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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 541 records · Page 30

Uncovering the True Active Sites in Ni–N–C Catalysts for CO 2 Electroreduction

Understanding and designing active sites in single-atom catalysts (SACs) requires going beyond static models to capture their dynamic evolution under realistic electrochemical conditions. Here, in this work, we develop an integrated theoretical framework that accounts for operational conditions, by combining grand canonical density functional theory (GC-DFT) with machine-learning-accelerated sampling, to uncover structure–activity–stability relationships in Ni–N–C SACs for the CO 2 reduction reaction (CO 2 RR). A library of NiN x C 4–x (x = 0–4) motifs─representing coordination defects likely formed during high-temperature synthesis─was systematically evaluated. Under working conditions, these sites were found to undergo hydrogenation, and NiN 3 C 1_ H 1 was identified as the most probable active site. At reducing potentials, hydrogen adsorbs spontaneously at C–Ni bridge sites rather than Ni top sites, while subsurface hydrogen facilitates bent CO 2 adsorption crucial for activation. High CO 2 RR selectivity toward CO arises from site separation: Ni centers drive CO2RR, while the hydrogen evolution reaction (HER) occurs at the C–Ni bridge or N sites and from thermodynamic suppression of HER at moderate hydrogen coverage. At more negative potentials, a shift in the CO 2 RR rate-determining process (RDP) and Ni out-of-surface displacement induced by coadsorption of H and H 2 O jointly reduce activity and selectivity. Thus, both the high CO2RR selectivity of Ni–N–C catalysts and its reversal with more negative potentials can be rationalized by accounting for hydrogenated surfaces. This highlights the necessity of modeling realistic; in situ conditions. This framework provides generalizable insights into the dynamic behavior of active sites in SACs, offering guidance for the rational design of active and robust catalysts for a wide range of electrochemical reactions.

25 ENERGY STORAGE↗

A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience

U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.

Brigantic, Robert T.↗

Dynamically Reconfigurable Systolic Array Accelorators

A polymorphic systolic array framework that works in conjunction with an embedded microprocessor on an FPGA, that allows for dynamic and complimentary scaling of acceleration levels of two algorithms active concurrently on the FPGA. Use is made of systolic arrays and hardware-software co-design to obtain an efficient multi-application acceleration system. The flexible and simple framework allows hosting of a broader range of algorithms and extendable to more complex applications in the area of aerospace embedded systems.

Dasu, Aravind↗

The interplay between chromophore and protein determines the extended excited state dynamics in a single-domain phytochrome

Phytochromes are a diverse family of bilin-binding photoreceptors that regulate a wide range of physiological processes. Their photochemical properties make them attractive for applications in optogenetics and superresolution microscopy. Phytochromes undergo reversible photoconversion triggered by the Z ⇌ E photoisomerization about the double bond in the bilin chromophore. However, it is not fully understood at the molecular level how the protein framework facilitates the complex photoisomerization dynamics. In this work, we have studied a single-domain bilin-binding photoreceptor All2699g1 ( Nostoc sp. PCC 7120) that exhibits photoconversion between the red light-absorbing (P r ) and far red-absorbing (P fr ) states just like canonical phytochromes. We present the crystal structure and examine the photoisomerization mechanism of the P r form as well as the formation of the primary photoproduct Lumi-R using time-resolved spectroscopy and hybrid quantum mechanics/molecular mechanics simulations. We show that the unusually long excited state lifetime (broad lifetime distribution centered at ~300 picoseconds) is due to the interactions between the isomerizing pyrrole ring D and an adjacent conserved Tyr142. The decay kinetics shows a strongly distributed character which is imposed by the nonexponential protein dynamics. Our findings offer a mechanistic insight into how the quantum efficiency of the bilin photoisomerization is tuned by the protein environment, thereby providing a structural framework for engineering bilin-based optical agents for imaging and optogenetics applications.

59 BASIC BIOLOGICAL SCIENCES↗

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving the training and computational efficiency of machine-learned interatomic potentials. We demonstrate the framework on Ca 2+ –CO 3 2– ion pairing in aqueous solution, a key process underlying CO 2 mineralization in seawater. As proof of principle, we first show that fine-tuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. The computed ion-pair association free energy is in quantitative agreement with experimental measurements, further validating the accuracy of the ECW-TL framework. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to efficient simulations of complex aqueous and interfacial chemical processes.

cluster chemistry↗

Framework for Analyzing the Complex Interactions Between Spacecraft Motion and Slosh Dynamics in Low-G Environments

The fuel-to-dry-mass ratio of spacecraft continues to grow as new human spaceflight missions target destinations from Earth. Large amounts of liquid propellant can lead to significant coupling between the rigid-body dynamics of the spacecraft and the motion of the fuel within its storage tank. The present work gives an overview of the dynamic features and a flowchart for a method of simulating the motion of a spacecraft with fuel slosh inside a cylindrical, domed tank in a low-g environment. The method involves modeling the liquid propellant as a particle that transfers momentum to the spacecraft through perfectly inelastic collisions with the tank wall. The foundation of the modeling methodology is the approach taken during the Apollo program used to predict the effect of fuel slosh on the complex motion exhibited by the Service Module following separation from the Crew Module. This paper discusses the motivation, methodology, and conclusions from the Apollo-era method, and subsequently builds upon it by incorporating corrections to the derivation of the dynamics and filling in the gaps due to unavailability of the detailed contractor report and simulation code. The results presented in this paper provide an example that demonstrates the effect that fuel slosh can have on the trajectory of a spacecraft in a low-g environment.

William J Elke III↗

Framework for Analyzing the Complex Interactions Between Spacecraft Motion and Slosh Dynamics in Low-G Environments

The fuel-to-dry-mass ratio of spacecraft continues to grow as new human spaceflight missions target destinations farther from Earth. Large amounts of liquid propellant can lead to significant coupling between the rigid-body dynamics of the spacecraft and the motion of the fuel within its storage tank. The present work gives an overview of the dynamic features and a flowchart for a method of simulating the motion of a spacecraft with fuel slosh inside a cylindrical, domed tank in a low-g environment. The method involves modeling the liquid propellant as a particle that transfers momentum to the spacecraft through perfectly inelastic collisions with the tank wall. The foundation of the modeling methodology is the approach taken during the Apollo program used to predict the effect of fuel slosh on the complex motion exhibited by the Service Module following separation from the Command Module. This paper discusses the motivation, methodology, and conclusions from the Apollo-era method, then presents corrections to the derivation of the dynamics and fills in the gaps left from the unavailability of the detailed contractor report and simulation code. The results presented in this paper provide an example that demonstrates the effect that fuel slosh can have on the trajectory of a spacecraft in a low-g environment.

William J Elke III↗

Direct Observation of the ππ* to nπ* Transition in 2-Thiouracil via Time-Resolved NEXAFS Spectroscopy

The photophysics of nucleobases has been the subject of both theoretical and experimental studies over the past decades due to the challenges posed by resolving the steps of their radiationless relaxation dynamics, which cannot be described in the framework of the Born–Oppenheimer approximation (BOA). In this context, the ultrafast dynamics of 2-thiouracil has been investigated with a time-resolved NEXAFS study at the Free Electron Laser FLASH. Near Edge X-ray Absorption Fine Structure spectroscopy (NEXAFS) can be used to observe electronic transitions in ultrafast molecular relaxation. We performed time-resolved UV-pump/X-ray probe absorption measurements at the sulfur 2s (L1) and 2p (L2/3) edges. We are able to identify absorption features corresponding to the S2 (ππ*) and S1 (nπ*) electronic states. We observe a delay of 102 ± 11 fs in the population of the nπ* state with respect to the initial optical excitation and interpret the delay as the time scale for the S2 → S1 internal conversion. We furthermore identify oscillations in the absorption signal that match a similar observation in our previous X-ray photoelectron spectroscopy study on the same molecule.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Characterizing the California Current System through Sea Surface Temperature and Salinity

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Marisol García-Reyes↗

Control-oriented modeling of combustion and flow processes in liquid propellant rocket engines

This paper presents a control-oriented model of the flow, reaction, and transport processes in liquid propellant rocket combustion chambers, based on the multicomponent conservation laws of gas dynamics. This model provides a framework for the inclusion of detailed chemical kinetic relations, viscous and other dissipative effects, a variety of actuators and sensors, as well as process and measurement disturbances. In addition to its potential usefulness to the designer in understanding the dynamical complexity of the system and the sources of model uncertainty, the model provides a rigorous basis for control system design. An appraisal of current and feasible actuators and sensors, and their mathematical representation are included.

Bentsman, Joseph↗

The Effect of Stochastic Acceleration on the Dynamics of Solar Energetic Particles in the Heliosphere

Solar Energetic Particles (SEPs) are crucial in space weather phenomena, impacting space-based technologies and human activities in space. Understanding the dynamics of SEPs, including their acceleration and transport mechanisms, is essential for improving predictive models and mitigating adverse effects. This study investigates the impact of stochastic acceleration (SA) on the dynamics of SEPs within the heliosphere. By integrating stochastic acceleration into a SEP transport model, we assess its impact relative to diffusive shock acceleration (DSA). The investigation utilizes the Adaptive Mesh Particle Simulator (AMPS) within the Space Weather Modeling Framework (SWMF) to simulate SEP dynamics, incorporating solar wind parameters and turbulence from the Alfven Wave Solar Model (AWSoM). To do this, we solve the focused transport equation to model the propagation of SEPs along magnetic field lines, extending these lines to 5 AU to incorporate the potential effects of pitch angle scattering beyond 1 AU. This aspect could notably impact the decay phase of a SEP event. Modeling the transport of SEPs is coupled with the simulation of solar wind dynamics, the interplanetary magnetic field, and the characteristics of Alfven wave turbulence. This presentation covers the modeling approach used in this research, the characterization of the impact of the stochastic acceleration, and the role of pitch angle scattering at various heliocentric distances on the dynamics of the SEP events' decay phase.

SEP↗

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation. Volume 1: Study results

A variety of artificial intelligence techniques which could be used with regard to NASA space applications and robotics were evaluated. The techniques studied were decision tree manipulators, problem solvers, rule based systems, logic programming languages, representation language languages, and expert systems. The overall structure of a robotic simulation tool was defined and a framework for that tool developed. Nonlinear and linearized dynamics equations were formulated for n link manipulator configurations. A framework for the robotic simulation was established which uses validated manipulator component models connected according to a user defined configuration.

Lowrie, J. W.↗

Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

Computational Engineering, Finance, and Science (c↗

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗

Distribution System Blackstart and Restoration Using DERs and Dynamically Formed Microgrids

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based gridforming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottomup blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. In conclusion, the proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

24 POWER TRANSMISSION AND DISTRIBUTION↗