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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 OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

LaSDI: Parametric Latent Space Dynamics Identification

Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. Herein we introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving 0 (100)x speed-up and 0 (1)% relative error with respect to the corresponding full order models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗
DOE OSTI2020

Enabling fast charging of lithium-ion batteries through secondary-/dual- pore network: Part II - numerical model

To increase the market share of electric vehicles, it is desirable to reduce the battery charge times, which are significantly limited by poor electrolyte transport. A high rate charging is achievable by using expensive and low energy density cells with thin electrodes. For higher energy density cells, new electrolytes with improved conductivity and diffusivity and/or electrodes with advanced architecture are required to boost the electrolyte transport, leading to a more uniform utilization of active materials. In our previous work, an analytical model was developed to investigate the effect of secondary pore network (SPN) on electrolyte transport and the configuration of SPN was optimized by enforcing equal characteristic diffusion times in through-plane and in-plane directions. Here, to evaluate the effect of SPN on the fast-charging capability of lithium-ion batteries, a 2D physics-based electrochemical model is developed with SPN in either one or both electrodes. Additionally, the effect of SPN on cell energy density and lithium plating is investigated for cells with different loadings and electrode porosities. Combining SPN with elevated charging temperatures, the model predicts that the volumetric discharge energy density of a 3 mA h/cm2 cell can reach 270 Wh/L after a 6C constant-current charging.

25 ENERGY STORAGE↗
DOE OSTI2025

Opportunity Assessment for Sustainable Aviation Fuel Production from Woody Biomass via Ex Situ Catalytic Fast Pyrolysis and Refinery Hydroprocessing

Biofuels have become a promising solution to reduce emissions in hard-to-electrify transportation sectors, such as aviation. However, several biofuel conversion pathways will likely be needed to scale sustainable aviation fuel (SAF) production to meet lower carbon intensity goals when biomass feedstock availability constraints are considered. This study evaluates the future potential of the catalytic fast pyrolysis (CFP) pathway to contribute to U.S. SAF production goals as outlined in the U.S. Department of Energy’s (DOE) SAF Grand Challenge, understanding that the CFP pathway is still under development toward maturity and scaleup. National forestland resource data from the DOE’s 2023 Billion Ton Study are integrated with recent experimental results demonstrating end-to-end woody biomass conversion to SAF via CFP and hydroprocessing in a novel bioeconomy optimization framework. U.S. refinery hydroprocessing capacity is also considered with repurposing and coprocessing strategies. Results indicate that the CFP process can contribute up to 4.6 billion gallons of SAF annually by 2040, meeting 13% of the 2050 SAF Grand Challenge target while reducing the carbon intensity (CI) of the U.S. jet fuel pool by 16%. The study further explores optimal processing strategies, suggesting that colocating CFP operations with existing petroleum refineries and repurposing some U.S. hydrocracking capacity may offer significant cost advantages and enhance the commercial viability of the CFP pathway. These results underscore the CFP pathway’s potential to support the global aviation industry’s lower carbon intensity objectives while producing other renewable fuels and products.

09 BIOMASS FUELS↗
DOE OSTI2021

Optimal Operations Management of Mobility-on-Demand Systems

The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.

Wollenstein-Betech, Salomón↗
DOE OSTI2024

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as pro- teins, for growth optimization. Recent experiments have begun to reveal molecular com- ponents unpinning the partition; however, it remains unclear quantitatively how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation, and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that cellular ability of resource partition lies in an ultrasensitive, negative feedback control- ling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism which mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations, and is also efficient for biomass production over time. Furthermore, this work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2022

NSTX-U theory, modeling and analysis results

Here, the mission of the low aspect ratio spherical tokamak NSTX-U is to advance the physics basis and technical solutions required for optimizing the configuration of next-step steady-state tokamak fusion devices. NSTX-U will ultimately operate at up to 2 MA of plasma current and 1 T toroidal field on axis for 5 s, and has available up to 15 MW of neutral beam injection power at different tangency radii and 6 MW of high harmonic fast wave heating. With these capabilities NSTX-U will develop the physics understanding and control tools to ramp-up and sustain high performance fully non-inductive plasmas with large bootstrap fraction and enhanced confinement enabled via the low aspect ratio, high beta configuration. With its unique capabilities, NSTX-U research also supports ITER and other critical fusion development needs. Super-Alfvénic ions in beam-heated NSTX-U plasmas access energetic particle (EP) parameter space that is relevant for both α-heated conventional and low aspect ratio burning plasmas. NSTX-U can also generate very large target heat fluxes to test conventional and innovative plasma exhaust and plasma facing component solutions. This paper summarizes recent analysis, theory and modelling progress to advance the tokamak physics basis in the areas of macrostability and 3D fields, EP stability and fast ion transport, thermal transport and pedestal structure, boundary and plasma material interaction, RF heating, scenario optimization and real-time control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reinforcement Learning for In-Spill Optimization of the Mu2e Resonant Extraction: Compensating Non-Stationarity

We present design considerations and challenges for the fast machine learning component of a third-order resonant beam extraction regulation system being commissioned to deliver steady beam rates to the mu2e experiment at Fermilab. Dedicated quadrupoles drive the tune toward the 29/3 resonance each spill, extracting beam at kV multiwire septa. The overall Spill Regulation System consists of (1) a “slow” process using ~100-spill averages to adjust the base quad ramp infrequently, (2) a feedforward harmonic content compensator, and (3) the “fast” ML agent reacting during each ongoing spill with on-the-fly additive corrections to the sum of (1) and (2). We have demonstrated improved beam-rate steadying for a fast ML agent compared to a PID controller using a quasi-physical spill simulation, and demonstrated distillation of that simulation into a predictive surrogate model. Current work includes a data-and-training pipeline to generate data-aware surrogates with real-world dynamics, even as the dynamics shift unpredictably. The surrogates are to act as RL environments against which to train our fast ML control agents before deploying them on FPGA in the live system. Further current efforts focus on modeling and controlling beam loss around the storage ring, understanding additional available hardware inputs to the model, and the interplay of these with beam-steadying performance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Coil optimization for quasi-helically symmetric stellarator configurations

Filament-based coil optimizations are performed for several quasi-helical stellarator configurations, beginning with the one from Landreman & Paul ( Phys. Rev. Lett. , vol. 128, 2022, 035001), demonstrating that precise quasi-helical symmetry can be achieved with realistic coils. Several constraints are placed on the shape and spacing of the coils, such as low curvature and sufficient plasma–coil distance for neutron shielding. The coils resulting from this optimization have a maximum curvature 0.8 times that of the coils of the Helically Symmetric eXperiment (HSX) and a mean squared curvature 0.4 times that of the HSX coils when scaled to the same plasma minor radius. When scaled up to reactor size and magnetic field strength, no fast particle losses were found in the free-boundary configuration when simulating 5000 alpha particles launched at $3.5\,\mathrm {MeV}$ on the flux surface with a normalized toroidal flux of $s=0.5$ . An analysis of the tolerance of the coils to manufacturing errors is performed using a Gaussian process model, and the coils are found to maintain low particle losses for smooth, large-scale errors up to amplitudes of approximately $0.15\,\mathrm {m}$ . Another coil optimization is performed for the Landreman–Paul configuration with the additional constraint that the coils are purely planar. Visual inspection of the Poincaré plot of the resulting magnetic field-lines reveal that the planar modular coils alone do a poor job of reproducing the target equilibrium. Additional non-planar coil optimizations are performed for the quasi-helical configuration with $5\,\%$ volume-averaged plasma beta from Landreman et al. ( Phys. Plasma , vol. 29, issue 8, 2022, 082501), and a similar configuration also optimized to satisfy the Mercier criterion. The finite beta configurations had larger fast-particle losses, with the free-boundary Mercier-optimized configuration performing the worst, losing approximately $5.5\,\%$ of alpha particles launched at $s=0.5$ .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

GraphTango: A Hybrid Representation Format for Efficient Streaming Graph Updates and Analysis

Abstract Streaming graph processing performs batched updates and analytics on a time-evolving graph. The underlying representation format of the graph largely determines the throughputs of these updates and analytics phases. Existing representation formats usually employ variations of hash tables or adjacency lists. However, a recent study showed that the adjacency-list-based approaches perform poorly on heavy-tailed graphs, and the hash table-based approaches suffer on short-tailed graphs. We propose GraphTango, a hybrid representation format that provides excellent update and analytics throughput regardless of the graph’s degree distribution. GraphTango dynamically switches among three different formats based on a vertex’s degree: (i) Low-degree vertices store the edges directly with the neighborhood metadata, confining accesses to a single cache line, (2) Medium-degree vertices use adjacency lists, and (3) High-degree vertices use hash tables as well as adjacency lists. In this case, the adjacency list provides fast traversal during the analytics phase, while the hash table provides constant-time lookups during the update phase. We further optimized the performance by designing an open-addressing-based hash table that fully utilizes every fetched cache line. In addition, we developed a thread-local lock-free memory pool that allows fast growing/shrinking of the adjacency lists and hash tables in a multi-threaded environment. We evaluated GraphTango with the help of the SAGA-Bench framework and compared it with four other representation formats: Stinger, Degree-aware Robin Hood Hashing, and two adjacency list-based formats with different workload balancing scheme. On average, GraphTango provides 4.5x higher insertion throughput, 3.2x higher deletion throughput, and 1.1x higher analytics throughput over the next best format. Furthermore, we integrated GraphTango with the state-of-the-art graph processing frameworks DZiG and RisGraph. Compared to the vanilla DZiG and vanilla RisGraph , [ GraphTango + DZiG ] and [ GraphTango + RisGraph ] reduces the average batch processing time by 2.3x and 1.5x, respectively.

Ahmed, Alif↗
DOE OSTI2020

An analysis of the spatio-temporal resolution of the immersed boundary method with direct forcing

The immersed boundary method (IBM) with direct forcing is very popular in the simulation of rigid particulate flows. In the IBM, an interaction force is introduced at the interface between fluid and particle in order to approximate the no-slip boundary condition. The interaction force is calculated through dividing the velocity difference (or error) between fluid and particle at the interface by the time step. Here, a dynamic equation for the velocity difference is derived. Additionally, analyses on the dynamic equation provide a few new findings: (i) The interaction force is the solution of a least-squares error problem, with the direct implication that the Lagrangian marker distribution has no effect on the large scale flow structure once the distribution of Lagrangian markers become saturated along the interface (i.e., each marker remains properly correlated with all its neighbors); (ii) The Lagrangian volume-weight is a relaxation factor to control how fast the velocity error decays to the ideal value of zero; (iii) The optimal choice of the Lagrangian volume-weight is the largest value permissible by a stability condition. A comprehensive convergence analysis with regard to the spatial and temporal resolution is presented for the velocity error and also for the shear-stress and surface pressure. In three simple canonical problems, it is analytically and numerically shown that the IBM results converge to the theoretical solutions obtained with precise imposition of no-slip and no-penetration boundary conditions. It is observed that it is not necessary to match the Lagrangian marker volume-weight to that of the local Eulerian cell volume and in fact this matching leads to lower than optimal computational efficiency. However, it is found that extremely high Eulerian grid resolution and small time step have to be used to obtain high precision simulation results. Especially, the time step should be inversely proportional to the particle Reynolds number for low Reynolds number flows. For high frequency oscillation problems, the grid size needs to be reduced by a factor of the square root of the frequency, and the time step to be reduced by a factor of the frequency. The theoretical findings here can be used to alleviate the technical difficulties in simulating non-spherical particles by not requiring the Lagrangian marker distribution to match the Eulerian grids and also in the implementation of IBM on non-uniform Eulerian grids. The present work also provides simple practical guidance on the choice of temporal and spatial resolution so as to control the simulation error a priori.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The Development of a Generalized Riser Flow Regime Map Based Upon Higher Moment and Chaotic Statistics Using Electrical Capacitance Volume Tomography (ECVT)

Dynamic analyses have been applied to the temporal signals from an Electro Capacitance Volume Tomography instrument located near mid-height on the riser of an industrial-scale cold-flow circulating fluidized bed to characterize gas-solids flow behavior in the riser. Twelve capacitance electrodes surround the cylindrical riser over a height of 1.3 m. The instrument used a neural network deconvolution algorithm to determine the spatially resolved solids fraction recorded at 52 Hz. Experiments were carried out over a range of gas and solids flows in the transport regime using a Geldart Group B bed material, high density polyethylene with mean particle size of 880 μm. The radial solids distribution was found to vary from one-time step to the next between profiles typical of laminar and turbulent flow. The duration of time spent in each of these flow profiles depended upon the operating regime – dilute, core-annular, or fast fluidized bed. The chaotic structure of the temporal data was characterized using the three conventional approaches: the first 4 moments from the distribution of signal in time, system memory parameters from the autocorrelation function and the Hurst exponent, and analysis of the correlationentropy and correlation dimension of the attractor. These signal analysis techniques were used to clearly distinguish differences between different transport operating regimes. Specifically, it was experimentally observed that a riser transitions from core annular flow profile to dilute and dense regimes via increasing the frequency of short term transients to either dilute or dense flow profiles, respectively. A regime map was generated based upon these dynamics using solids flux and gas velocity axes. Fast fluidized, core annular, and dilute each exhibited different degree of dynamic characteristics typical of fluid dominated or particle compromising behavior. It should be noted that the magnitude for the different statistics was in the same range regardless of the regime, it was the radial profile for the statistic that changed and subsequently identified that there was a change in the regime. Finally, a reduced regime map was developed consisting of plotting the gas velocity normalized by the upper transport velocity versus the solids flux normalized by the saturation carrying capacity. The use of this reduced plot allowed the data from widely different conditions to be plotted and compared on the same<p>graph. Note that in many instances, some of the statistics identified the operating point as being in one regime while others indicated that it was in another indicating a transition region between dilute or core annular regimes and between the core annular and fast fluidization regimes. This now provides a tool that can be used to optimize process performance, identify changes in operating states, or replicate process dynamics during process scaling or changing operating parameters. </p>

Breault, Ronald↗
DOE OSTI2020

Developing Scaling Relationships for Molecular Electrocatalysis through Studies of Fe-Porphyrin-Catalyzed O 2 Reduction

The oxygen reduction reaction (ORR) is a multiproton/multielectron transformation in which dioxygen (O 2 ) is reduced to water or hydrogen peroxide and serves as the cathode reaction in most fuel cells. The ORR (O 2 + 4e – + 4H + → 2H 2 O) involves up to nine substrates and thus requires navigating a complicated reaction landscape, typically with several high-energy intermediates. Many catalysts can perform this reaction, though few operate with fast rates and at low overpotentials (close to the thermodynamic potential). Attempts to optimize these parameters, both in homogeneous and heterogeneous electrocatalytic systems, have focused on modifying catalyst design and understanding kinetic/thermodynamic relationships between catalytic intermediates. One such method for analyzing and predicting catalyst reactivity and efficiency has been the development of “molecular scaling relationships”. In this work, we share our experience deriving and utilizing molecular scaling relationships for soluble, iron-porphyrin-catalyzed O 2 reduction in organic solvents. These relationships correlate turnover frequencies (TOF max ) and effective overpotentials (η eff ), properties uniquely defined for homogeneous catalysts. Following a general introduction of scaling relationships for both homogeneous and heterogeneous electrocatalysis, we describe the components of such scaling relationships: (i) the overall thermochemistry of the reaction and (ii) the rate and rate law of the catalyzed reaction. We then show how connecting these thermodynamic and kinetic parameters reveals multiple molecular scaling relationships for iron-porphyrin-catalyzed O 2 reduction. For example, the log(TOF max ) responds steeply to changes in ηeff that result from different catalyst reduction potentials (18.5 decades in TOF max /V in η eff ) but much less dramatically to changes in η eff that arise from varying the pKa of the acid buffer (5.1 decades in TOF max /V in η eff ). Thus, a single scaling relationship is not always sufficient for describing molecular electrocatalysis. This is particularly evident when the catalyst identity and reaction conditions are coupled. Using these multiple scaling relationships, we demonstrate that the metrics of turnover frequency and effective overpotential can be predictably tuned to achieve faster rates at lowered overpotentials. This Account uses a collection of related stories describing our research on soluble iron-porphyrin-catalyzed ORR to show how molecular scaling relationships can be derived and used for any electrocatalytic reaction. Such scaling relationships are powerful tools that connect the thermochemistry, mechanism, and rate law for a catalytic system. We hope that this collection shows the utility and simplicity of the molecular scaling approach for understanding catalysis, for enabling direct comparisons between catalyst systems, and for optimizing catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Multiprincipal Component P2-Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 as a High-Rate Cathode for Sodium-Ion Batteries

Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g –1 at 0.1 C and 68 mAh g –1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Image processing tools for petabyte-scale light sheet microscopy data

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson–Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

QUBIC II: Spectral polarimetry with bolometric interferometry

Bolometric interferometry is a novel technique that has the ability to perform spectral imaging. A bolometric interferometer observes the sky in a wide frequency band and can reconstruct sky maps in several sub-bands within the physical band in post-processing of the data. This provides a powerful spectral method to discriminate between the cosmic microwave background (CMB) and astrophysical foregrounds. In this paper, the methodology is illustrated with examples based on the Q & U Bolometric Interferometer for Cosmology (QUBIC) which is a ground-based instrument designed to measure the B-mode polarization of the sky at millimeter wavelengths. We consider the specific cases of point source reconstruction and Galactic dust mapping and we characterize the point spread function as a function of frequency. We study the noise properties of spectral imaging, especially the correlations between sub-bands, using end-to-end simulations together with a fast noise simulator. Finally, we conclude showing that spectral imaging performance are nearly optimal up to five sub-bands in the case of QUBIC.

79 ASTRONOMY AND ASTROPHYSICS↗