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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Aerospace Sensor Systems: From Sensor Development To Vehicle Application

This paper presents an overview of years of sensor system development and application for aerospace systems. The emphasis of this work is on developing advanced capabilities for measurement and control of aeropropulsion and crew vehicle systems as well as monitoring the safety of those systems. Specific areas of work include chemical species sensors, thin film thermocouples and strain gages, heat flux gages, fuel gages, SiC based electronic devices and sensors, space qualified electronics, and MicroElectroMechanical Systems (MEMS) as well as integrated and multifunctional sensor systems. Each sensor type has its own technical challenges related to integration and reliability in a given application. The general approach has been to develop base sensor technology using microfabrication techniques, integrate sensors with "smart" hardware and software, and demonstrate those systems in a range of aerospace applications. Descriptions of the sensor elements, their integration into sensors systems, and examples of sensor system applications will be discussed. Finally, suggestions related to the future of sensor technology will be given. It is concluded that smart micro/nano sensor technology can revolutionize aerospace applications, but significant challenges exist in maturing the technology and demonstrating its value in real-life applications.

Hunter, Gary W.↗
NASA NTRS2022

S-band Network Analysis and Strategies for LEO Multi-CubeSat Science Missions

This paper presents the network architecture utilized at NASA’s Goddard Space Flight Center (GSFC) to support a mission set of five 6U CubeSats. These five CubeSats employ a multi-waveform Software Defined Radio (SDR) platform developed by Vulcan Wireless for use with NASA’s Space Relay (SR) and Direct to Earth (DTE) networks. The performance testing of the SDR is discussed via a comprehensive S-band and communication link analysis. The tested capabilities of the SDR and antenna components are reviewed in terms of the mission requirements for each CubeSat. Size Weight and Power (SWaP), required availability, and access times are discussed. The measured data from experimental compatibility testing is incorporated into detailed simulations of the CubeSat mission set to verify desired performance over the mission lifetime. The model is also used to investigate the impact of potential adverse effects on the communication links such as interference and weather conditions. This paper also reviews potential improvements from future technological advances and commercial partnerships. A collaborative investigation between GSFC and Oklahoma State University is presented in which a qualitative analysis of Hybrid RF/Optical communication strategies is performed. The data rate improvements of optical communication techniques are weighed against attitude control and science mission requirements for CubeSats, and network architectures/switching strategies are discussed. A separate analysis reviews the potential benefits of ground station partnerships, which aligns with NASA’s future goal to include commercial partners in its Earth and space network architectures, e.g. LunaNet.

Space networks↗
DOE OSTI2025

Advanced Model Development for Large Eddy Simulation of Oxy-Combustion and Supercritical Carbon Dioxide Power Cycles

A joint experimental and numerical study is performed to observe the characteristics of a supercritical carbon dioxide turbulent mixing layer in the presence of strong nonlinearities in the thermodynamic and transport properties. A bespoke experimental setup is designed and employed for this purpose and provides insight into macroscopic mixing behavior. The mixing is experimentally observed using two techniques: shadowgraphy and spontaneous Raman scattering. Qualitative and quantitative intensity fields obtained via these techniques yield instantaneous and mean density data. Spanwise temperature data is also collected using analogue resistance temperature detectors. These measurements are used to quantify the level of mixed material within the field. The experimental data are supplemented by a companion high-fidelity numerical study. The numerical results are obtained through fully resolved, three-dimensional direct numerical simulation. The numerical dataset permits observation of the near-field mixing characteristics, which are difficult to measure experimentally due to the rapid dynamics and sharp thermophysical gradients in this area. Qualitative field visualizations are presented, followed by quantitative mixed material results and observations regarding thermodynamic property trends at select locations within the field. One-dimensional spectra of the turbulent kinetic energy and solenoidal dissipation are provided to observe the spectral characteristics of the flow. Reynolds stress anisotropy is analyzed graphically through anisotropy invariance maps (Lumley triangles). The mixing quantification, spectral data and anisotropy analysis of a flow at these thermodynamic conditions represent the main outcomes of the work.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2020

Protein structural changes on a CubeSat under rocket acceleration profile

Catalyzing life-sustaining reactions, proteins are composed by 20 different amino acids that fold into a compact yet flexible three-dimensional architecture, which dictates what their function(s) might be. Determining the spatial arrangement of the atoms, the protein’s 3D structure, enables key advances in fundamental and applied research. Protein crystallization is a powerful technique to achieve this. Unlike Earth’s crystallization experiments, biomolecular crystallization in space in the absence of gravitational force is actively sought to improve crystal growth techniques. However, the effects of changing gravitational vectors on a protein solution reaching supersaturation remain largely unknown. Here, we have developed a low-cost crystallization cell within a CubeSat payload module to exploit the unique experimental conditions set aboard a sounding rocket. We designed a biaxial gimbal to house the crystallization experiments and take measurements on the protein solution in-flight with a spectrophotometry system. After flight, we used X-ray diffraction analysis to determine that flown protein has a structural rearrangement marked by loss of the protein’s water and sodium in a manner that differs from crystals grown on the ground. We finally show that our gimbal payload module design is a portable experimental setup to take laboratory research investigations into exploratory space flights.

42 ENGINEERING↗
DOE OSTI2023

One thousand soils for molecular understanding of belowground carbon cycling

While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray computed tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

47 OTHER INSTRUMENTATION↗
NASA NTRS2013

Unified Simulation and Analysis Framework for Deep Space Navigation Design

As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.

Anzalone, Evan↗
NASA NTRS2014

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Modeling↗
DOE OSTI2022

OPTIMIZATION OF A NUCLEAR VESSEL OUTLET FOR INCIDENT MONITORING

Classical nuclear core fluidic design techniques require improvement to better align with modern technological innovations. The US Department of Energy’s Office of Nuclear Energy (DOE-NE) Transformational Challenge Reactor (TCR) program is deploying additive manufacturing and advanced modeling and simulation to reimagine these designs. With the aid of modern computing power, computerized design optimization can be implemented to remove unwanted pressure drop while simultaneously optimizing flow structures, resulting in new opportunities to enable advanced instrumentation and monitoring capabilities.Previous development of geometric specifications for the TCR pressure vessel’s outlet plenum used design optimization to (1) limit pressure losses below 3.5 kPa (~0.5 psi) and (2) create a fluidic plane in which the temperature variation would not exceed ±5°C. This significant limit of the allowable pressure drop stems from the overarching goal of the TCR program to apply cutting edge techniques and unconventional thinking to demonstrate potential opportunities in additive manufacturing (AM).This paper expands the previous work by optimizing thermowell locations for robust measurements by explicitly modeling them and the resulting flow impacts. Additionally, a single core coolant channel was chosen to represent an event that causes an increased bulk flow temperature increase of 100°C.High fidelity unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of the conjugate heat transfer problem were run in Siemen’s Star-CCM+ for this study. Next, the bulk flow temperature of a single coolant channel was increased by 100°C and was allowed to converge again. Finally, statistical analysis using a sequential probability ratio test (SPRT) was used to determine the elapsed time the thermocouples took to discover the increased bulk flow temperature.

See, Nate↗
DOE OSTI2020

Large room-temperature elastocaloric effect in a bulk polycrystalline Ni-Ti-Cu-Co alloy with low isothermal stress hysteresis

Creation of high-performance advanced elastocaloric materials is essential for the execution of elastocaloric refrigeration that can be an environment-friendly and high-efficiency substitute for the widely used traditional vapor-compression cooling technology. Here we have developed a bulk polycrystalline Ni-Ti-Cu-Co shape memory alloy exhibiting large elastocaloric effect, low stress hysteresis and room temperature working temperature, all of which are of great importance to and urgently demanded for high-efficiency room-temperature elastocaloric refrigeration. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy shows a large room-temperature elastocaloric effect with directly measured adiabatic temperature change up to -14.4 K during unloading. The stress hysteresis of the isothermal superelastic stress-strain curve is as low as 60 MPa when the maximum tensile strain is 2.7%. Owing to the large elastocaloric effect and low stress hysteresis, a very high coefficient of performance up to 19 is achieved on the material level. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy is a robust candidate for efficient elastocaloric refrigeration. Advanced in-situ synchrotron high-energy X-ray diffraction technique was employed to reveal the phase transformation sequence and to accurately determine the crystal structure of different phases, based on which the lattice compatibility between the transforming phases was evaluated and the phase transformation strain was predicted, providing in-depth fundamental understanding of the martensitic transformation in this newly developed alloy. This work could be useful for designing high-performance elastocaloric materials for solid-state cooling applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrated Methane Monitoring Platform Extension, Volume I: Final Technical Report

The IMMPE project, DE-FE0032284, was to enhance methane monitoring technologies and their applications across various natural gas asset classes. The scope included deploying advanced methane detection and monitoring technologies to identify and mitigate fugitive methane emissions, measuring emission rates, and assessing impacts. The findings included the successful mitigation of identified emissions and quantification of emission rates. A key outcome was the development of a comprehensive template and summary of recommendations for methane emissions monitoring, which is replicable for both upstream and downstream applications. Furthermore, the project emphasized the importance of education by providing training opportunities for technicians and regulators, thereby fostering awareness and promoting the adoption of cost-effective methane emissions monitoring and management techniques.

02 PETROLEUM↗
NASA NTRS1993

RAPID: The imaging energetic particle spectrometer on Cluster

The RAPID spectrometer for the Cluster mission, an advanced particle detector for the analysis of suprathermal plasma distributions in the energy range from 20 to 400 keV and from 2 keV/nuc to 1500 keV for electrons and ions, respectively, is presented. Novel detector concepts in combination with pinhole acceptance permit the measurement of angular distributions over a range of 180 deg in polar angle for either species. The detection principle for the ionic component is based on a two dimensional analysis of a particle's velocity and energy. Electrons are identified by the well known energy range relationship. The detection techniques are described and selected areas in geospace are used to highlight the scientific objectives of this investigation.

Wilken, B.↗
NASA NTRS2009

Surface Wind Vector and Rain Rate Observation Capability of Future Hurricane Imaging Radiometer (HIRAD)

The Hurricane Imaging Radiometer (HIRAD) is the next-generation Stepped Frequency Microwave Radiometer (SFMR), and it will offer the capability of simultaneous wide-swath observations of both extreme ocean surface wind vector and strong precipitation from either aircraft (including UAS) or satellite platforms. HIRAD will be a compact, lightweight, low-power instrument with no moving parts that will produce valid wind observations under hurricane conditions when existing microwave sensors (radiometers or scatterometers) are hindered by precipitation. The SFMR i s a proven aircraft remote sensing system for simultaneously observing extreme ocean surface wind speeds and rain rates, including those of major hurricane intensity. The proposed HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer technology. The first version of the instrument will be a single polarization system for wind speed and rain rate, with a dual-polarization system to follow for wind vector capability. This sensor will operate over 4-7 GHz (C-band frequencies) where the required tropical cyclone remote sensing physics has been validated by both SFMR and WindSat radiometers. HIRAD incorporates a unique, technologically advanced array antenna and several other technologies successfully demonstrated by NASA s Instrument Incubator Program. A brassboard (laboratory) version of the instrument has been completed and successfully tested in a test chamber. Development of the aircraft instrument is underway, with flight testing planned for the fall of 2009. Preliminary Observing System Simulation Experiments (OSSEs) show that HIRAD will have a significant positive impact on surface wind analyses as either a new aircraft or satellite sensor. New off-nadir data collected in 2008 by SFMR that affirms the ability of this measurement technique to obtain wind speed data at non-zero incidence angle will be presented, as well as data from the brassboard instrument chamber tests.

Miller, Timothy↗
NASA NTRS2008

The Super Tuesday Outbreak: Forecast Sensitivities to Single-Moment Microphysics Schemes

Forecast precipitation and radar characteristics are used by operational centers to guide the issuance of advisory products. As operational numerical weather prediction is performed at increasingly finer spatial resolution, convective precipitation traditionally represented by sub-grid scale parameterization schemes is now being determined explicitly through single- or multi-moment bulk water microphysics routines. Gains in forecasting skill are expected through improved simulation of clouds and their microphysical processes. High resolution model grids and advanced parameterizations are now available through steady increases in computer resources. As with any parameterization, their reliability must be measured through performance metrics, with errors noted and targeted for improvement. Furthermore, the use of these schemes within an operational framework requires an understanding of limitations and an estimate of biases so that forecasters and model development teams can be aware of potential errors. The National Severe Storms Laboratory (NSSL) Spring Experiments have produced daily, high resolution forecasts used to evaluate forecast skill among an ensemble with varied physical parameterizations and data assimilation techniques. In this research, high resolution forecasts of the 5-6 February 2008 Super Tuesday Outbreak are replicated using the NSSL configuration in order to evaluate two components of simulated convection on a large domain: sensitivities of quantitative precipitation forecasts to assumptions within a single-moment bulk water microphysics scheme, and to determine if these schemes accurately depict the reflectivity characteristics of well-simulated, organized, cold frontal convection. As radar returns are sensitive to the amount of hydrometeor mass and the distribution of mass among variably sized targets, radar comparisons may guide potential improvements to a single-moment scheme. In addition, object-based verification metrics are evaluated for their utility in gauging model performance and QPF variability.

Molthan, Andrew L.↗
DOE OSTI2023

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S ( Q , ω ), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Element- and Time-Resolved Measurements of Spin Dynamics Using X-ray Detected Ferromagnetic Resonance

Although XFMR experiments have been carried out at a number of synchrotron radiation facilities since 2004, only a few facilities currently have XFMR setups available for user operation. Here in this review, we mainly focus on recent developments and work that has been carried out using time-resolved XFMR at beamline 4.0.2 of the Advanced Light Source and at beamline I10 of the Diamond Light Source. The experimental setup and the sample holder used at beamline I10 and beamline 4.0.2, respectively, are shown in Figure 1. We will first introduce the working principle of XFMR and then provide examples of the capabilities of this technique. Finally, we will briefly describe an outlook for the future development of and new opportunities for XFMR.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗