Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Space Network”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗
DOE OSTI2020

Whose Gas is it anyway? Differentiating the Source of a Large Soil Vapor Plume beneath Two Adjacent Waste Sites - 20487

DOE contractor CH2M Hill Plateau Remediation Company is currently responsible for conducting groundwater contamination monitoring at several RCRA treatment, storage, and disposal units located on the Hanford Site in Richland, Washington State. The Nonradioactive Dangerous Waste Landfill treatment, storage, and disposal unit presents a distinct groundwater monitoring problem because of a large multi-contaminant soil vapor plume beneath it that is a likely source of low-level volatile organic compound groundwater contamination. Adjacent to Nonradioactive Dangerous Waste Landfill is the Solid Waste Landfill. Volatile organic compounds are inventory components of both the Nonradioactive Dangerous Waste Landfill and the Solid Waste Landfill. Therefore, it is possible that both sites could be contributing to the soil vapor plume. For regulatory purposes, it is important to differentiate which site is the primary contributor of volatile organic compounds to the plume. An approach was developed to identify the primary volatile organic compound source of the soil vapor plume beneath Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. The site conceptual model hypothesis of vapor-phase volatile organic compound transport to the dissolved phase in groundwater was tested by a simple mathematical model of vapor/liquid equilibrium concentrations at the groundwater/air interface. Once it was shown that vapor-phase volatile organic compound transport to groundwater was a valid conceptual model for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill, spatial and statistical methods were used to determine the primary site contributing to the majority of volatile organic compounds to the soil vapor plume. Average groundwater chloroform, tetrachloroethene, and trichloroethene concentrations from Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring network wells were plotted on maps of the facilities and immediate vicinities and compared to soil vapor sampling probe locations. Principal component analysis and mixing ratios were used to identify source contributions of each treatment, storage, and disposal unit to the plume. Results of the vapor/liquid equilibrium concentrations mathematical model showed that transport phenomena outweigh steady-state equilibria. Estimated vapor/liquid equilibrium concentrations were considerably lower than soil vapor measurements. The results indicate that dynamic vadose zone and groundwater factors such as decreased vapor concentrations with depth, vapor dilution from dispersion in the vadose zone, and advective and diffusional volatile organic compound dilution in groundwater result in groundwater volatile organic compound concentrations much less than would be measured under steady-state equilibrium conditions. Site source contribution differentiation by principal component analysis and mixing ratios was inconclusive using actual soil gas data because of the similarity in concentration values in both datasets for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. Similar data populations suggest mixing of the vapor contributions from both sites by dispersion through the soil matrix pore spaces. However, when groundwater volatile organic compound data were compared between the Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring networks, Solid Waste Landfill mean concentrations were higher, suggesting more vapor-phase volatile organic compound transport to groundwater at those locations. Simulated volatile organic compound soil vapor and groundwater datasets created to test the methods developed for this study show that the method can be successful in source differentiation when significantly different datasets are compared. This paper will describe a method of testing a conceptual model for vapor-phase contaminant transport to groundwater and for differentiating site sources of contaminants comprising a mixed-constituent soil vapor plume. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Fully Inkjet‐Printed, 2D Materials‐Based Field‐Effect Transistor for Water Sensing

Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. Here we achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. Here, we report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

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

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning

Abstract Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNi x Mn y Co 1-x-y O 2 systems with high confidence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational design of highly signalling-active membrane receptors through solvent-mediated allosteric networks

Abstract Protein catalysis and allostery require the atomic-level orchestration and motion of residues and ligand, solvent and protein effector molecules. However, the ability to design protein activity through precise protein–solvent cooperative interactions has not yet been demonstrated. Here we report the design of 14 membrane receptors that catalyse G protein nucleotide exchange through diverse engineered allosteric pathways mediated by cooperative networks of intraprotein, protein–ligand and –solvent molecule interactions. Consistent with predictions, the designed protein activities correlated well with the level of plasticity of the networks at flexible transmembrane helical interfaces. Several designs displayed considerably enhanced thermostability and activity compared with related natural receptors. The most stable and active variant crystallized in an unforeseen signalling-active conformation, in excellent agreement with the design models. The allosteric network topologies of the best designs bear limited similarity to those of natural receptors and reveal an allosteric interaction space larger than previously inferred from natural proteins. The approach should prove useful for engineering proteins with novel complex protein binding, catalytic and signalling activities.

Chemistry↗
DOE OSTI2020

Hierarchical Phenomena in Multicomponent Liquids: Simulation Methods, Analysis, Chemistry

Complex, multicomponent, solutions have often been studied solely through the lens of specific applications of interest. Yet advances to both simulation methodologies (enhanced sampling, etc.) and analysis techniques (network analysis algorithms and others), are creating a trove of data that reveal transcending characteristics across vast compositional phase space. This perspective discusses technical considerations of the reliable and accurate simulations of complex solutions, followed by the advances to analysis algorithms that elucidate coupling of different length and timescale behavior (hierarchical phenomena). The different manifestations of hierarchical phenomena are presented across an array of solution environments, emphasizing fundamental and ongoing science questions. With a more advanced molecular understanding in hand, a quintessential application (solvent extraction) is discussed, where significant opportunities exist to re-imagine the technical scope of an established technology.

Servis, Michael J.↗
DOE OSTI2025

Dynamics of long-lived (axionic) domain walls and its cosmological implications

Here, we perform an updated analysis on a long-lived domain wall (DW) network, which may apply to a broad class of axion models. By simulating an axion-like scalar field on a 3D lattice and fitting an analytical model for the DW evolution, we identify the leading energy loss mechanisms of the DWs and compute the spectrum of axions emitted from the network. The contribution from the DWs to axion-like dark matter (DM) density is derived, with viable parameter space given. The application to both QCD axions and general axion-like particles (ALPs) is considered. Due to the new approaches taken, while our results bear consistency with earlier literature, notable discrepancies are also revealed, such as the specifics about DW decay rate which impacts the prediction for DM abundance, which may have a profound impact on axion phenomenology at large.

Cosmic strings↗
DOE OSTI2023

Entanglement area law for one-dimensional gauge theories and bosonic systems

Here, we prove an entanglement area law for a class of one-dimensional quantum systems involving infinite-dimensional local Hilbert spaces. This class of quantum systems includes bosonic models and lattice gauge theories in one spatial dimension. Our proof relies on results concerning the robustness of the ground state and spectral gap to the truncation of Hilbert space, applied within the approximate-ground-state projector (AGSP) framework. Our result provides theoretical justification for using tensor networks to study the ground-state properties of quantum systems with infinite local degrees of freedom.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Development of fracture diagnostic methods for fluid distribution based on quantitative interpretation of distributed acoustic sensing and distributed temperature sensing

Multistage hydraulic fracturing design on horizontal wells has significantly evolved with larger fluid volume, more fracturing stages, and tighter perforation cluster spacing to efficiently stimulate unconventional reservoirs. From the published field observations, the recent fracturing design results in complex fracture networks or swarm of fractures. Fracture treatment evaluation is extremely challenging in such a case because of the large amount of variables in well completion and stimulation design. Combined measurements from different technologies can help in fracture diagnosis. Fluid distribution, either during fracture injection or during production, directly relates to the stimulation efficiency at the cluster level and at the stage level. Because it is unlikely in the real world to distribute the injected fluid uniformly among all the clusters, we need diagnostic techniques to generate the flow profile along a lateral. Fiber-optic measurements, such as distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), are currently used to diagnose downhole flow conditions. This technology allows us to qualitatively confirm the fluid flow profile and other issues occurring downhole during fracturing such as leakage through plugs. For optimizing a fracturing design, we also need to understand how the design parameters are correlated with the stimulation efficiency. In this study, we combine two sets of models of DAS and DTS data interpretation for injected fluid volume distribution. The DAS is interpreted based on an empirical correlation between fluid flow rates and frequency band energy from the acoustic signals. The DTS is interpreted by performing temperature history match-based thermal energy conservation. Because of the completely different physics behind the interpretations, the confirmation of two interpretations provides confidence in fluid distribution.

Geochemistry & Geophysics↗
DOE OSTI2022

High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate

Integrated photonics operating at visible-near-infrared (VNIR) wavelengths offer scalable platforms for advancing optical systems for addressing atomic clocks, sensors, and quantum computers. The complexity of free-space control optics causes limited addressability of atoms and ions, and this remains an impediment on scalability and cost. Networks of Mach-Zehnder interferometers can overcome challenges in addressing atoms by providing high-bandwidth electro-optic control of multiple output beams. Here, we demonstrate a VNIR Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS voltage-level compatible full-swing voltage of 4.2 V and an electro-optic bandwidth of 2.7 GHz occupying only 0.35 mm 2 . Our waveguides exhibit 1.6 dB/cm propagation loss and our microring resonators have intrinsic quality factors of 4.4 × 10 5 . This specialized platform for VNIR integrated photonics can open new avenues for addressing large arrays of qubits with high precision and negligible cross-talk.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Generation and characterization of ultrabroadband polarization–frequency hyperentangled photons

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530–1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks.

42 ENGINEERING↗
DOE OSTI2026

Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Butter, Anja [Heidelberg Univ. (Germany); Sorbonne↗
DOE OSTI2023

Drones, Delivery Robots, Driverless Cars, and Intelligent Curbs for Increasing Energy Productivity of First/Last Mile Goods Movement

The objective of the project is to use empirical testing, life cycle assessment, and systems analysis to research and demonstrate energy improvement when using goods delivery drones, eVTOL, ground delivery robots and automated vehicles compared to a baseline network. The research will also develop proof-of-concept testing, a model, and simulation for a smart curb space as an intelligently-managed urban delivery zone demonstrating additional improvement in energy productivity.

Scherer, Sebastian↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDRs success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

accessibility↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDR's success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

access↗