC substrate using finite-element thermal analysis for steady-state and transient thermal performance. The solutions’ thermal performances are compared under different coolant temperature and thermal loading conditions, and the proposed substrate's electrical performance is validated with static and dynamic characterization. Using graphite-embedded substrates, junction-to-case thermal resistance of SiC MOSFETs can be reduced up to 17%, and device current density can be increased by 10%, regardless of the thermal management strategy used to cool the substrate. Reduced transient thermal impedance of up to 40% of dies owing to increased heat capacity is validated in transient thermal simulations and experiments. The half-bridge power module's electrical performance is evaluated for on -state resistance, switching performance, and switching loss at three junction temperature conditions. The proposed substrate solution has minimal impact on conduction and switching performance of SiC MOSFETs.

42 ENGINEERING↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2023

Projective embedding of dynamical systems: Uniform mean field equations

Herein we study embeddings of continuous dynamical systems in larger dimensions via projector operators. We call this technique PEDS, projective embedding of dynamical systems, as the stable fixed point of the original system dynamics are recovered via projection from the higher dimensional space. In this paper we provide a general definition and prove that for a particular type of rank-1 projector operator, the uniform mean field projector, the equations of motion become a mean field approximation of the dynamical system. While in general the embedding depends on a specified variable ordering, the same is not true for the uniform mean field projector. We prove a variety of results on the relationship between the spectrum of the Jacobian for fixed points in the original and in the embedded system. Direct applications of PEDS can be non-convex optimization and machine learning.

97 MATHEMATICS AND COMPUTING↗
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↗