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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Next Steps: Laying the Groundwork for Bundle Protocol v7

Delay/Disruption Tolerant Networking (DTN) is a networking model and protocol suite that extends the terrestrial internet to the challenging communication environments of space. These environments are typically subject to frequent disruptions, which can cause delays or errors. DTN protects data transmission by wrapping data into bundles (similar to Internet Protocol packets), storing them until a connection can be established between two nodes (similar to terrestrial routers or computers), and forwarding them to their destinations. Bundle Protocol (BP) is responsible for generating those bundles and creates the transport layer of DTN, much like how Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) create the transport layer of the Internet Protocol. BPv6 is the current, accepted version of the Bundle Protocol standard. However, recent missions and test implementations have revealed missing components and areas for improvement in the standard. Using lessons learned from NASA missions and gathering inspiration from the Internet Protocol, BPv7 is intended to be a more robust Bundle Protocol that improves upon its predecessor and increases the technology readiness level of the DTN architecture. The DTN Standard Interface Design team, a sub-team of the DTN Infusion Project at the Goddard Space Flight Center, worked to create a dictionary of terms for bundle components and functional decomposition of the protocol. These efforts aided in the standardization of BP interfaces, something missing from BPv6, and supported parallel network management and configuration work. This standardization will ultimately contribute to LunaNet (a lunar communications and navigation architecture that will bring networking, positioning, navigation, timing and science services to the Moon), the Solar System Internet (SSI), and expand crewed and uncrewed space exploration opportunities.

DTN↗
NASA NTRSDate not supplied

Next Steps: Laying the Groundwork for Bundle Protocol v7

Delay/Disruption Tolerant Networking (DTN) is a networking model and protocol suite that extends the terrestrial internet to the challenging communication environments of space. These environments are typically subject to frequent disruptions, which can cause delays or errors. DTN protects data transmission by wrapping data into bundles (similar to Internet Protocol packets), storing them until a connection can be established between two nodes (similar to terrestrial routers or computers), and forwarding them to their destinations. Bundle Protocol (BP) is responsible for generating those bundles and creates the transport layer of DTN, much like how Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) create the transport layer of the Internet Protocol. BPv6 is the current, accepted version of the Bundle Protocol standard. However, recent missions and test implementations have revealed missing components and areas for improvement in the standard. Using lessons learned from NASA missions and gathering inspiration from the Internet Protocol, BPv7 is intended to be a more robust Bundle Protocol that improves upon its predecessor and increases the technology readiness level of the DTN architecture. The DTN Standard Interface Design team, a sub-team of the DTN Infusion Project at the Goddard Space Flight Center, worked to create a dictionary of terms for bundle components and functional decomposition of the protocol. These efforts aided in the standardization of BP interfaces, something missing from BPv6, and supported parallel network management and configuration work. This standardization will ultimately contribute to LunaNet (a lunar communications and navigation architecture that will bring networking, positioning, navigation, timing and science services to the Moon), the Solar System Internet (SSI), and expand crewed and uncrewed space exploration opportunities.

dtn↗
NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Neural Networks↗
DOE OSTI2026

The 2025 “Hacking Limnology” Workshop Series and DSOS Virtual Summit: A Half Decade of Data‐Intensive Aquatic Science

The 5th Aquatic Ecosystem MOdeling Network—Junior (AEMON-J) “Hacking Limnology” Workshop and 6th Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) convened 21–25 July 2025. As in previous years (Fig. 1; Meyer and Zwart 2020; Meyer et al. 2021b, 2021c, 2022, 2024), the virtual workshops and summit were free of charge, the content was formatted to allow for broad engagement from a globally distributed audience, and workshop materials and recordings were made available on the AEMON-J/DSOS archive (Meyer et al. 2021a). In contrast to previous years, which primarily focused on inland aquatic ecosystems, this year's workshops and summit showcased a notable plurality of ecosystem types, with workshops spanning marine, riverine, and lacustrine environments. The weeklong event brought together researchers and practitioners interested in the nexus of data science, open science, and the aquatic sciences, hosting between 47 and 65 attendees at a single time and a higher number of registrants (n = 389), who might opt to access the material asynchronously.

Meyer, Michael F. [US Geological Survey, Portland,↗
DOE OSTI2021

Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-task and Inductive Transfer Learning

Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Holevo information and ensemble theory of gravity

Holevo information is an upper bound for the accessible classical information of an ensemble of quantum states. In this work, we use Holevo information to investigate the ensemble theory interpretation of quantum gravity. We study the Holevo information in random tensor network states, where the random parameters are the random tensors at each vertex. Based on the results in random tensor network models, we propose a conjecture on the holographic bulk formula of the Holevo information in the gravity case. As concrete examples of holographic systems, we compute the Holevo information in the ensemble of thermal states and thermo-field double states in the Sachdev-Ye-Kitaev model. The results are consistent with our conjecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Approximate Bacon-Shor code and holography

We explicitly construct a class of holographic quantum error correction codes with non-trivial centers in the code subalgebra. Specifically, we use the Bacon-Shor codes and perfect tensors to construct a gauge code (or a stabilizer code with gauge-fixing), which we call the holographic hybrid code. This code admits a local log-depth encoding/decoding circuit, and can be represented as a holographic tensor network which satisfies an analog of the Ryu-Takayanagi formula and reproduces features of the sub-region duality. We then construct approximate versions of the holographic hybrid codes by “skewing” the code subspace, where the size of skewing is analogous to the size of the gravitational constant in holography. These approximate hybrid codes are not necessarily stabilizer codes, but they can be expressed as the superposition of holographic tensor networks that are stabilizer codes. For such constructions, different logical states, representing different bulk matter content, can “back-react” on the emergent geometry, resembling a key feature of gravity. The locality of the bulk degrees of freedom becomes subspace-dependent and approximate. Such subspace-dependence is manifest from the point of view of the “entanglement wedge” and bulk operator reconstruction from the boundary. Exact complementary error correction breaks down for certain bipartition of the boundary degrees of freedom; however, a limited, state-dependent form is preserved for particular subspaces. We also construct an example where the connected two-point correlation functions can have a power-law decay. Coupled with known constraints from holography, a weakly back-reacting bulk also forces these skewed tensor network models to the “large N limit” where they are built by concatenating a large N number of copies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The Python’s Lunch: geometric obstructions to decoding Hawking radiation

According to Harlow and Hayden [ arXiv:1301.4504 ] the task of distilling information out of Hawking radiation appears to be computationally hard despite the fact that the quantum state of the black hole and its radiation is relatively un-complex. We trace this computational difficulty to a geometric obstruction in the Einstein-Rosen bridge connecting the black hole and its radiation. Inspired by tensor network models, we conjecture a precise formula relating the computational hardness of distilling information to geometric properties of the wormhole — specifically to the exponential of the difference in generalized entropies between the two non-minimal quantum extremal surfaces that constitute the obstruction. Due to its shape, we call this obstruction the ‘Python’s Lunch’, in analogy to the reptile’s postprandial bulge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Flat entanglement spectra in fixed-area states of quantum gravity

We use the Einstein-Hilbert gravitational path integral to investigate gravitational entanglement at leading order O(1/G). We argue that semiclassical states prepared by a Euclidean path integral have the property that projecting them onto a subspace in which the Ryu-Takayanagi or Hubeny-Rangamani-Takayanagi surface has definite area gives a state with a flat entanglement spectrum at this order in gravitational perturbation theory. This means that the reduced density matrix can be approximated as proportional to the identity to the extent that its Renyi entropies Sn are independent of n at this order. The n-dependence of Sn in more general states then arises from sums over the RT/HRT-area, which are generally dominated by different values of this area for each n. This provides a simple picture of gravitational entanglement, bolsters the connection between holographic systems and tensor network models, clarifies the bulk interpretation of algebraic centers which arise in the quantum errorcorrecting description of holography, and strengthens the connection between bulk and boundary modular Hamiltonians described by Jafferis, Lewkowycz, Maldacena, and Suh.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2024

Multilabel proportion prediction and out-of-distribution detection on gamma spectra of short-lived fission products

In the machine learning problem of multilabel classification, the objective is to determine for each test instance which classes the instance belongs to. In this work, we consider an extension of multilabel classification, called multilabel proportion prediction, in the context of radioisotope identification (RIID) using gamma spectra data. We aim to not only predict radioisotope proportions, but also identify out-of-distribution (OOD) spectra. We achieve this goal by viewing gamma spectra as discrete probability distributions, and based on this perspective, we develop a custom semi-supervised loss function that combines a traditional supervised loss with an unsupervised reconstruction error function. Our approach was motivated by its application to the analysis of short-lived fission products from spent nuclear fuel. In particular, we demonstrate that a neural network model trained with our loss function can successfully predict the relative proportions of 37 radioisotopes simultaneously. The model trained with synthetic data was then applied to measurements taken by Pacific Northwest National Laboratory (PNNL) to conduct analysis typically done by subject-matter experts. Here, we also extend our approach to successfully identify when measurements are OOD, and thus should not be trusted, whether due to the presence of a novel source or novel proportions.

Anomaly detection↗
DOE OSTI2024

Design and evaluation of a dilute flow particle-to-air heat exchanger for energy storage applications

The use of inert and redox-active particles for high-temperature energy storage requires the development of components that can efficiently transfer energy to high-pressure working fluids like supercritical carbon dioxide (sCO 2 ). Dilute flow reactors can enable high working fluid outlet temperatures and minimal parasitic losses compared to moving packed bed and fluidized bed reactors. This research uses both computational and experimental methods to explore the design trade-offs and practical challenges of a novel component for transferring energy from dilute flows of hot, reduced metal oxide (MO x ) particles to sCO 2 in tubes. A discretized thermal resistance network model, which accounts for particle hydrodynamics, multi-mode heat transfer, and reaction equilibrium, guides the design of a prototype device. This device is experimentally tested with a surrogate heat transfer fluids and inert particle temperatures up to 400°C and a heat duty exceeding 1 kW. The data are used to validate the thermal hydraulic sub-models, allowing for the simulation of reacting particle scenarios. Under nominal design conditions, the flow rate of reactive particles is predicted to be 30% lower than that of inert particles for the same energy recovered, with over 70% of the stored particle energy transferred to the sCO 2 . Furthermore, these findings can inform the design of more efficient energy recovery reactors for particle-based systems and can be integrated into system-level concentrated solar power models with thermal storage to optimize operating conditions.

14 SOLAR ENERGY↗
DOE OSTI2026

Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage

Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multi-modal characterization and simulation of human epileptic circuitry

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, “early-disease-like” state.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Incorporating PCM-enabled thermal energy storage into 3D printable cementitious composites

This paper delineates the feasibility of incorporating microencapsulated phase change materials (mPCM) into 3D printable cementitious composite materials. A comprehensive experimental program was carried out to evaluate the impacts of mPCM on the printability, microstructures, mechanical and thermal properties of cementitious 3D printing ‘inks’. Results showed that the mPCM affected the printability of the cementitious ink material based on its physical properties (e.g., particle size) and volume loading – at lower volume loadings, mPCM increased the flowability of the cementitious ink material while leading to increased compressive strength and thermal conductivity for the hardened printed material. However, further increase in mPCM dosage led to a decrease in printability and, therefore, decrease in compressive strength and thermal conductivity as compared to the reference mixture. Here the results also showed that the inclusion of mPCM influence the printing parameters. In general, the inclusion of higher volume contents of mPCM necessitates a higher extrusion rate to achieve a desirable extrudability. Lastly, a thermal network model was formulated for 3D printed mPCM charged building components (e.g., wall). The study shows that microencapsulated PCM materials have good potential to be used in 3D printable cementitious mixtures for improving the thermal and energy performance of 3D printed buildings.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗
DOE OSTI2023

Stochastic generation of electrolyzer anode catalyst layers

Here, we introduce a stochastic methodology to reproduce the complex pore structure observed in commercial iridium catalyst layers. This method preserves the α pore (pores smaller than 250 nm) and β pore (pores greater than or equal to 250 nm) regions of the catalyst layer. The morphology of the generated materials was validated by comparing the pore size distributions of generated materials against those obtained from commercial materials imaged using x-ray nano computed tomography. We further demonstrate that the pore size distributions of the generated materials are statistically indistinguishable from the imaged catalyst layers, indicating that the stochastic methodology is capable of accurately reproducing catalyst layer morphology. Pore network modelling was conducted on the generated catalyst materials to simulate single-phase permeability, electrical conductivity, and ionic conductivity, and these properties were found to be within experimentally measured ranges for electrolyzer catalyst layers. Additionally, simulations were performed on the generated materials with varying ionomer and iridium catalyst loadings. As the ionomer loading is added, proton conductivity increases exponentially, which demonstrates the importance of optimizing ionomer loading, considering that these effects will be exacerbated in the hydration and temperature conditions of operating electrolyzers. The stochastic material generation method presented in this work is a powerful tool for the development of novel low loading catalyst layers, where the effect of various structural parameters on electrolyzer performance characteristics can be explored.

36 MATERIALS SCIENCE↗