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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TU-F-17A-07: Real-Time Personalized Margins

Purpose: To maximize normal tissue sparing for treatments requiring motion encompassing margins. Motion mitigation techniques including DMLC or couch tracking can freeze tumor motion within the treatment aperture potentially allowing for smaller treatment margins and thus better sparing of normal tissue. To enable for a safe application of this concept in the clinic we propose adapting margins dynamically in real-time during radiotherapy delivery based on personalized tumor localization confidence. To demonstrate technical feasibility we present a phantom study. Methods: We utilize a realistic anthropomorphic dynamic thorax phantom with a lung tumor model embedded close to the spine. The tumor, a 3D-printout of a patient's GTV, is moved 15mm peak-to-peak by diaphragm compression and monitored by continuous EPID imaging in real-time. Two treatment apertures are created for each beam, one representing ITV -based and the other GTV-based margin expansion. A soft tissue localization (STiL) algorithm utilizing the continuous EPID images is employed to freeze tumor motion within the treatment aperture by means of DMLC tracking. Depending on a tracking confidence measure (TCM), the treatment aperture is adjusted between the ITV and the GTV leaf. Results: We successfully demonstrate real-time personalized margin adjustment in a phantom study. We measured a system latency of about 250 ms which we compensated by utilizing a respiratory motion prediction algorithm (ridge regression). With prediction in place we observe tracking accuracies better than 1mm. For TCM=0 (as during startup) an ITV-based treatment aperture is chosen, for TCM=1 a GTV-based aperture and for 0<TCM<1 a linear interpolation between the two apertures is chosen. Conclusions: We have proposed and demonstrated a concept to safely shrink treatment margins during radiotherapy and adapt to tracking confidence in real-time. Normal tissue sparing is maximized. The worst case scenario results in delivering a plan with standard margins used in the clinic today.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Diphoton resonance in F-theory inspired flipped $\mathrm{SO}(10)$

Motivated by the di-photon excess at 750 GeV reported by the ATLAS and CMS experiments, we present an F-theory inspired flipped $\mathrm{SO}(10)$ model embedded in ε 6 . The low energy spectrum includes the three MSSM chiral families, vector-like colour triplets, several pairs of charged SU(2) L singlet fields (E c ,E¯ c ), as well as MSSM singlets, one or more of which could contribute to the di-photon resonance. As a result, a total decay width in the multi-GeV range can arise from couplings involving the singlet and MSSM fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Machine learning approaches for integrating multi-omics data to expand microbiome annotation

Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine learning approaches for integrating multi-omics data to expand microbiome annotation (Final Technical Report)

We fulfilled all original three aims of the proposal. Following the earlier release (during the first phase of the project at Montana) of software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes, we have nearly completed a second gap-filling tool that improves accuracy and explainability. We completed software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we completed a neural embedding model for identifying similarities between protein sequences based on amino-wise latent vectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Facial Named Entity Recognition by Attention-Based Graph Convolutional Neural Network

In the realm of facial recognition and analysis, the ability to accurately cluster large datasets of facial images stands as a cornerstone for various applications, ranging from security surveillance to user biometric identification. This project evolves a novel approach to facial data clustering by embedding facial images into a high-dimensional vector space using an advanced embedding model trained on separate data and assumes a graph-like structure on the high-dimensional vectors. We find our method works significantly better than common shallow methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Knowledge Graph Entity Linking using Graph Embeddings

Details the use of a custom embedding model on knowledge graphs to aid in downstream natural language processing (NLP) models for Derivative Classification Assist. Motivations, algorithms, and results were discussed.

Mahesh, Aarav [Sandia National Laboratories (SNL-N↗
DOE OSTI2025

FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging

We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. This hardware realization transitions SpeckleNN from a prototypic model into a practical edge solution, optimized for running inference near the detector in high-throughput X-ray free-electron laser (XFEL) facilities, such as those found at the Linac Coherent Light Source (LCLS). To address the resource constraints inherent in FPGAs, we developed a more specialized version of SpeckleNN. The original model, which was designed for broader classification across multiple biological samples, comprised ~5.6 million parameters. The new implementation, while reducing the parameter count to 64.6K (a 98.8% reduction), focuses on maintaining the model's essential functionality for real-time operation, achieving an accuracy of 90%. Furthermore, we compressed the latent space from 128 to 50 dimensions. This implementation was demonstrated on the KCU1500 FPGA board, utilizing 71% of available DSPs, 75% of LUTs, and 48% of FFs, with an average power consumption of 9.4W according to the Vivado post-implementation report. The FPGA performed inference on a single image with a latency of 45.015 microseconds at a 200 MHz clock rate. In comparison, running the same inference on an NVIDIA A100 GPU resulted in an average power consumption of ~73W and an image processing latency of around 400 microseconds. Our FPGA-accelerated version of SpeckleNN demonstrated significant improvements, achieving an 8.9 × speedup and a 7.8 × reduction in power consumption compared to the GPU implementation. Key advancements include model specialization and dynamic weight loading through SNL, which eliminates the need for time-consuming FPGA design re-synthesis, allowing fast and continuous deployment of models (re)trained online. These innovations enable real-time adaptive classification and efficient vetoing of speckle patterns, making SpeckleNN more suited for deployment in XFEL facilities. This implementation has the potential to significantly accelerate SPI experiments and enhance adaptability to evolving experimental conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Investigation of the \(\gamma \)-ray Properties of the \(2^+\) States in \(^{14}\)C

The properties of the 2$^+_1$ and 2$^+_2$ excited states in 14 C were studied in an experiment conducted at Argonne National Laboratory. A 9 Be( 6 Li,pγ) fusion-evaporation reaction and the GRETINA-ORRUBA setup were employed to populate states of 14 C and detect γ-particle coincidence events. Finally, the precise determination of the 2$^+_1$ level energy, complemented by the estimation of the γ-ray branch of the 2$^+_2$ near-threshold state, will serve as a benchmark to test the Shell Model Embedded in the Continuum calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1985

Probabilistic Finite Element: Variational Theory

The goal of this research is to provide techniques which are cost-effective and enable the engineer to evaluate the effect of uncertainties in complex finite element models. Embedding the probabilistic aspects in a variational formulation is a natural approach. In addition, a variational approach to probabilistic finite elements enables it to be incorporated within standard finite element methodologies. Therefore, once the procedures are developed, they can easily be adapted to existing general purpose programs. Furthermore, the variational basis for these methods enables them to be adapted to a wide variety of structural elements and to provide a consistent basis for incorporating probabilistic features in many aspects of the structural problem. Tasks concluded include the theoretical development of probabilistic variational equations for structural dynamics, the development of efficient numerical algorithms for probabilistic sensitivity displacement and stress analysis, and integration of methodologies into a pilot computer code.

Belytschko, T.↗
NASA NTRS1998

GCM Studies on the Interactions Between Photosynthesis and Climate at Diurnal to Decadal Time Scales

Transpiration, a major component of total evaporation from vegetated surfaces, is an unavoidable consequence of photosynthetic carbon fixation. Because of limiting soil moisture and competition for solar radiation plants invest most of their fixed carbon into structural and hydraulic functions (roots and stems) and solar radiation absorption (leaves). These investments permit individuals to overshadow competitors and provide for transport of water from the soil to the leaves where photosynthesis and transpiration occur. Often low soil moisture or high evaporative demand limit the supply of water to leaves reducing photosynthesis and thus transpiration. The absorption of solar radiation for photosynthesis and dissipation of this energy via radiation, heat, mass and momentum fluxes represents the link between photosynthesis and climate. Recognition of these relationships has led to the development of hydro/energy balance models that are based on the physiological ecology of photosynthesis. We discuss an approach to study vegetation-climate interactions using photosynthesis-centric models embedded in a GCM. The rate at which a vegetated area transpires and photosynthesizes is determined by the physiological state of the vegetation, its amount and its type. The latter two are specified from global satellite data collected since 1982. Climate simulations have been carried out to study how this simulated climate system responds to changes in radiative forcing, physiological capacity, atmospheric CO2, vegetation type and variable vegetation cover observed from satellites during the 1980's. Results from these studies reveal significant feedbacks between the vegetation activity and climate. For example, vegetation cover and physiological activity increases cause the total latent heat flux and precipitation to increase while mean and maximum air temperatures decrease. The reverse occurs if cover or activity'decreases. In general climate response of a particular region was dominated by local processes but we also find evidence that plausible climate-vegetation scenarios lead to changes in global atmospheric circulation and strong non-local influences in some cases.

Collatz, G. James↗
NASA NTRS2008

Provenance in Data Interoperability for Multi-Sensor Intercomparison

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.

Lynnes, Chris↗
NASA NTRS2015

Advanced Booster Liquid Engine Combustion Stability

Combustion instability is a phenomenon in liquid rocket engines caused by complex coupling between the time-varying combustion processes and the fluid dynamics in the combustor. Consequences of the large pressure oscillations associated with combustion instability often cause significant hardware damage and can be catastrophic. The current combustion stability assessment tools are limited by the level of empiricism in many inputs and embedded models. This limited predictive capability creates significant uncertainty in stability assessments. This large uncertainty then increases hardware development costs due to heavy reliance on expensive and time-consuming testing.

Tucker, Kevin↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2021

An Introduction to Word Embeddings and Language Models

Language models have advanced at a phenomenal pace over the past decade. This document provides a short introduction to terminology, word embeddings (aka low-dimensional representations), and popular large-scale language models (LMs). Word embeddings are used to represent words as numerical vectors and are context-independent, meaning a word can only have a single representation (e.g., club can only be club sandwich, not golf club ). Language models can determine the probability of a given sequence of words occurring in a sentence and can provide context to distinguish between words and phrases that sound similar. LMs are context-dependent (e.g., club can be club sandwich or golf club ) and largely fall in two main classes – autoregressive and autoencoding models. Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the previous ones. Those models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. A typical example of such models is GPT, but others include GPT-2, GPT-3, CTLR, TRANSFORMER-XL, REFORMER, XLNET. Autoencoding models are pretrained by corrupting the input tokens in some way and trying to reconstruct the original sentence. They can be fine-tuned and achieve great results on many tasks such as text generation, but their most natural application is sentence classification or token classification. A typical example of such models is BERT, but others include ROBERTA, ALBERT, XML, XML-ROBERTA, FLAUBERT AND LONGFORMER.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Multiscale Modeling of Metallic Materials Containing Embedded Particles

Multiscale modeling at small length scales (10(exp -9) to 10(exp -3) m) is discussed for aluminum matrices with embedded particles. A configuration containing one particle surrounded by about 50 grains and subjected to uniform tension and lateral constraint is considered. The analyses are performed to better understand the effects of material configuration on the initiation and progression of debonding of the particles from the surrounding aluminum matrix. Configurational parameters considered include particle aspect ratio and orientation within the surrounding matrix. Both configurational parameters are shown to have a significant effect on the behavior of the materials as a whole. For elliptical particles with the major axis perpendicular to the direction of loading, a particle with a 1:1 aspect ratio completely debonds from the surrounding matrix at higher loads than particles with higher aspect ratios. As the particle major axis is aligned with the direction of the applied load, increasing amounts of load are required to completely debond the particles.

Phillips, Dawn R.↗
DOE OSTI2022

Experiments and Modeling of Proppant Embedment and Fracture Conductivity for the Caney Shale, Oklahoma, USA

ABSTRACT: The ultimate aim of hydraulic fracturing is to have a long and conductive flow path that extends from the wellbore into the formation. The effective fracture length is part of a hydraulically propped fracture which contributes to production. The difficulty in achieving economical production targets from shale reservoirs is at the forefront in many exploration companies. Fracture conductivity loss is related to; proppant embedment under depletion, proppant crushing, damage as a result of fracturing fluid, fines migration and proppant-pack permeability-damage are some of the factors that contribute to production decline after hydraulic fracturing in shale reservoirs. The Caney Shale is a calcareous organic-rich mudrock. Various studies have investigated the effect that clay on shale well productivity, however, there is currently no literature on the Caney shale in relation to horizontal wells; all available literature exists in vertical wells as well as on formations of the Caney that are shallow in comparison to an emerging play which is twice the depth. In this paper we investigate stress-dependent fracture conductivity of proppant-filled fractures and proppant embedment in Caney shale through laboratory and modeling studies. API fracture conductivity tests were conducted using 2% KCl on five locations within the Caney shale that consisted of selecting three brittle(reservoir) zones and two ductile zones. Confining pressures range from 1,000 psi to 12,000 psi at 210°F. Conductivity, permeability as well as embedment were measured during the test. Our experimental results have confirmed that improved fracture conductivity is attributed to; proppant size, the increase in porosity of the proppant pack, closure pressure changes and the reduction in fracture conductivity are a function of many factors such as fracture closure stress. The findings from this study could help the stimulation design by providing new insights into the critical factors that are to be determined to facilitate the choice of proppants as well as fracturing fluids for long term production and recovery from shale reservoirs. 1 INTRODUCTION The development of low permeability formations, like shales, has been aided by hydraulic fracturing of horizontal wells (Radonjic et al., 2020). Hydraulic fracturing fluid is injected at a high pressure to induce tensile fractures that can link to and stimulate natural fractures (Katende et al., 2021a,b). Preserving adequate conductivity in hydraulic fractures over the life of the wells is required for economic production; nevertheless, conserving such conductivity can be difficult in some circumstances, particularly in soft, clay-rich formations (Wang et al., 2021). Proppant particles help to keep the fractures open when the pumping stops and the fracturing fluid returns to the wellbore, producing one or more propped hydraulic fractures of varying length, breadth, and height (Katende et al., 2021a). The proppant pack within the hydraulic fracture boosts well output by providing a greater permeability flowpath for hydrocarbons (Duenckel et al., 2016). Proppant in the fracture is under complicated stress conditions, and the interplay between the rock formation and the proppant pack has a significant impact on proppant-pack permeability (Karazincir et al., 2019). Proppant may be embedded (Katende et al., 2021a) in the rock or crushed into small pieces if the proppant size and strength characteristics are not specified appropriately, resulting in a loss in proppant-pack permeability and fracture aperture, and consequently a fall in well output.

Katende, A.↗
DOE OSTI2018

A hybrid embedded discrete fracture model for simulating tight porous media with complex fracture systems

Numerical simulation considering complex fracture networks in tight oil reservoir becomes a hot issue during these years. Recently, embedded discrete fracture model (EDFM) shows great advantages over the DFM approaches in both calculation efficiency and simplicity of simulation workflow. EDFM has been widely used in tight oil/gas natural energy or enhanced oil recovery (EOR) development. If a large number of small-scale natural fractures exist in reservoir, the calculation efficiency of EDFM method may decrease. A new hybrid simulation model combining the EDFM and dual porosity (DP) model is proposed. The large-scale fractures are dealt with EDFM method while small scale fractures are dealt with DP model. Considering the low permeability of matrix, transient transfer between matrix and natural fracture is considered. The proposed hybrid model is validated compared with the local grid refinement (LGR) model and EDFM-MINC model, and the difference between transient transfer and pseudo-steady-state transfer effect on production is presented. Finally, two test cases are presented, and the sensitivity of some key parameters are investigated. Result shows that the new simulation model has higher calculation efficiency than LGR and EDFM-MINC models. Meanwhile, it honors a good accuracy. The pseudo-steady-state transfer between matrix and natural fracture leads to big error in tight oil production performance analysis both in single phase and two phases flow. Low matrix permeability and big matrix size increase the production difference between pseudo-steady-state transfer and transient transfer models. The proposed model can simulate reservoirs considering stochastic permeability distribution.

58 GEOSCIENCES↗