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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Implementation Options for the PROPEL Electrodynamic Tether Demonstration Mission

The PROPEL ("Propulsion using Electrodynamics") flight demonstration mission concept will demonstrate the use of an electrodynamic tether (EDT) for generating thrust, which will allow the propulsion system to overcome the limitations of the rocket equation. The mission concept has been developed by a team of government, industry, and academia partners led by NASA Marshall Space Flight Center (MSFC). PROPEL is being designed for versatility of the EDT system with multiple end users in mind and to be flexible with respect to platform. Previously, we reported on a comprehensive mission design for PROPEL with a mission duration of six months or longer with multiple mission goals including demonstration of significant boost, deboost, inclination change, and drag make-up activities. To explore a range of possible configurations, primarily driven by cost considerations, other mission concept designs have been pursued. In partnership with the NASA's Office of Chief Technologist (OCT) Game Changing Program, NASA MSFC Leadership, and the MSFC Advanced Concepts Office, a mission concept design was developed for a near-term EDT propulsion flight validation mission. The Electrodynamic Tether Propulsion Study (ETPS) defined an EDT propulsion system capable of very large delta-V for use on future missions developed by NASA, DoD, and commercial customers. To demonstrate the feasibility of an ETPS, the study focused on a space demonstration mission concept design with configuration of a pair of tethered satellite busses, one of which is the Japanese H-II Transfer Vehicle (HTV). The HTV would fly its standard ISS resupply mission. When resupply mission is complete, the ISS reconfigures and releases the HTV to perform the EDT experiment at safe orbital altitudes below the ISS. Though the focus of this particular mission concept design addresses a scenario involving the HTV or a similar vehicle, the propulsion system's capability is relevant to a number of applications, as noted above. The ETPS builds on prior work on long-life, failure-resistant, conducting tethers and includes an instrument suite with demonstrated heritage capable of performing necessary diagnostics to measure performance against predictions for a given system size (to be determined) and boost rate. Mission designs in other configurations and launch vehicle options are being developed such that the system can be demonstration should a flight opportunity be identified. We will report on past and ongoing implementation options for PROPEL.

Bilen, Sven G.↗
NASA NTRS2015

Spacecube: A Family of Reconfigurable Hybrid On-Board Science Data Processors

SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science data processing systems developed at the NASA Goddard Space Flight Center (GSFC). The goal of the SpaceCube program is to provide 10x to 100x improvements in on-board computing power while lowering relative power consumption and cost. SpaceCube is based on the Xilinx Virtex family of FPGAs, which include processor, FPGA logic and digital signal processing (DSP) resources. These processing elements are leveraged to produce a hybrid science data processing platform that accelerates the execution of algorithms by distributing computational functions to the most suitable elements. This approach enables the implementation of complex on-board functions that were previously limited to ground based systems, such as on-board product generation, data reduction, calibration, classification, eventfeature detection, data mining and real-time autonomous operations. The system is fully reconfigurable in flight, including data parameters, software and FPGA logic, through either ground commanding or autonomously in response to detected eventsfeatures in the instrument data stream.

reconfigurable computing↗
NASA NTRS2017

Inviscid/Boundary-Layer Aeroheating Approach for Integrated Vehicle Design

A typical entry vehicle design depends on the synthesis of many essential subsystems, including thermal protection system (TPS), structures, payload, avionics, and propulsion, among others. The ability to incorporate aerothermodynamic considerations and TPS design into the early design phase is crucial, as both are closely coupled to the vehicle's aerodynamics, shape and mass. In the preliminary design stage, reasonably accurate results with rapid turn-representative entry envelope was explored. Initial results suggest that for Mach numbers ranging from 9-20, a few inviscid solutions could reasonably sup- port surface heating predictions at Mach numbers variation of +/-2, altitudes variation of +/-10 to 20 kft, and angle-of-attack variation of +/- 5. Agreement with Navier-Stokes solutions was generally found to be within 10-15% for Mach number and altitude, and 20% for angle of attack. A smaller angle-of-attack increment than the 5 deg around times for parametric studies and quickly evolving configurations are necessary to steer design decisions. This investigation considers the use of an unstructured 3D inviscid code in conjunction with an integral boundary-layer method; the former providing the flow field solution and the latter the surface heating. Sensitivity studies for Mach number, angle of attack, and altitude, examine the feasibility of using this approach to populate a representative entry flight envelope based on a limited set of inviscid solutions. Each inviscid solution is used to generate surface heating over the nearby trajectory space. A subset of a considered in this study is recommended. Results of the angle-of-attack sensitivity studies show that smaller increments may be needed for better heating predictions. The approach is well suited for application to conceptual multidisciplinary design and analysis studies where transient aeroheating environments are critical for vehicle TPS and thermal design. Concurrent prediction of aeroheating environments, coupled with the use of unstructured methods, is considered enabling for TPS material selection and design in conceptual studies where vehicle mission, shape, and entry strategies evolve rapidly.

Lee, Esther↗
NASA NTRSDate not supplied

Pretest Comprehensive Analysis for the Urban Air Mobility Side-by-Side Test Stand

The Urban Air Mobility (UAM) Side-by-Side Test Stand (SBS) is a two-rotor test stand designed and built at NASA Ames Research Center under the Revolutionary Vertical Lift Technology (RVLT) Project. The SBS entered service in the wind tunnel in the latter part of 2021 and allows for the experimental analysis of rotor-rotor interactions for UAM vehicles of the side-by-side variety. The SBS will allow investigation of the effect of numerous variables such as rotor lateral separation, rotor collective pitch, model pitch angle (𝛼), rotor rotation direction, and flight speed. This paper presents performance predictions for the SBS. These predictions, which were generated using CAMRAD II, provided increased assurance of the safe operational limits of the system. Additionally, these results, when compared with future data, will be used for validation of the computational models.

Comprehensive Analysis↗
NASA NTRSDate not supplied

Trade Study Analysis of a Cryogenic Oxygen Architecture for Lunar Outpost Life Support

A trade study was performed to compare the use of cryogenic liquid oxygen (LOX) with high pressure gaseous oxygen (GOX) and electrolysis approaches for Lunar outpost life support, which consists of a surface habitat and pressurized rover. This study presents the mission details pertaining to a Lunar outpost architecture, discusses the viable concept of operations for each architecture, and compares the equivalent system mass (ESM) of the cryogenic LOX, high pressure GOX, and electrolysis approaches across different parameter trades, e.g. mission duration or extravehicular activity frequency, for the single and 10-year mission architectures. For a single nominal mission, high pressure GOX is favored for short missions (< 50 days); cryogenic LOX is favored for a wide-range of mission durations (50 – 270 days); and the electrolysis approach is favored for long missions (> 270 days). However, when considering a 10-year mission architecture, each additional resupply negatively impacts cryogenic LOX due to the additional replacement tankage. Thus, over a 10-year mission, an electrolysis approach, which can provide all life support O2 utilizing solely recovered H2O, appears to be favored over cryogenic LOX. However, a real electrolysis system may need resupplied H2O due to incomplete closure of the air revitalization loop. Thus, the cryogenic LOX approach was compared with the electrolysis approaches utilizing 100% resupplied or 100% recovered H2O to approximate the resupplied to recovered H2O ratio, i.e. the degree of loop closure, where one approach trades over the other. Additionally, gaps were identified, which are expected to affect the viability and trade of cryogenic LOX. These include the development of cryogenic pumps and vaporizers to generate high pressure GOX from LOX as well as understanding payload limitations which can affect O2 resupply.

ECLSS↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
DOE OSTI2023

Impact Analysis of Transitioning to Heat Pump Rooftop Units for the U.S. Commercial Building Stock: Preprint

20% of the U.S. commercial building sector's energy usage is from on-site combustion of fossil fuels for heating. Decarbonization will require electrification of these systems to meet climate goals, often by transitioning to heat pumps. Rooftop units (RTU) are the most prominent commercial building HVAC system type and should therefore be prioritized for decarbonization solutions. However, there is limited understanding of the impact on emissions that considers regional electricity generation methods, as well as the impact of other heat pump intricacies such as the effects of temperature on capacity/efficiency, defrost operation, realistic sizing methodologies, and supplementary heating. This study explores the effects of transitioning all conventional RTUs to high-performance heat pump RTUs for the U.S. commercial building stock. The analysis is performed using ComStock, DOE's calibrated model of the U.S. commercial building stock developed by NREL. Results show 9% and 17% reductions in stock aggregate energy consumption and GHG emissions, respectively. Furthermore, average heat pump operational details are presented by state. This analysis will help inform the transition to heat pump RTUs for the U.S. commercial building stock.

commercial building electrification↗
DOE OSTI2023

Towards constrained grid-forming control

Grid-forming converters are commonly envisioned to replace conventional synchronous generators as the cornerstone of future power systems. However, compared to synchronous generators, power converters and their power sources (e.g., renewable generation, energy storage) are subject to significant physical constraints (e.g., current limits, modulation limits). Grid-forming controls are commonly designed neglecting these constraints and subsequently augmented with limiters for a subset of constraints. In this work, we propose a systematic approach to constrained grid-forming control of two-level voltage source converters interfacing photovoltaics. We first formulate grid-forming control objectives and constraints of voltage source converters and photovoltaics in a one-step optimal grid-forming control problem. Next, we leverage primal-dual dynamics to obtain a dynamic feedback controller that approximates the solution of the one-step optimal grid-forming control problem. Finally, electromagnetic transient simulations are used to illustrate the results.

Gross, Dominic↗
DOE OSTI2024

Impact Analysis of Transitioning to Heat Pump Rooftop Units for the U.S. Commercial Building Stock

Twenty percent (25%) of the energy consumed by the U.S. commercial building sector is from on-site combustion of fossil fuels for space heating. Part of decarbonizing U.S. energy systems to meet climate initiatives will require electrification of space heating equipment, often by transitioning to heat pumps. Rooftop units (RTU) are the most prominent commercial building HVAC system type and should therefore be prioritized for electrification solutions. However, there is limited understanding of the impact on emissions when considering regional electricity generation methods, as well as the impact of ambient temperature on capacity and efficiency, defrost operation, realistic sizing methodologies, and supplementary heating on overall heat pump performance. This study explores the effects of transitioning all installed, existing RTUs to high-performance heat pump RTUs for the U.S. commercial building stock. The analysis is performed using ComStock (TM), the U.S. Department of Energy's calibrated model of the U.S. commercial building stock. Results show 10% and 9% reductions in stock aggregate energy consumption and greenhouse gas emissions, respectively. This analysis will help inform the transition to heat pump RTUs for the U.S. commercial building stock.

commercial building↗
DOE OSTI2025

Performance Evaluation of Different Parallel Programming Models in SCALE-Shift Sequences for Criticality and Shielding Applications [Abstract]

The SCALE code system has been widely used for nuclear criticality safety, reactor physics, radiation shielding, source term generation, and inventory analyses by researchers, industry, and regulatory bodies. Although limited support for shared- and distributed-memory parallel processing was introduced via C++ threading, OpenMP, and MPI, a hybrid parallel programming model with both distributed- and shared-memory parallelism has not been fully supported in the SCALE code system.

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Room temperature plasma-etching and surface passivation of far-ultraviolet Al mirrors using electron beam generated plasmas

The development of optical systems operating in the far ultraviolet range (FUV, λ=100-200 nm) is limited by the efficiency of passivated aluminum (Al) mirrors. Although it is presently possible to obtain high-reflectivity FUV mirrors through physical vapor deposition, the process involves deposition with substrates at high temperatures, which is technically challenging for large optical elements. A novel passivation procedure for bare Al mirrors is reported. The treatment consisted of using a low-temperature electron-beam generated plasma produced in a gas mixture of Ar and SF6 to etch away the native oxide layer from the Al film, while simultaneously promoting the generation of a thin aluminum tri-fluoride (AlF3) layer on the Al surface. In the first section we analyze the effect of varying both ion energy and SF6 concentration on the FUV reflectance, thickness, composition, and surface morphology of the resulting AlF3 protective layers. In the second section, the reflectivity of samples is optimized at selected important FUV wavelengths for astronomical observations. Notably, samples attained state-of-the-art reflectances of 75% at 108.5 nm (He Lyman γ), 91% at 121.6 nm (H Lyman α), 90% at 130.4 nm (OI), and of 95% at 155.0 nm (C IV). The stability over time of these passivated mirrors is also investigated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Qiao, Zhi (ORCID:0000000233928732)↗
NASA NTRS1983

Electrostrictive limit and focusing effects in pulsed photoacoustic detection

We give an analytical solution in the time and frequency domain for the cylindrical pressure wave generated by a laser pulse traveling in a liquid, which is valid over a wide range of laser beam dimensions and pulse durations. This leads to a simple prediction of the ultimate limitation set by the electrostrictive coupling and an easy analysis of the focusing effects on the photoacoustic signal. Two separate detection schemes were considered and show different behavior.

Heritier, J.-M.↗
DOE OSTI2025

Flavor patterns of fundamental particles from quantum entanglement?

The Cabibbo-Kobayashi-Maskawa (CKM) matrix, which controls flavor mixing between the three generations of quark fermions, is a key input to the standard model of particle physics. In this paper, we identify a surprising connection between quantum entanglement and the degree of quark mixing. Focusing on a specific limit of 2 → 2 quark scattering mediated by electroweak bosons, we find that the quantum entanglement generated by scattering is minimized when the CKM matrix is almost (but not exactly) diagonal, in qualitative agreement with observation. With the discovery of neutrino masses and mixings, additional angles are needed to parametrize the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix in the lepton sector. Applying the same logic, we find that quantum entanglement is minimized when the PMNS matrix features two large angles and a smaller one, again in qualitative agreement with observation, plus a hint for suppressed C P violation. We speculate on the (unlikely but tantalizing) possibility that minimization of quantum entanglement might be a fundamental principle that determines particle physics input parameters. Published by the American Physical Society 2025

Thaler, Jesse (ORCID:0000000224068160)↗
DOE OSTI2024

Radiation Hardened Engineered Substrates for Time and Space Resolution

Low Gain Avalanche Diodes (LGADs) have become sensors of choice for fast timing of minimum ionizing particles. The current generation of these devices suffer from only moderate radiation hardness, low fill factor, and limited design options. Cactus Materials Inc. proposes to develop sensors with gain layer implants buried beneath the surface which will, with AC coupling developed by Brookhaven National Laboratory (BNL) and University of California Santa Cruz (UC Santa Cruz) solve these problems. Fermilab has a major role in the CMS timing upgrade using the current generation of LGADs and development of AC-coupled, buried layer LGADs will solve some of the central problems inherent in this technology. Annex B included a two-year work plan, deliverables, schedule and funding for the project’s SBIR Phase II award to develop and test prototype LGADs utilizing wafer bonding technology and epitaxial growth, that will allow for fabrication of radiation hard AC coupled devices with 100% fill factor and adjustable operating point.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A Data-Driven Method for Estimating Behind-the-Meter Photovoltaic Generation in Hawaii

Due to the increasing penetration of distributed behind-the-meter photovoltaic (PV) systems and the installed utility revenue metering limited to monitoring only the net power import/export of the household, it is increasingly challenging for utilities to effectively plan and operate the grid. This paper proposes a methodology that estimates behind-the-meter PV generation using a selected subset of monitored PV systems. It is a data-driven approach, and the PV output is estimated utilizing a statistic regression model. A Minimum Redundancy Maximum Relevance (MRMR) algorithm is applied to preselect the optimal subset of the monitored PV systems. The performance of this approach is compared with a spatial interpolation method and a model-based approach. The proposed method is validated using high-resolution meter data recorded from 18 residential rooftop PV systems located on the island of Maui, Hawaii.

Data-driven modeling↗
DOE OSTI2024

Search for Long-Lived Heavy Neutral Leptons with Lepton Flavour Conserving or Violating Decays to a Jet and a Charged Lepton

A search for long-lived heavy neutral leptons (HNLs) is presented, which considers the hadronic final state and coupling scenarios involving all three lepton generations in the 2–20 GeV HNL mass range for the first time. Events comprising two leptons (electrons or muons) and jets are analyzed in a data sample of proton-proton collisions, recorded with the CMS experiment at the CERN LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$^{−1}$. A novel jet tagger, based on a deep neural network, has been developed to identify jets from an HNL decay using various features of the jet and its constituent particles. The network output can be used as a powerful discriminating tool to probe a broad range of HNL lifetimes and masses. Contributions from background processes are determined from data. No excess of events in data over the expected background is observed. Upper limits on the HNL production cross section are derived as functions of the HNL mass and the three coupling strengths V$_{ℓN}$ to each lepton generation ℓ and presented as exclusion limits in the coupling-mass plane, as lower limits on the HNL lifetime, and on the HNL mass. In this search, the most stringent limit on the coupling strength is obtained for pure muon coupling scenarios; values of |$ {V}_{\mu \textrm{N}}^2 $| > 5 (4) × 10$^{−7}$ are excluded for Dirac (Majorana) HNLs with a mass of 10 GeV at a confidence level of 95% that correspond to proper decay lengths of 17 (10) mm.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗