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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Alpenglow Instruments All Phase Water Probe: SBIR Phase I Final Report

Current approaches for measuring water vapor, ice water content, and liquid water content in the atmosphere have a variety of limitations. This is particularly true for ice water content measurements, which are needed in a variety of contexts including understanding the impact of clouds on global climate. The Alpenglow All-Phase Water Probe, which was motivated by limitations of current approaches to measuring water vapor, liquid water, and ice water content in the atmosphere, uses Raman scattering to derive these quantities. The use of Raman scattering signals for retrieval of water vapor mixing ratio, liquid and ice water content has been used in remote sensing applications. This project explored the use of the same approach in an in situ probe to measure all three phases of water in the atmosphere. The prototype device developed in this Phase 1 DOE SBIR proposal was used to demonstrate proof of concept for using Raman scattering to detect signals from water vapor, liquid water, and ice water. The prototype instrument consisted of the transmitting laser (355 nm, 7ns pulse), collector optics and receiver body, and data system. The receiver section body split the return signal into four channels, one for nitrogen plus channels dominated by water vapor, liquid water, and ice. Tests were conducted over a range of conditions ranging from ambient conditions in the lab (low water vapor mixing ratio, no liquid or ice water content), through a cool but saturated plume produced by an off-the-shelf humidifier (moderate water vapor mixing ratio plus some liquid water content), and hot, saturated stream containing extremely high water vapor mixing ratios with liquid water. Data from these tests, which was recorded using a Licel photon-counting data system, demonstrated the sensitivity of the system to varying water vapor mixing ratios ranging from ~5 g m -3 to > 20 g m -3 . Signals in the water vapor and ice channels were observed during the humidifier and hot vapor tests, as expected given the overlap between the ice and water vapor Raman spectra, with the relative contributions to each channel varying as expected with temperature.

58 GEOSCIENCES↗
DOE OSTI2025

Spin dynamics in the itinerant antiferromagnet SrCr 2 ⁢As 2

SrCr 2 ⁢As 2 is an itinerant antiferromagnet in the same structural family as the SrFe 2 ⁢As 2 high-temperature superconductors. Here, we report our calculations of exchange-coupling parameters 𝐽 𝑖⁢𝑗 for SrCr 2 ⁢As 2 using a static linear-response method based on first-principles electronic-structure calculations. We find that the dominant nearest-neighbor exchange coupling 𝐽 1 >0 is antiferromagnetic whereas the next-nearest-neighbor exchange coupling 𝐽 2 <0 is ferromagnetic with 𝐽 2 /𝐽 1 = −0.68, reinforcing the checkerboard in-plane magnetic structure. Thus, unlike other transition-metal arsenides based on Mn, Fe, or Co, we find no competing magnetic interactions in SrCr 2 ⁢As 2 , which aligns with experimental findings. Moreover, the orbital resolution of exchange interactions shows that 𝐽 1 and 𝐽 2 are dominated by direct exchange mediated by the Cr 𝑑 orbitals. To validate the calculations we conduct inelastic neutron-scattering measurements on powder samples that show steeply dispersive magnetic excitations arising from the magnetic Γ points and persisting up to energies of at least 175 meV. The spin-wave spectra are then modeled using the Heisenberg Hamiltonian with the theoretically calculated exchange couplings. In conclusion, the calculated neutron-scattering spectra are in good agreement with the experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Effects of Improved 17 O Correction on Interlaboratory Agreement in Clumped Isotope Calibrations, Estimates of Mineral-Specific Offsets, and Temperature Dependence of Acid Digestion Fractionation

The clumped isotopic composition of carbonate-CO 2 (denoted Δ 47 ) is a function of carbonate formation temperature and in natural samples can act as a recorder of paleoclimate, burial, or diagenetic conditions. The absolute abundance of heavy isotopes in the universal standards VPDB and VSMOW (defined by four parameters: R 13 VPDB , R 17 VSMOW , R 18 VSMOW , and λ ) impact calculated Δ 47 values. In this work, we investigate whether use of updated and more accurate values for these parameters can remove observed interlaboratory differences in the measured T-Δ 47 relationship. Using the updated parameters, we reprocess 14 published calibration data sets measured in 11 different laboratories, representing many mineralogies, bulk compositions, sample types, reaction temperatures, and sample preparation and analysis methods. Exploiting this large composite data set ( n = 1,253 sample replicates), we investigate the possibility for a “universal” clumped isotope calibration. We find that applying updated parameters improves the T-Δ 47 relationship (reduces residuals) within most labs and improves overall agreement but does not eliminate all interlaboratory differences. We reaffirm earlier findings that different mineralogies do not require different calibration equations and that cleaning procedures, method of pressure baseline correction, and mass spectrometer type do not affect interlaboratory agreement. We also present new estimates of the temperature dependence of the acid digestion fractionation for Δ 47 (Δ* 25-X ), based on combining reprocessed data from four studies, and new theoretical equilibrium values to be used in calculation of the empirical transfer function. Overall, we have ruled out a number of possible causes of interlaboratory disagreement in the T-Δ 47 relationship, but many more remain to be investigated.

58 GEOSCIENCES↗
DOE OSTI2018

Transportation Modeling and Global Health

A patient in the United States has been diagnosed with Ebola. Fear and panic kicks in across the country, and hospitals are inundated with hundreds of people, some infected with the highly contagious disease and others not. Blood tests are needed for positive diagnoses, but the diagnostic labs are overwhelmed with blood samples to test, and staff are overworked and stressed. Infected people need to be quarantined and treated, but it's hard to find rooms to quarantine so many patients. Sick people who need triage and regular care for other emergencies are afraid to go to hospitals for fear of Ebola, which has a 50% fatality rate. And since hospitals are so overwhelmed, sick people often stay home, infecting heathy people around them; the U.S. is now in the grips a full-blown Ebola outbreak. Sandia's high-performance computers simulated such a nightmare scenario recently, and with good reason. An Ebola outbreak in the United States could be devastating if hospitals are not prepared. When an Ebola outbreak in West Africa became a global concern in 2014, health advisers were alarmed at the length of time it took to properly diagnose infected people. In rural areas in Liberia, for example, blood samples from ailing people would be sent to a laboratory for testing, but the closest lab was hundreds of miles away through difficult and sometimes impassable roads. In more urban areas, blood samples would be sent to nearby labs, but those labs were often already overburdened by the sheer volume of samples to test. Staff at some treatment centers were unaware that a lab a little farther away might have the capacity to take in more samples. Meanwhile, undiagnosed infected people were unknowingly spreading the disease to many others around them, worsening the outbreak. The U.S. Defense Threat Reduction Agency (DTRA) and Centers for Disease Control and Prevention (CDC) posed a serious question: how do we improve blood-sample transportation routes in Liberia to ensure that samples taken from ill people are tested as quickly as possible, ensuring a proper diagnosis and faster treatment? Sandia scientists, already experts in transportation modeling for nuclear materials, quickly swarmed on this problem. The Sandia Ebola response team immediately set out to collect data from the region using available maps and local information, and transformed the raw data to GIS maps. Then, applying Sandia transportation routing algorithms, the team identified the optimal routes to get blood samples to the best laboratory for testing, even if that lab was not geographically the closest. The models also showed the best possible locations for mobile diagnostic laboratories that would better support the very rural regions that were most affected by the Ebola outbreak.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Learnings from the Marcellus Shale Energy and Environmental Lab (MSEEL) Using Fiber Optic Tools and Geomechanical Modeling

The study focuses on the MIP and Boggess pads of the MSEEL (Marcellus Shale Energy and Environmental Laboratory), a public-private partnership with a mandate to publicly release data for scientists and engineers to engage with. Multiple diagnostic tools are used to characterize the formation and monitor fracture treatment and propagation. Geomechanical modeling is used to understand the insitu stresses, microseismic to describe half-lengths and heights, and fiber optics to characterize offset well. Fracture Driven Interactions (FDI’s) and interstage communication. Recent publications covering the MSEEL MIP-3H and MIP-5H wells are reviewed and discussed. A preview of the findings from the Boggess pad (first production in November 2019) is also shared here

03 NATURAL GAS↗
DOE OSTI2021

Getting their days in the sun

Although metal halide perovskite solar cells are extensively investigated in the lab their performance and degradation in real-world outdoor conditions are still poorly understood. Now, researchers propose a method to analyse field data to identify how and why the outdoor device performance changes over time.

14 SOLAR ENERGY↗
DOE OSTI2014

Study of the proton structure by measurements of polarization transfers in real Compton scattering at Jlab

A preliminary analysis of polarization-transfer data at large scattering angle (70°), obtained in an experiment of real Compton scattering on proton, performed in Hall-C of Jefferson Lab, is presented. It is also discussed the relevance of this kind of experiments for shedding light on the non-perturbative structure of the proton, at low energy, and on the transition from the non-perturbative regime to the perturbative one, that occurs at high energy. Moreover, the possibility to extract Compton form factors and the Generalized Parton Distributions, one of the most promising theoretical tool to determine the total angular momentum contribution of quarks and gluons to nucleon spin, is emphasized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Exploring the influence of transfer channels on fusion reactions: The case of 40 Ca + 58,64 Ni

Fusion cross sections have been measured in the 40 Ca + 58 Ni and 40 Ca + 64 Ni systems at beam energies ranging from E lab = 104.75 MeV to 153.5 MeV using the Laboratori Nazionali di Legnaro electrostatic deflector. Distributions of barriers have been extracted from the experimental data. Preliminary coupled channel calculations were performed and hints of effects of neutron transfers on the fusion below the barrier in the 40 Ca + 64 Ni are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Initial-state and final-state vibrational effects in the treatment of molecular photoionization dynamics

The influence upon molecular photoionization dynamics of vibrational motion of the nuclei in both the initial (neutral) state and the final (molecular ion) state is examined using a consistent theoretical treatment applied to the wide range of existing experimental data for the benchmark CO C 1s{sup -1} K-shell ionization. This allows comparisons to be made against cross sections, lab-frame {beta}-parameter measurements, and molecule-frame photoelectron angular distributions that have all been recorded both with, and without, ion vibrational-state resolution. A relatively simple multiple-scattering treatment works well in all these applications, its performance comparing very favorably with alternative relaxed core Hartree-Fock methods. The calculations are extended to examine possible effects of vibrational excitation in the neutral, and show marked effects that extend to energies lying away from the obvious center of the CO {sigma}{sup *} shape resonance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Carbon Capture Multidisciplinary Simulation Center Trilab Support Team (TST) Fall Meeting 2016 Report

The theme of this year’s meeting was “Predictivity: Now and in the Future”. After welcoming remarks, Erik Draeger gave a talk on the NNSA Labs’ history of predictive simulation and the new challenges faced by upcoming architecture changes. He described an example where the volume of analysis data produced by a set of inertial confinement fusion (ICF) simulations on the Trinity machine was too large to store or transfer, and the steps needed to reduce it to a manageable size. He also described the software re-engineering plan for LLNL’s suite of multiphysics codes and physics packages with a new push toward common components, making collaboration with teams like the CCMSC who already have experience trying to architect complex multiphysics code infrastructure on next-generation architectures all the more important. Phil Smith then gave an overview outlining the goals of the project, namely to accelerate development of new technology in the form of high efficiency carbon capture pulverized coal power generation as well as further optimize existing state of the art designs. He then presented a summary of the Center’s top-down uncertainty quantification approach, in which ultimate target predictivity informs uncertainty targets for lower-level components, and gave data on how close all the different components currently are to their targets. Most components still need an approximately two-fold reduction in uncertainty to hit the ultimate predictivity target, but the current accuracy is already rather impressive.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

ML based control systems for nuclear physics experiments

The Experimental Physics Software and Computing Infrastructure (EPSCI) group at Jefferson Lab is leading the use of machine learning (ML) to enhance control systems in nuclear physics experiments. Collaborating closely with domain experts and data scientists, we have developed an ML-based control system that uses a Gaussian process to dynamically adjust the high voltage of the GlueX Central Drift Chamber. This results in stable detector performance by adapting to environmental changes, thereby reducing the offline calibration effort. Furthermore, we are developing ML-driven systems for optimizing the polarization of photon beams and polarized cryotargets. These systems will maintain the optimal microwave frequency in cryogenic targets and make real-time adjustments to diamond radiators for polarized photon sources, tasks traditionally handled by human operators. By automating these functions, we aim to optimize the polarization, reduce downtime, and minimize human error. This talk will highlight the development of reliable ML-based control systems and the policies to ensure they are both effective and trustworthy.

Jeske, Torri↗
DOE OSTI2026

AMICE2 measurements of lab-generated and ambient aerosols at BNL from 2025-07-21 to 2025-08-22

From July 21 through August 22, 2025 the Absorption Measurement Inter-Comparison Experiment phase 2 (AMICE2) was conducted at Brookhaven National Laboratory. The experiment included concurrent operation of several filter-based instruments and in situ instruments sampling from a common sample line. The common sample line provided all instruments with either lab-generated aerosols composed of external mixtures of nigrosin, cabojet, and ammonium sulfate, or with ambient aerosol drawn through an external stack. This data set contains measurements from the CAPS PMssa instrument taken as a reference instrument at 532 nm and against which the effectiveness of the filter corrections may be assessed. It also contains attenuation coefficients from all filter-based instruments and filter transmittances in order to permit correction for filter loading, and the SSA and COA to permit corrections for aerosol darkness. Finally, two sets of multiplicative corrections are provided for the AE-33, TAP, and MA-350 instruments: the first one, Corr1, corrects for filter amplification factor and loading, the second one, Corr2, corrects for the aerosol darkness in terms of the natural log of COA.

Aerosol Extinction↗
DOE OSTI2019

Electric Skyrmion with a Twist

Simulation of a single polar skyrmion. Red arrows signify that this is a left-handed skyrmion. Electric Skyrmions Charge Ahead for Next-Generation Data Storage News Release: https://newscenter.lbl.gov/2019/04/18/electric-skyrmions-charge-ahead-for-next-generation-data-storage/ Credit: Xiaoxing Cheng ( Pennsylvania State University), C.T. Nelson (Oak Ridge National Laboratory), and Ramamoorthy Ramesh (Berkeley Lab)

36 MATERIALS SCIENCE↗
DOE OSTI2021

Finalizing Construction of a New Data Center at BNL

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source II (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term.

99 GENERAL AND MISCELLANEOUS↗