Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “deep potential”

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.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Impact of a Cold Pool Parameterization on the Diurnal Cycle and Intraseasonal Variability in the GEOS AGCM

A gradual transition from shallow to deep convection may be important both to the continental diurnal cycle of precipitation and to the tropical Madden-Julian Oscillation. However, many existing convection parameterizations transition too readily, with corresponding diurnal and intraseasonal biases. High entrainment rates can be used to delay deep convection, but typically produce mean state biases; this is the "entrainment dilemma." Cold pools and sub-grid organization offer a potential solution to this dilemma, and recent work shows parameterized cold pools can effectively modulate deep convection, with improvements to the diurnal cycle and intraseasonal variability. Here we investigate the effects of a simple prognostic cold pool scheme coupled to the Grell-Freitas convection parameterization, in a set of global simulations with the NASA GEOS model. Air detrained from parameterized downdrafts is maintained in vertically resolved cold pools, which evolve with simplified dynamics. We test several options for cold pool feedbacks on convection, including modifications to deep convective entrainment rates, convective source air properties, and thermodynamic profiles, based on the level of cold pool activity. Cold pool impacts on the diurnal cycle are evaluated against TRMM, and moisture and moist static energy budgets are used to understand changes in tropical intraseasonal variability. Preliminary results show delays in the diurnal cycle of precipitation.

Arnold, Nathan↗
NASA NTRS2017

NASA's In Space Manufacturing Initiatives: Conquering the Challenges of In-Space Manufacturing

Current maintenance logistics strategy will not be effective for deep space exploration missions. ISM (In Space Manufacturing) offers the potential to: Significantly reduce maintenance logistics mass requirements; Enable the use of recycled materials and in-situ resources for more dramatic reductions in mass requirements; Enable flexibility, giving systems a broad capability to adapt to unanticipated circumstances; Mitigate risks that are not covered by current approaches to maintainability. Multiple projects are underway currently to develop and validate these capabilities for infusion into ISM exploration systems. ISS is a critical testbed for demonstrating ISM technologies, proving out these capabilities, and performing operational validation of deep space ISM applications. Developing and testing FabLab is a major milestone for springboard to DSG/Cis-lunar Space applications. ISM is a necessary paradigm shift in space operations – design for repair culture must be embraced. ISM team needs to be working with exploration system designers now to identify high-value application areas and influence design.

Clinton, R. G., Jr.↗
NASA NTRS1978

Human/computer control of undersea teleoperators

The potential of supervisory controlled teleoperators for accomplishment of manipulation and sensory tasks in deep ocean environments is discussed. Teleoperators and supervisory control are defined, the current problems of human divers are reviewed, and some assertions are made about why supervisory control has potential use to replace and extend human diver capabilities. The relative roles of man and computer and the variables involved in man-computer interaction are next discussed. Finally, a detailed description of a supervisory controlled teleoperator system, SUPERMAN, is presented.

Sheridan, T. B.↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2021

Injection data analysis using material balance time for CO 2 storage capacity estimation in deep closed saline aquifers

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO 2 . Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO 2 ) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred through adapting the concept of the material balance time. Material balance time is an approximate superposition time function developed to interpret production data from oil and gas wells operating at variable pressure/rate conditions during the boundary-dominated flow period. Using material balance techniques, the ultimate storage capacity is then estimated through linear extrapolation of the average pressure trend to the maximum allowable pressure the formation can withstand. The average pressure is not available in practice, but is can be obtained from the injection data. Two approaches are presented in this study to calculate the average pressure; namely the rigorous and the approximate approaches. Unlike the rigorous approach, the approximate approach does not require a prior knowledge of some reservoir properties (e.g. relative permeability, absolute permeability, formation porosity and thickness) to calculate the average pressure. To investigate its potential and reliability in analyzing CO 2 injection data, the proposed technique is applied to four synthetic cases representing different well operating conditions. Results indicate that the approximate approach consistently overestimates the actual (simulated) storage capacity as compared to the rigorous approach. The agreement - between the inferred and the simulated reservoir pore volume, and between the analytical and numerical estimates of storage capacity - validates the potential application of the technique to CO 2 storage in closed saline aquifers. The technique is further substantiated through application to a field data set utilized from a commercial-scale geological storage (CGS) project. Finally, field data interpretation shows that the proposed technique can be utilized to identify the degree of hydraulic continuity and reservoir compartmentalization within a target formation by interpreting the corresponding pressure and rate responses.

02 PETROLEUM↗
DOE OSTI2025

Certifying almost all quantum states with few single-qubit measurements

Certifying that an n -qubit state synthesized in the laboratory is close to a given target state is a fundamental task in quantum information science. However, existing rigorous protocols applicable to general target states have potentially prohibitive resource requirements in the form of either deep quantum circuits or exponentially many single-qubit measurements. Here we prove that almost all n -qubit target states, including those with exponential circuit complexity, can be certified from only O ( n 2 ) single-qubit measurements. Given access to the target state’s amplitudes, our protocol requires only O ( n 3 ) classical computation. This result is established by a technique that relates certification to the mixing time of a random walk. Our protocol has applications for benchmarking quantum systems, for optimizing quantum circuits to generate a desired target state and for learning and verifying neural networks, tensor networks and various other representations of quantum states using only single-qubit measurements. We show that such verified representations can be used to efficiently predict highly non-local properties of a synthesized state that would otherwise require an exponential number of measurements on the state. We demonstrate these applications in numerical experiments with up to 120 qubits and observe an advantage over existing methods such as cross-entropy benchmarking.

information theory and computation↗
DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗
DOE OSTI2016

Multiple stimulated emission fluorescence photoacoustic sensing and spectroscopy

Multiple stimulated emission fluorescence photoacoustic (MSEF-PA) phenomenon is demonstrated in this letter. Under simultaneous illumination of pumping light and stimulated emission light, the fluorescence emission process is speeded up by the stimulated emission effect. This leads to nonlinear enhancement of photoacoustic signal while the quantity of absorbed photons is more than that of fluorescent molecules illuminated by pumping light. The electronic states' specificity of fluorescent molecular can also be labelled by the MSEF-PA signals, which can potentially be used to obtain fluorescence excitation spectrum in deep scattering tissue with nonlinearly enhanced photoacoustic detection. In this preliminary study, the fluorescence excitation spectrum is reconstructed by MSEF-PA signals through sweeping the wavelength of exciting light, which confirms the theoretical derivation well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Strategies for p -type doping of ZnGeN 2

ZnGeN 2 has been proposed as an attractive semiconductor for a number of applications, but doping is largely unexplored. In this work, we examine the behavior of Li, Cu, Al, Ga, In, and C as candidate acceptors using hybrid density functional theory. Cu, In, and C give rise to deep acceptor levels, but Li, Al, or Ga could potentially lead to p -type conductivity. Al is particularly attractive since it has an ionization energy of 0.24 eV, comparable to Mg in GaN. However, self-compensation due to wrong-site incorporation is a serious issue. We demonstrate that co-doping with hydrogen can be used to overcome this problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Higgs-portal assisted Higgs inflation with a sizeable tensor-to-scalar ratio

We show that the Higgs portal interactions involving extra dark Higgs field can save generically the original Higgs inflation of the standard model (SM) from the problem of a deep non-SM vacuum in the SM Higgs potential. Specifically, we show that such interactions disconnect the top quark pole mass from inflationary observables and allow multi-dimensional parameter space to save the Higgs inflation, thanks to the additional parameters (the dark Higgs boson mass m {sub φ}, the mixing angle α between the SM Higgs H and dark Higgs Φ, and the mixed quartic coupling) affecting RG-running of the Higgs quartic coupling. The effect of Higgs portal interactions may lead to a larger tensor-to-scalar ratio, 0.08 ∼< r ∼< 0.1, by adjusting relevant parameters in wide ranges of α and m {sub φ}, some region of which can be probed at future colliders. Performing a numerical analysis we find an allowed region of parameters, matching the latest Planck data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Mars Extant Life: What's Next? Conference Report

On November 5–8, 2019, the ‘‘Mars Extant Life: What’s Next?’’ conference was convened in Carlsbad, New Mexico. The conference gathered a community of actively publishing experts in disciplines related to habitability and astrobiology. Primary conclusions are as follows: A significant subset of conference attendees concluded that there is a realistic possibility that Mars hosts indigenous microbial life. A powerful theme that permeated the conference is that the key to the search for martian extant life lies in identifying and exploring refugia (‘‘oases’’), where conditions are either permanently or episodically significantly more hospitable than average. Based on our existing knowledge of Mars, conference participants highlighted four potential martian refugium (not listed in priority order): Caves, Deep Subsurface, Ices, and Salts. The conference group did not attempt to reach a consensus prioritization of these candidate environments, but instead felt that a defensible prioritization would require a future competitive process. Within the context of these candidate environments, we identified a variety of geological search strategies that could narrow the search space. Additionally, we summarized a number of measurement techniques that could be used to detect evidence of extant life (if present). Again, it was not within the scope of the conference to prioritize these measurement techniques—that is best left for the competitive process. We specifically note that the number and sensitivity of detection methods that could be implemented if samples were returned to Earth greatly exceed the methodologies that could be used at Mars. Finally, important lessons to guide extant life search processes can be derived both from experiments carried out in terrestrial laboratories and analog field sites and from theoretical modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Topological nonmagnetic impurity states in topological Kondo insulators

We examine here the presence of nonmagnetic impurities in a hybridization gap model of a Kondo insulator which has band inversion. The model has been used to predict that SmB 6 is a topological insulator. We show that there are two types of nonmagnetic impurity states in a Kondo insulator. The type of states can be categorized as deep impurity states, similar to the impurity states in an ordinary metal, and impurity states which have energies within the hybridization gap. Unlike the deep impurity states which only form if the impurity potential exceeds a critical value of the order of the conduction bandwidth, the in-gap impurity states form for exceptionally small values of the impurity potential comparable to the hybridization gap. This finding may explain why Kondo insulators are found to be exceptionally sensitive to impurities. We demonstrate that these in-gap states are caused by band inversion and have properties similar to those expected for impurities in a topological insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A refraction correction for buried interfaces applied to in situ grazing-incidence X-ray diffraction studies on Pd electrodes

In situ characterization of electrochemical systems can provide deep insights into the structure of electrodes under applied potential. Grazing-incidence X-ray diffraction (GIXRD) is a particularly valuable tool owing to its ability to characterize the near-surface structure of electrodes through a layer of electrolyte, which is of paramount importance in surface-mediated processes such as catalysis and adsorption. Corrections for the refraction that occurs as an X-ray passes through an interface have been derived for a vacuum–material interface. In this work, a more general form of the refraction correction was developed which can be applied to buried interfaces, including liquid–solid interfaces. Furthermore, the correction is largest at incidence angles near the critical angle for the interface and decreases at angles larger and smaller than the critical angle. Effective optical constants are also introduced which can be used to calculate the critical angle for total external reflection at the interface. This correction is applied to GIXRD measurements of an aqueous electrolyte–Pd interface, demonstrating that the correction allows for the comparison of GIXRD measurements at multiple incidence angles. This work improves quantitative analysis of d-spacing values from GIXRD measurements of liquid–solid systems, facilitating the connection between electrochemical behavior and structure under in situ conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Transfer Learning approach to parking lot classification in aerial imagery

The importance of satellite imagery analysis has increased dramatically over the last several years, keeping pace with the rapid improvements seen in both remote sensing platforms and sensors. As this field expands, so too does the interest in using machine learning methods to automate parts of the imagery analyst’s workflow. In this paper we address one aspect of this challenge: the development of a method for the automatic extraction of parking lots from aerial imagery. To the best of our knowledge, there has been no prior work conducted on the development of an end-to-end pipeline for this particular task. Due to the limited size of our dataset and to accommodate the potentially limited size of future datasets, we propose a deep learning approach using transfer learning. This process hinges upon the use of state of the art Convolutional Neural Networks (CNNs), trained on general image classification datasets. These networks were then fine-tuned on our custom dataset, to establish a comprehensive benchmark for this task. Our method exhibits promising results for automatic parking lot extraction, and is generalizable enough to work with different input types, including high resolution aerial orthoimagery, satellite imagery, full motion video (FMV), and UAV imagery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗
DOE OSTI2014

Thermoacoustic imaging over large field of view for three-dimensional breast tumor localization: A phantom study

Purpose: Previous studies demonstrated that thermoacoustic imaging (TAI) has great potential for breast tumor detection. However, large field of view (FOV) imaging remains a long-standing challenge for three-dimensional (3D) breast tumor localization. Here, the authors propose a practical TAI system for noninvasive 3D localization of breast tumors with large FOV through the use of ultrashort microwave pulse (USMP). Methods: A USMP generator was employed for TAI. The energy density required for quality imaging and the corresponding microwave-to-acoustic conversion efficiency were compared with that of conventional TAI. The microwave energy distribution, imaging depth, resolution, and 3D imaging capabilities were then investigated. Finally, a breast phantom embedded with a laboratory-grown tumor was imaged to evaluate the FOV performance of the USMP TAI system, under a simulated clinical situation. Results: A radiation energy density equivalent to just 1.6%–2.2% of that for conventional submicrosecond microwave TAI was sufficient to obtain a thermoacoustic signal with the required signal-to-noise ratio. This result clearly demonstrated a significantly higher microwave-to-acoustic conversion efficiency of USMP TAI compared to that of conventional TAI. The USMP TAI system achieved 61 mm imaging depth and 12 × 12 cm{sup 2} microwave radiation area. The volumetric image of a copper target measured at depth of 4–6 cm matched well with the actual shape and the resolution reaches 230 μm. The TAI of the breast phantom was precisely localized to an accuracy of 0.1 cm over an 8 × 8 cm{sup 2} FOV. Conclusions: The experimental results demonstrated that the USMP TAI system offered significant potential for noninvasive clinical detection and 3D localization of deep breast tumors, with low microwave radiation dose and high spatial resolution over a sufficiently large FOV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-CAMPUS-JeP1-04: Evaluating DECT Vs SECT Range Differences in Proton Therapy Using Clinical Data

Purpose: Range uncertainty from X-ray CT number conversion to stopping power ratio (SPR) is one of the key factors limiting the potential of proton therapy. The large margins required for deep seated tumors degrade the organ sparing achievable with the technology. Of interest is the application of dual energy CT (DECT) to SPR estimation. In this planning study proton range differences between SECT and DECT have been quantified for brain cases. Methods: A last generation dual source DECT scanner was used to acquire SECT (150 kVp with Sn filtration) and DECT (additionally 90 kVp) scans of phantoms and 5 head trauma patients, acting as surrogate cancer patients. Phantom materials were characterized in terms of SPR in a particle beam to obtain reference values. IMPT treatment plans were generated on the basis of SECT and DECT SPR images for hypothetical brain tumors using a short and a long beam path. Range differences between SECT and DECT from plan recalculations were evaluated in beam-eye-view (BEV) by comparing the 80% isodose. Results: For the 18 phantom materials the SECT RMS SPR errors were 2.6% compared to 1.1% for DECT. Group median relative range differences between SECT and DECT plans were −1.0% for the short beam path over the 5 patients investigated in this study. For the long beam path the median difference was −1.4%. These relative range differences corresponded to −0.5 mm and −1.4 mm shifts respectively. Conclusion: This is the first study performing proton therapy treatment planning on DECT patient images. Important range differences of more than 1 mm were observed between SECT and DECT treatment plans, and DECT SPR accuracy was found superior on the basis of phantom measurements. While the patients investigated in this study did not have brain tumors, the findings we observed should apply to cancer patients. Deutsche Forschungsgemeinschaft (MAP); Bundesministerium fur Bildung und Forschung (01IB13001)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Terabase Metagenome Sequencing of Grassland Soil Microbiomes

ABSTRACT To enable an in-depth survey of the metabolic potential of complex soil microbiomes, we performed ultra-deep metagenome sequencing, collecting >1 Tb of sequence data from three grassland soils representing different precipitation regimes.

59 BASIC BIOLOGICAL SCIENCES↗