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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 OSTI2024

Combination of Measurements of the Top Quark Mass from Data Collected by the ATLAS and CMS Experiments at s = 7 and 8 TeV

A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The datasets used correspond to an integrated luminosity of up to 5 and 20 fb − 1 of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exploit both the semileptonic and hadronic decays of the top quark, and a measurement using events enriched in single top quark production via the electroweak t channel. The combination accounts for the correlations between measurements and achieves an improvement in the total uncertainty of 31% relative to the most precise input measurement. The result is m t = 172.52 ± 0.14 ( stat ) ± 0.30 ( syst ) GeV , with a total uncertainty of 0.33 GeV. © 2024 CERN, for the CMS and ATLASs Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2025

Estimating ionization states and continuum lowering from ab initio path integral Monte Carlo simulations for warm dense hydrogen

Warm dense matter (WDM) is an active field of research, with applications ranging from astrophysics to inertial confinement fusion. Ionization degree and continuum lowering are important quantities to understand how materials behave under these conditions, but can be difficult to diagnose since experimental campaigns are limited and often require model-dependent analysis. This is especially true for hydrogen, which has a comparably low scattering cross section, making high-quality data particularly difficult to obtain. Consequently, building equation of state tables often relies on simulations in combination with untested approximations to extract properties from experiments. Here, we investigate an approach for extracting the ionization potential depression and ionization degree—quantities which are otherwise not directly accessible from the physical model—from first-principles path integral Monte Carlo (PIMC) simulations utilizing a chemical model. In contrast to experimental measurements, where noise and nonequilibrium effects add to the uncertainty of the inferred parameters, PIMC simulations provide a clean signal with well-defined thermodynamic conditions. Comparisons against commonly used models show a qualitative agreement, but we find deviations primarily for the high-density and high-temperature cases. We also demonstrate the decreasing sensitivity of the dynamic structure factor with respect to both ionization and continuum lowering for increasing scattering angles in x-ray Thomson scattering experiments. Our work has important implications for the design of future experiments, but also offers qualitative understanding of structure factors and the imaginary-time correlation function obtained from first-principles quantum Monte Carlo simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2009

Developing Lightning Prediction Tools for the CCAFS Dual-Polarimetric Radar

NASA Marshall Space Flight Center and the University of Alabama Huntsville are collaborating with the 45th Weather Squadron (45WS) to develop improved lightning prediction capabilities for the new C-band dual-polarimetric weather radar being acquired for use by 45WS and launch weather forecasters at Cape Canaveral Air Force Station (CCAFS). In particular, these algorithms will focus on lightning onset, cessation and combined lightning-radar applications for convective winds assessment. Research using radar reflectivity (Z) data for prediction of lightning onset has been extensively discussed in the literature and subsequently applied by launch weather forecasters as it pertains to lightning nowcasting. Currently the forecasters apply a relatively straight forward but effective temperature-Z threshold algorithm for assessing the likelihood of lightning onset in a given storm. In addition, a layered VIL above the freezing level product is used as automated guidance for the onset of lightning. Only limited research and field work has been conducted on lightning cessation using Z and vertically-integrated Z for determining cessation. Though not used operationally vertically-integrated Z (basis for VIL) has recently shown promise as a tool for use in nowcasting lightning cessation. The work discussed herein leverages and expands upon these and similar reflectivity-threshold approaches via the application/addition of over two decades of polarimetric radar research focused on distinct multi-parameter radar signatures of ice/mixed-phase initiation and ice-crystal orientation in highly electrified convective clouds. Specifically, our approach is based on numerous previous studies that have observed repeatable patterns in the behavior of the vertical hydrometeor column as it relates to the temporal evolution of differential reflectivity and depolarization (manifested in either LDR or p(sub hv)), development of in-situ mixed and ice phase microphysics, electric fields, and ensuing lightning in the sub-tropical/tropical convection typical of the southeastern U.S., Maritime Continent, and southwestern Amazon. The polarimetric signatures detected in this setting provide a basis for automated 3-D detection of hydrometeor types in fuzzy logic hydrometeor identification algorithms (HID). Our working hypothesis is that improvement in lightning onset warning lead time and specificity for a given storm, relative to application of a Z-threshold algorithm, should arise as a consequence of the ability of dual-polarimetric radar to unambiguously detect and identify (through HID algorithms) the updraft elevation of rain-water cores above the freezing level and subsequent onset of drop freezing, riming, and robust mixed phase processes leading to significant charge separation and lightning. This type of algorithm, though dependent on the quality of the polarimetric data should be less susceptible to variable Z-calibration that can impact a given Z-threshold approach. To facilitate development of the algorithm while the 45WS dual-pol radar is in its current test stages and to evaluate the impact of polarimetric data quality (e.g., modified scan parameters and sampling) on the ensuing algorithms, we are using the ARMOR C-band dual-pol radar in Huntsville combined with N. Alabama LMA data and ARMOR HID algorithms [NCAR algorithm modified for application at C-band] in a testbed fashion. For lightning cessation we are revisiting the application of differential propagation phase variables for the monitoring of ice crystal alignment driven by in-cloud electric fields combined with metrics of ice water path (i.e., vertically integrated reflectivity). Importantly it should be noted that this approach is still very much a research topic and as such, we will explore operational applications that involve radar frequencies other than C-Band by using the UAH MAX X-band dual-pol radar in slow staring modes.

Petersen, W. A.↗
DOE OSTI2024

Enhancements and Deployment of the TDAQ System for the Mu2e Experiment

The Real Time Processing Systems Division at Fermilab has deployed new features to the Off-The-Shelf Data Acquisition framework (otsdaq) for the Mu2e experiment. The Mu2e experiment will search for the coherent neutrino-less conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement of 10,000 times over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable at present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system uses otsdaq as its online Data Acquisition System (DAQ) framework. otsdaq integrates the artdaq and art frameworks for event transfer, filtering, and processing. otsdaq is a web-based DAQ software suite focusing on flexibility and scalability and provides a multi-user interface accessible through a web browser. artdaq handles the entire data stream, which is read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events combined with the data flux coming from a cosmic-ray veto (CRV) system. Detector front-ends are configured through the PCIe bus by customized otsdaq plugins. The otsdaq slow controls infrastructure has been further developed using the experimental physics and industrial control system (EPICS) open-source platform for monitoring, controlling, alarming, and archiving. The detector control system (DCS) for Mu2e has been integrated into otsdaq. The production TDAQ and DCS system has been deployed at the experimental hall and is being debugged and optimized for experiment operations. We report on the feature enhancements and deployment of otsdaq for Mu2e.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
NASA NTRSDate not supplied

Earth System Digital Twins (ESDT) Technology for NASA Earth Science

For NASA's Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as an interactive and integrated multidomain, multiscale, digital replica of the state and temporal evolution of Earth systems. It dynamically integrates: relevant Earth system models and simulations; other relevant models (e.g., related to the world's infrastructure); continuous and timely (including near real time and direct readout) observations (e.g., space, air, ground, over/underwater, Internet of Things (IoT), socioeconomic); long-time records; as well as analytics and artificial intelligence tools. Effective ESDTs enable users to run hypothetical scenarios to improve the understanding, prediction of and mitigation/response to Earth system processes, natural phenomena and human activities as well as their many interactions. An ESDT is a type of integrated information system that, for example, enables continuous assessment of impact from naturally occurring and/or human activities on physical and natural environments. AIST ESDT strategic goals are to: 1. Develop information system frameworks to provide continuous and accurate representations of systems as they change over time; 2. Mirror various Earth Science systems and utilize the combination of Data Analytics, Artificial Intelligence, Digital Thread, and state-of-the-art models to help predict the Earth’s response to various phenomena; 3. Provide the tools to conduct "what if" investigations that can result in actionable predictions. The AIST ESDT thrust is developing capabilities toward the development of future digital twins of the Earth or of subcomponents of the Earth. This will enable the development of an overarching framework that will integrate New Observing Strategies (NOS) to enable new observation measurements, i.e., multi-source, coordinated, dynamic and responsive to needs and requests defined by Analytic Collaborative Frameworks (ACF) that enable agile science investigations fusing and analyzing very large amounts of diverse data. NOS and ACF capabilities along with open access to various science, infrastructure and human data, interconnected modeling, data assimilation, simulations, surrogate modeling, high-performance computing and advanced visualization, will define a powerful framework that could be utilized for local, regional or global and/or thematic digital twins. This presentation will describe a general overview of the AIST ESDT vision including prior work done in the areas of NOS and ACF as well as current and upcoming ESDT projects.

Jacqueline Le Moigne↗
DOE OSTI2022

Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico

Hurricane Maria left unprecedented impacts on Puerto Rican communities, leaving some without infrastructure services and unable to communicate with family for several months. Here, to understand the forms of community-level resilience that emerged while hard infrastructure systems took time recover, this article (1) abductively explores resilience as an emergent phenomenon of complex adaptive systems; (2) identifies subsequent forms of social capital, local adaptive capacities, and manifestations of quantifiable variables, such as infrastructure performance, in community experiences; and (3) demonstrates a framework to integrate disparate methodologies for resilience assessments via a multiplicity of mappings of space and place. We combine ethnographic and geospatial methods into an interactive GeoApp for analysis using participant-coded narratives and a series of geospatial indicators as a thick map. Thick mapping facilitates quantitative and qualitative data analysis at several scales, while enabling qualitative query of collected narratives. Results highlight local innovation, community bonding and bridging, and nuances in the role of public institutions as emergent elements of resilience. The thick map shows how top-down assessments can be augmented by thick data and how multiple framings can be anchored in the same system or place. These findings are important to inform and integrate community-oriented and technocentric solutions toward resilience-enhancing measures.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Tropical Rainfall Distributions Determined Using TRMM Combined with other Satellite and Raingauge Information

Abstract A technique is described to use Tropical Rain Measuring Mission (TRMM) combined radar/radiometer information to adjust geosynchronous infrared satellite data (the TRMM Adjusted GOES Precipitation Index, or TRMM AGPI). The AGPI is then merged with rain gauge information (mostly over land; the TRMM merged product) to provide fine- scale (1 deg latitude/longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37 deg N-S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged-product tropical-mean estimates for 1998 are 3.3 mm/ day over ocean and 3.1 mm/ day over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, very different from the other tropical rainy areas where TRMM merged-product estimates are higher. This regional difference may indicate that TRMM is better at taking in to account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection. Comparisons of these TRMM merged analysis estimates with surface data sets shows varied results; the bias is near zero when compared to western Pacific Ocean atoll raingauge data, but significantly positive compared to Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected. The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the ENSO tropical precipitation pattern from early 1998 (El Nino) through early 1999 (La Nina) and beyond. The El Nino minus La Nina difference map shows the eastern Pacific maximum, the maritime continent minima and other tropical and mid-latitude features. The differences in the Pacific are very similar to those detected by the GPCP analyses. However, summing the El Nino minus La Nina differences over the global tropical oceans yields divergent answers from TRMM, GPCP and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.

Adler, Robert F.↗
DOE OSTI2020

Integrated Data Analysis to Improve Te and Zeff Measurement on NSTX-U

The multi-energy soft x-ray (ME-SXR) camera is a new concept for a fusion plasma diagnostic based on a pixelated detector with adjustable photon cutoff thresholds. It is a single, compact, solid-state detector which combines spatial and spectral sensitivity and can be configured remotely at run time, making it ideal for a fusion environment. The detector can be configured for application to a wide range of physics problems including impurity transport, electron temperature profiles, and non-thermal effects. The detector has been installed on the Madison Symmetric Torus (MST) as part of a collaboration between Princeton Plasma Physics Laboratory and the University of Wisconsin–Madison to develop the technology and analysis techniques. A full diagnostic forward model of the ME-SXR diagnostic has been developed to enable analysis of the raw photon count data. This model incorporates the atomic physics underlying the plasma emission, diagnostic geometry, and detector response characteristics. Employing this model, a Bayesian analysis approach is used to extract electron temperature from the raw data. This analysis approach uses all available information, clearly quantifies prior information, and handles uncertainties transparently. The ME-SXR camera is being used to study the 3D (helical, stellarator-like) state that forms in the MST device. This helical core structure has improved energy confinement. The transition to the helical state is accompanied by a rise in electron temperature in the central part of the helical structure, but then this enhancement fluctuates. The characteristics of this fluctuation are being compared to a magneto-hydrodynamics model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1996

Stochastic Filtering of Rain Profiles Using Radar, Surface-Referenced Radar, or Combined Radar-Radiometer Measurements

This paper describes a computationally efficient nearly optimal Bayesian algorithm to estimate rain (and drop size distribution) profiles, given a radar reflectivity profile at a single attenuating wavelength. In addition to estimating the averages of all the mutually ambiguous combinations of rain parameters that can produce the data observed, the approach also calculates the n-ns uncertainty in its estimates (this uncertainty thus quantifies "the amount of ambiguity" in the "solution"). The paper also describes a more general approach that can make estimates based on a radar reflectivity profile together with an approximate measurement of the path-integrated attenuation, or a radar reflectivity profile and a set of passive microwave brightness temperatures. This more general "combined" algorithm is currently being adapted for the Tropical Rainfall Measuring Mission.

Haddad, Ziad S.↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
NASA NTRSDate not supplied

A Process Model of Interpersonal Relationship Formation in Isolated and Confined Environments

Future space crews will face several challenges such as living and working in a confined environment, isolated from others. These circumstances increase the importance of interpersonal compatibility, teamwork, and team performance. The interpersonal compatibility of space crews has been and continues to be of interest to both NASA and the Institute of Biomedical Problems (IBMP), whose research informs operations for Roscosmos. In NASA-sponsored research, interpersonal compatibility has been examined in terms of how the combination of team members’ personality traits, values, and demographics shape team member relationships and team performance overtime. IBMP-sponsored research mostly has moved away from trait-based approaches toward an idiographic (in-depth, heavily descriptive) approach to researching crew interpersonal relations. This research uses software such as Personal Self-Perception and Attitudes (PSPA), network approaches to team member relations, and content analysis of interactions to assess interpersonal compatibility, and infer states and team dynamics. Our research program integrated these ideas. Our primary research aim was to develop and empirically test a process model of interpersonal relationship formation in isolated and confinement environments. We created a model, collected data from teams in an isolated and confined environment, and applied a novel analytical strategy that combines trait, state, and interaction data (i.e., relational events). We focused specifically on the formation of strained relationships in isolation and confinement—or with whom fellow crewmembers find it difficult to work.

S T Bell↗
DOE OSTI2020

Dalitz analysis of $D^0 → K^–π^+η$ decays at Belle

We present the results of the first Dalitz plot analysis of the decay D 0 → K – π + η . The analysis is performed on a data set corresponding to an integrated luminosity of 953 fb – 1 collected by the Belle detector at the asymmetric-energy e + e – KEKB collider. The Dalitz plot is well described by a combination of the six resonant decay channels K ¯ * ( 892 ) 0 η , K – a 0 ( 980 ) + , K – a 2 ( 1320 ) + , K ¯ * ( 1410 ) 0 η , K * ( 1680 ) – π + and K 2 * ( 1980 ) – π + , together with K π and K η S-wave components. The decays K * ( 1680 ) – → K – η and K 2 * ( 1980 ) – → K – η are observed for the first time. We measure ratio of the branching fractions, B ( D 0 → K – π + η ) B ( D 0 → K – π + ) = 0.500 ± 0.002 ( stat ) ± 0.020 ( syst ) ± 0.003 ( B PDG ) . Using the Dalitz fit result, the ratio B ( K * ( 1680 ) → K η ) B ( K * ( 1680 ) → K π ) is measured to be 0.11 ± 0.02 ( stat ) – 0.04 + 0.06 ( syst ) ± 0.04 ( B PDG ) ; this is much lower than the theoretical expectations ( ≈ 1 ) made under the assumption that K * ( 1680 ) is a pure 1 3 D 1 state. The product branching fraction B ( D 0 → [ K 2 * ( 1980 ) – → K – η ] π + ) = ( 2. 2 – 1.9 + 1.7 ) × 10 – 4 is determined. In addition, the π η ' contribution to the a 0 ( 980 ) ± resonance shape is confirmed with 10.1 σ statistical significance using the three-channel Flatté model. We also measure B ( D 0 → K ¯ * ( 892 ) 0 η ) = ( 1.4 1 – 0.12 + 0.13 ) % . This is consistent with, and more precise than, the current world average ( 1.02 ± 0.30 ) % , deviates with a significance of more than 3 σ from the theoretical predictions of (0.51–0.92)%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Batteries Included: Top 10 Findings from Berkeley Lab Research on the Growth of Hybrid Power Plants in the United States

One of the most important electric power system trends of the 2010s was the rapid deployment of wind turbines and photovoltaic arrays, but a twist for the 2020s may be the rapid deployment of ‘hybrid’ generation resources. Hybrid power plants typically combine solar or wind (or other energy sources) with co-located storage. While hybridization helps to ease the challenge of balancing variable supply and demand, its relative novelty means that research is needed to facilitate integration and promote innovation. Combining the characteristics of multiple energy, storage, and conversion technologies poses complex questions for grid operations and economics. Project developers, system operators, planners, and regulators would benefit from better data, methods, and tools to estimate the costs, values, and system impacts of hybrid projects. This publication showcases some of Berkeley Lab’s robust research program intended to support private- and public-sector decision-making about hybrid plants in the United States. Our short briefing summarizes articles that we published between 2020 and 2022, links to the in-depth reports, and provides contact details for further engagement on the specific research topics: Growth: Developer interest in hybrid power plants is strong and growing Price vs. Value: PV+storage hybrids have low PPA prices and high value in some regions Market Drivers: Solar hybridization is driven by tax credits and other benefits Configuration Choices: Market prices have incentivized shorter duration batteries with PV Capacity Value: The capacity contribution of a hybrid is less than the sum of its parts Ancillary Services: AS markets are a valuable yet fleeting option for hybrids Market Participation: Hybrids can more flexibly engage with electricity markets Operations: The power system value of hybrids depends on how they are operated Distributed Hybrids: Growth of customer-sited PV+storage hybrids offers new opportunities Future Research: Where next? Priority areas for hybrid power research.

25 ENERGY STORAGE↗
NASA NTRS1986

Application of photosynthetic N2-fixing cyanobacteria to the CELSS program

Commercially available air lift fermentors were used to simultaneously monitor biomass production, N2-fixation, photosynthesis, respiration, and sensitivity to oxidative damage during growth under various nutritional and light regimes, to establish a data base for the integration of these organisms into a Closed Ecological Life Support System (CELSS) program. Certain cyanobacterial species have the unique ability to reduce atmospheric N2 to organic nitrogen. These organisms combine the ease of cultivation characteristics of prokaryotes with the fully developed photosynthetic apparatus of higher plants. This, along with their ability to adapt to changes in their environment by modulation of certain biochemical pathways, make them attractive candidates for incorporation into the CELSS program.

Packer, L.↗
NASA NTRS1989

The use of COSMIC NASTRAN in an integrated conceptual design environment

Changes in both software and hardware are rapidly bringing conceptual engineering tools like finite element analysis into mainstream mechanical design. Systems that integrate all phases of the manufacturing process provide the most cost benefits. The application of programming concepts like object oriented programming allow for the encapsulation of intelligent data within the design geometry. This combined with declining cost in per seat hardware bring new alternatives to the user.

White, Gil↗