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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Here in this paper, a physics-data-based detection method is proposed to detect a variety of cyber-attacks in Photovoltaic (PV) farms using the power electronics-enabled harmonic state space (HSS) models, which, to our knowledge, is original. At the device level, HSS-based detection is developed to monitor harmonic vectors of individual PV converter with minimum sensor measurements, thus improving accuracy and robustness compared to Kalman Filter-based detection. At the system level that involves multiple PV converters, a clustering approach is developed to investigate attack propagation and accurately locate attack sources within a PV farm. The proposed approach is one of the first attempts to address PV security through interaction between the device and system, maximizing the accuracy and robustness at different levels. To verify the feasibility, a comprehensive attacks model is built, including single attack, coordinated attacks, and replay attacks. Besides, the impacts of irradiance changes are taken into consideration in the test scenarios. With the real-time data acquisition and hardware-in-the-loop testbed, comprehensive test results are provided to verify the feasibility of the proposed detection methodology.

42 ENGINEERING↗
DOE OSTI2025

Interacting dark sector within ETHOS: Cosmological constraints from SPT cluster abundance with DES and HST weak lensing data

We use galaxy cluster abundance measurements from the South Pole Telescope enhanced by multicomponent matched filter confirmation and complemented with mass information obtained using weak-lensing data from Dark Energy Survey Year 3 (DES Y3) and targeted Hubble Space Telescope observations for probing deviations from the cold dark matter paradigm. Concretely, we consider a class of dark sector models featuring interactions between dark matter (DM) and a dark radiation (DR) component within the framework of the effective theory of structure formation (ETHOS). We focus on scenarios that lead to power suppression over a wide range of scales, and thus can be tested with data sensitive to large scales, as realized, for example, for DM–DR interactions following from an unbroken non-Abelian 𝑆⁢𝑈⁡(𝑁) gauge theory (interaction rate with power-law index 𝑛 = 0 within the ETHOS parametrization). Cluster abundance measurements are mostly sensitive to the amount of DR interacting with DM, parametrized by the ratio of DR temperature to the cosmic microwave background (CMB) temperature, 𝜉 DR = 𝑇 DR /𝑇 CMB . We find an upper limit 𝜉 DR < 17% at 95% credibility. When the cluster data are combined with Planck 2018 CMB data along with baryon acoustic oscillation (BAO) measurements we find 𝜉 DR < 10%, corresponding to a limit on the abundance of interacting DR that is around 3 times tighter than that from CMB + BAO data alone. We also discuss the complementarity of weak lensing informed cluster abundance studies with probes sensitive to smaller scales, explore the impact on our analysis of massive neutrinos, and comment on a slight preference for the presence of a nonzero interacting DR abundance, which enables a physical solution to the 𝑆 8 tension.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2021

ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing

The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this work, we propose ARENA – an asynchronous reconfigurable accelerator ring architecture as a potential scenario on how the future HPC and data centers will be like. Despite using the coarse-grained reconfigurable arrays (CGRAs) as the substrate platform, our key contribution is not only the CGRA-cluster design itself, but also the ensemble of a new architecture and programming model that enables asynchronous tasking across a cluster of reconfigurable nodes, so as to bring specialized computation to the data rather than the reverse. We presume distributed data storage without asserting any prior knowledge on the data distribution. Hardware specialization occurs at runtime when a task finds the majority of data it requires are available at the present node. In other words, we dynamically generate specialized CGRA accelerators where the data reside. The asynchronous tasking for bringing computation to data is achieved by circulating the task token, which describes the dataflow graphs to be executed for a task, among the CGRA cluster connected by a fast ring network. Evaluations on a set of HPC and data-driven applications across different domains show that ARENA can provide better parallel scalability with reduced data movement (53.9 percent). Compared with contemporary compute-centric parallel models, ARENA can bring on average 4.37× speedup. The synthesized CGRAs and their task-dispatchers only occupy 2.93mm 2 chip area under 45nm process technology and can run at 800MHz with on average 759.8mW power consumption. ARENA also supports the concurrent execution of multi-applications, offering ideal architectural support for future high-performance parallel computing and data analytics systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Studying CPU and memory utilization of applications on Fujitsu A64FX and Nvidia Grace Superchip

ARM-based manycore CPU architectures are well-positioned to provide the rising memory throughput requirements of modern data intensive scientific applications in High Performance Computing (HPC). The Fujitsu A64FX CPU platform is based on the ARM v8.2A architecture, and is the processor of the flagship Japanese supercomputer - "Fugaku", which was previously ranked as the #1 supercomputer in the world according to the Top500 list. The Nvidia Grace superchip features 144 Neoverse V2 cores based on the ARMv9 architecture with 4x128b SVE2, providing exceptional computational power. The chip supports up to 480GB of memory, making it ideal for AI, machine learning, and scientific computing workloads. In this paper, we conduct a thorough performance exploration of a variety of parallel bandwidth-sensitive benchmarks and applications compiled with the native Fujitsu compiler on a Fugaku A64FX compute node and ARM (LLVM) Compiler on an NVIDIA Grace superchip compute node, engaging all the computational cores per cluster using OpenMP multithreading (assuming the cores can drive the available bandwidth). Our ultimate goals are to study the resource utilization of scientific applications and benchmarks on A64FX and Grace superchip, considering graph application scenarios ( GAP Benchmark suite) and eleven appli- cation proxies from the Rodinia heterogeneous benchmark suite (considering domains such as Data Mining, Bioinformatics, Fluid Dynamics, Pattern Recognition, etc.). Through exhaustive performance monitoring, we quantify the resource utilization of diverse OpenMP-based HPC applications on both the Fujitsu A64FX and the Nvidia Grace Superchip platforms.

benchmarking, Performance Analysis, High performan↗
DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗
DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗
DOE OSTI2025

A Melanoma Brain Metastasis CTC Signature and CTC:B-cell Clusters Associate with Secondary Liver Metastasis: A Melanoma Brain–Liver Metastasis Axis

Melanoma brain metastasis is linked to dismal prognosis and low overall survival and is detected in up to 80% of patients at autopsy. Circulating tumor cells (CTC) are the smallest functional units of cancer and precursors of fatal metastasis. We previously used an unbiased multilevel approach to discover a unique ribosomal protein large/small subunit (RPL/RPS) CTC gene signature associated with melanoma brain metastasis. In this study, we hypothesized that CTC-driven melanoma brain metastasis secondary metastasis (“metastasis of metastasis” per clinical scenarios) has targeted organ specificity for the liver. We injected parallel cohorts of immunodeficient and newly developed humanized NBSGW (huNBSGW) mice with cells from CTC-derived melanoma brain metastasis to identify secondary metastatic patterns. We found the presence of a melanoma brain–liver metastasis axis in huNBSGW mice. Furthermore, RNA sequencing analysis of tissues showed a significant upregulation of the RPL/RPS CTC gene signature linked to metastatic spread to the liver. Additional RNA sequencing of CTCs from huNBSGW blood revealed extensive CTC clustering with human B cells in these mice. CTC:B-cell clusters were also upregulated in the blood of patients with primary melanoma and maintained either in CTC-driven melanoma brain metastasis or melanoma brain metastasis CTC–derived cells promoting liver metastasis. CTC-generated tumor tissues were interrogated at single-cell gene and protein expression levels (10x Genomics Xenium and HALO spatial biology platforms, respectively). Collectively, our findings suggest that heterotypic CTC:B-cell interactions can be critical at multiple stages of metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Variable stars in Local Group galaxies – V. The fast and early evolution of the low-mass Eridanus II dSph galaxy

ABSTRACT We present a detailed study of the variable star population of Eridanus II (Eri II), an ultra-faint dwarf galaxy that lies close to the Milky Way virial radius. We analyse multi-epoch g, r, i ground-based data from Goodman and the Dark Energy Camera, plus F475W, F606W, F814W space data from the Advanced Camera for Surveys. We report the detection of 67 RR Lyrae (RRL) stars and 2 Anomalous Cepheids, most of them new discoveries. With the RRL stars, we measure the distance modulus of Eri II, μ0 = 22.84 ± 0.05 mag (D⊙ = 370 ± 9 kpc), and derive a metallicity spread of 0.3 dex (0.2 dex intrinsic). The colour distribution of the horizontal branch (HB) and the period distribution of the RRL stars can be nicely reproduced by a combination of two stellar models of [Fe/H] = (−2.62, −2.14). The overall low metallicity is consistent with the red giant branch bump location, 0.65 mag brighter than the HB. These results are in agreement with previous spectroscopic studies. The more metal-rich RRL and the RRab stars have greater central concentration than the more metal-poor RRL and the RRc stars that are mainly located outside ∼1 rh. This is similar to what is found in larger dwarf galaxies such as Sculptor, and in agreement with an outside-in galaxy formation scenario. This is remarkable in such a faint dwarf galaxy with an apparently single and extremely short (<1 Gyr) star formation burst. Finally, we have derived new and independent structural parameters for Eri II and its star cluster using our new data that are in very good agreement with previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Resolving Salt-Induced Agglomeration of Laponite Suspensions Using X-ray Photon Correlation Spectroscopy and Molecular Dynamics Simulations

Linking the physics of the relaxation behavior of viscoelastic fluids as they form arrested gel states to the underlying chemical changes is essential for developing predictive controls on the properties of the suspensions. In this study, 3 wt.% laponite suspensions are studied as model systems to probe the influence of salt-induced relaxation behavior arising from the assembly of laponite nanodisks. X-ray Photon Correlation Spectroscopy (XPCS) measurements show that laponite suspensions prepared in the presence of 5 mM concentrations of CaCl2, MgCl2 and CsCl salts accelerate the formation of arrested gel states, with CaCl2 having a significant impact followed by CsCl and MgCl2 salts. The competing effects of ion size and charge on relaxation behavior are noted. For example, the relaxation times of laponite suspensions in the presence of Mg2+ ions are slower compared to Cs+ ions despite the higher charge, suggesting that cation size dominates in this scenario. The faster relaxation behavior of laponite suspensions in the presence of Ca2+ ions compared to Cs+ ions shows that a higher charge dominates the size of the ion. The trends in relaxation behavior are consistent with the cluster formation behavior of laponite suspensions and the electrostatic interactions predicted from MD simulations. Charge balance is achieved by the intercalation of the cations at the negatively charged surfaces of laponite suspensions. These studies show that the arrested gel state of laponite suspensions is accelerated in the presence of salts, with ion sizes and charges having a competing effect on relaxation behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exploiting Synergies Between Lensing and BAO Surveys for Improved Cosmological Constraints

Pinning down the nature of dark energy is one of the most pressing questions in modern physics and is one of the five key science drivers identified in the P5 report [P514]. Dark energy is thought to be either a cosmological constant with an equation of state w = P/ρ = −1 which remains constant at all times, a new type of fluid with an equation of state that varies with time (w ̸= constant), or dark energy might indicate a breakdown of Einstein’s Theory of General Relativity (GR). It is of critical importance to distinguish between these three scenarios. This can only be accomplished by ambitious and demanding measurements of both the expansion rate of the universe (to track the time evolution of dark energy) together with measurements of the rate at which cosmic structures, such as galaxies and clusters of galaxies, grow with time (the “growth rate”). Because of the challenging nature of such observations, and because no single probe simultaneously measures both expansion and growth, the Dark Energy Task Force [A+06] emphasized the importance of using multiple distinct methods to characterize dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?

Abstract The historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Formation of S0s in extreme environments Ⅲ: the role of environment in the formation pathways

It is well established that there are at least two main channels to form lenticular (or S0) galaxies. The first, which we name ‘faded spiral’ scenario, includes quenching events that led to consumption or removal of gas from a spiral progenitor. The second, which we call ‘merger’ scenario, includes merger-like events and interactions between galaxies. Each scenario leaves characteristic signatures in the newly formed lenticular galaxy. However, the conditions that trigger one mechanism over another are still unknown. This paper is the third of a series aimed at understanding the role of the environment in the formation of lenticular galaxies. In this study, we combine the kinematics, morphology, and properties of the stellar populations of 329 S0s from the SAMI and MaNGA surveys in order to highlight the role of the environment in the process. We divide the S0s into two classes (A and B) according to their global properties, that we can associate to the products of a faded spiral scenario (class A) or a merger scenario (class B). Here we then study how the various classes are distributed within different environments. Our study reveals that the ‘faded spiral’ pathway is the most efficient channel to produce S0s, and it becomes more efficient as the mass of the group or cluster or local density of galaxies increase. The merger pathway is also a viable channel, and its efficiency becomes higher with decreasing local density or environment mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Environmental Evaluation of Gas Switching Reforming for Low Carbon Hydrogen: A Power-to-X Study

Gas Switching Reforming for hydrogen production (GSR-H2) offers a promising pathway for producing low-carbon hydrogen at scale, with significant implications for Power-to-X (PtX) systems that rely on clean hydrogen as a feedstock for synthetic fuels and chemicals. GSR-H2 integrates inherent carbon capture and thermal self-sufficiency, positioning it as an efficient alternative to conventional steam methane reforming (SMR), proton exchange membrane (PEM) electrolysis, and chemical looping reforming (CLR). Unlike SMR, GSR-H2 avoids external natural gas combustion by leveraging exothermic redox cycles to generate process steam and recover electricity internally. Its innovative reactor design consolidates all reforming stages within a single reactor cluster, eliminating the need for solid circulation found in CLR, thereby reducing capital costs and improving system reliability and scalability. This presentation describes the first environmental life cycle assessment (LCA) of GSR-H2, evaluating its environmental performance across U.S. grid and renewable energy scenarios. In a renewables-powered configuration, GSR-H2 achieves a global warming potential (GWP) of 2.77 kg CO2 equivalent per kg H2, substantially lower than SMR (10.4 kg) and competitive with PEM electrolysis (1.85 kg) and CLR (1.84 kg). Results across additional impact categories, including air quality and water use, support GSR-H2’s role as a complementary hydrogen source in PtX applications. Its reduced environmental burden, thermal integration, and simplified scale-up potential make GSR-H2 a viable contributor to net-zero PtX systems, particularly where renewable energy is abundant and electricity-intensive hydrogen production faces economic or infrastructure constraints.

03 NATURAL GAS↗