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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗
DOE OSTI2024

Adaptive tau-leaping methods for microscopic-lattice kinetic Monte Carlo simulations

Traditional Kinetic Monte Carlo (KMC) approaches, rooted in Gillespie’s stochastic simulation algorithm, become computationally demanding in systems with a large range of timescales. The goal of this work is to propose and study new adaptive lattice-KMC time integration strategies for spatially non-uniform systems. To that end, two novel adaptive tau-leaping methods and their corresponding time integration strategies are developed based on the idea of the “n-fold” direct KMC method. These strategies allow for the simultaneous execution of multiple reactions, advancing time by adaptively selected coarse increments. We present numerical experiments comparing the proposed methods with existing approaches in a catalytic surface kinetics application involving ammonia decomposition.

Bimolecular reactions↗
DOE OSTI2026

Near-Optimal Performance of Stochastic Model Predictive Control

Here, this article presents a regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative uncertainties. Under a finite support assumption, the problem can be cast as a finite-dimensional quadratic program, but the problem becomes quickly intractable as the problem size grows exponentially in the horizon length. SMPC aims to compute approximate solutions by solving a sequence of problems with truncated prediction horizons and committing the solution in a receding-horizon fashion. Although this approach is widely used in practice, its performance relative to the optimal solution is not well understood. This article reports for the first time a rigorous near-optimal performance guarantee of SMPC: under stabilizability and detectability conditions, the regret of SMPC is exponentially small in the prediction horizon length, allowing SMPC to achieve near-optimal performance at a substantially reduced computational expense.

93E20, 93B45↗
DOE OSTI2025

The tensor-train stochastic finite volume method for uncertainty quantification

The stochastic finite volume method offers an efficient one-pass approach for assessing uncertainty in hyperbolic conservation laws. Still, it struggles with the curse of dimensionality when dealing with multiple stochastic variables. Here, we introduce the stochastic finite volume method within the tensor-train framework to counteract this limitation. This integration, however, comes with its own set of difficulties, mainly due to the propensity for shock formation in hyperbolic systems. To overcome these issues, we have developed a tensor-train-adapted stochastic finite volume method that employs a global WENO reconstruction, making it suitable for such complex systems. This approach represents the first step in designing tensor-train techniques for hyperbolic systems and conservation laws involving shocks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

From Coherence to Function: Exploring the Connection in Chemical Systems

The role of quantum mechanical coherences or coherent superposition states in excited state processes has received considerable attention in the last two decades largely due to advancements in ultrafast laser spectroscopy. These coherence effects hold promise for enhancing the efficiency and robustness of functionally relevant processes, even when confronted with energy disorder and environmental fluctuations. Understanding coherence deeply drives us to unravel mechanisms and dynamics controlled by order and synchronization at a quantum mechanical level, envisioning optical control of coherence to enhance functions or create new ones in molecular and material systems. In this frontier, the interplay between electronic and vibrational dynamics, specifically the influence of vibrations in directing electronic dynamics, has emerged as the leading principle. Here, two energetically disparate quantum degrees of freedom work in-sync to dictate the trajectory of an excited state reaction. Moreover, with the vibrational degree being directly related to the structural composition of molecular or material systems, new molecular designs could be inspired by tailoring certain structural elements. In the realm of chemical kinetics, our understanding of the dynamics of chemical transformations is underpinned by fundamental theories, such as transition state theory, activated rate theory, and Marcus theory. These theories elucidate reaction rates by considering the energy barriers that must be overcome for reactants to transform into products. Those barriers are surmounted by the stochastic nature of energy gap fluctuations within reacting systems, emphasizing that the reaction coordinate, the pathway from reactants to products, is not rigidly defined by a specific vibrational motion but encompasses a diverse array of molecular motions. While less is known about the involvement of specific intramolecular vibrational modes, their significance in certain cases cannot be overlooked. In this Account, we summarize key experimental findings that offer deeper insights into the complex electronic–vibrational trajectories encompassing excited states afforded from state-of-the-art ultrafast laser spectroscopy in three exemplary processes: photoinduced electron transfer, singlet–triplet intersystem crossing, and intramolecular vibrational energy flow in molecular systems. We delve into the rapid decoherence, or loss of phase and amplitude correlations, of vibrational coherences along promoter vibrations during subpicosecond intersystem crossing dynamics in a series of binuclear platinum complexes. This rapid decoherence illustrates the vibration-driven reactive pathways from the Franck–Condon state to the curve crossing region. We also explore the generation of new vibrational coherences induced by impulsive reaction dynamics rather than by the laser pulse in these systems, which sheds light on specific energy dissipation pathways and thereby on the progression of the reaction trajectory in the vicinity of the curve crossing on the product side. Another property of vibrational coherences, amplitude, reveals how energy can flow from one vibration to another in the electronic excited state of a terpyridine–molybdenum complex hosting a nonreactive dinitrogen substrate. In conclusion, a slight change in vibrational energy triggers a quasi-resonant interaction, leading to constructive wavepacket interference and ultimately intramolecular vibrational redistribution from a Franck–Condon active terpyridine vibration to a dinitrogen stretching vibration, energizing the dinitrogen bond.

Electrical energy↗
DOE OSTI2024

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

computation↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2024

Denoising of imaginary time response functions with Hankel projections

Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost—in quantum Monte Carlo simulations, the reduction of noise by a factor of two incurs a simulation cost of a factor of four. In this paper, we relate certain imaginary-time response functions to an inner product on the space of linear operators on Fock space. We then show that data with noise typically does not respect the positive definiteness of its associated Gramian. The Gramian has the structure of a Hankel matrix. As a method for denoising noisy data, we introduce an alternating projection algorithm that finds the closest positive definite Hankel matrix consistent with noisy data. We test our methodology at the example of fermion Green's functions for continuous-time quantum Monte Carlo data and show remarkable improvements of the error, reducing noise by a factor of up to 20 in practical examples. We argue that Hankel projections should be used whenever finite-temperature imaginary-time data of response functions with errors is analyzed, be it in the context of quantum Monte Carlo, quantum computing, or in approximate semianalytic methodologies. Published by the American Physical Society 2024

Yu, Yang (ORCID:0000000186178878)↗
DOE OSTI2026

Coexistence of Synchronization and Stochasticity in Thermally Coupled Mott Oscillators

Synchronization is conventionally regarded as a mechanism for suppressing variability and enforcing order in coupled systems, from pendula and lasers to neurons and electronic oscillators. Here, we show that synchronization can also embed stochasticity at finer scales. We observe this phenomenon in thermally coupled VO 2 neuristors, where robust in-phase synchronization at the microsecond scale coexists with spike onset fluctuations at the nanosecond scale, with no fixed leader. The coexistence of order and disorder originates from stochastic domain-level physics of the insulator–metal and metal–insulator transitions, where local variations in transition temperature drive cycle-to-cycle randomness in nucleation, percolation, and relaxation. A stochastic domain model reproduces this effect by generating synchronized spike trains with random lead–lag jitter, and experimental interspike interval statistics confirm the persistence of fine-scale variability despite macroscopic phase locking. These findings establish that synchronization and stochasticity can coexist within the same physical platform, revealing hidden disorder within collective order. Furthermore, this insight reframes synchronization as not purely deterministic, but as a universal context where microscopic variability can persist, with implications for electronics, cryptography, and the fundamental physics of order–disorder coexistence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗
DOE OSTI2024

Stochastic Maxwell-Bloch equations for modeling amplified spontaneous emission

An approach for studying atom-radiation interaction has been developed, associating quantum operators with stochastic variables governed by discrete Heisenberg equations. This framework models general multilevel atomic systems using the paraxial approximation in both single- and multipass configurations. Simulation results for x-ray laser oscillators with parameters of interest are presented. Finally, the stochastic model is efficient to solve, as the required computational resources scale linearly with the number of emitters, and it may be applicable to various other quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Marine energy supported multi-energy system planning and operation optimization for sustainable coastal community

The growing need for sustainable energy solutions in coastal areas necessitates the development of integrated systems that leverage abundant marine resources. In this study, a standalone Marine Energy Supported Multi-Energy System (MRE-MES) is designed for sustainable coastal community development, utilizing renewable marine resources, including offshore wind, wave, and solar energy, to address the energy needs of electricity, heat, freshwater, and hydrogen. The proposed MRE-MES incorporates a co-optimization model that simultaneously balances capacity planning and operational efficiency to minimize costs and environmental impacts. The system is tested under different renewable energy penetration levels and demand uncertainties, using a two-stage stochastic programming to account for variability in renewable resources and consumption needs. The experimental results indicate that in the optimal system capacity configuration, the percentage of total renewable energy generation is around 80 %, with or without capacity limitation constraints on PV, water tank, and hydrogen storage. Compared to the worst-case scenario in Monte Carlo experiments, two-stage stochastic optimization results in a more robust decision that effectively mitigates the risks posed by future uncertain demand conditions. In conclusion, the findings highlight the viability of marine energy for providing a resilient, comprehensive energy solution to coastal communities.

Capacity planning↗
DOE OSTI2025

Two- and three-meson scattering amplitudes with physical quark masses from lattice QCD

We study systems of two and three mesons composed of pions and kaons at maximal isospin using four CLS ensembles with 𝑎 ≈ 0.063 fm, including one with approximately physical quark masses. Using the stochastic Laplacian-Heaviside method, we determine the energy spectrum of these systems including many levels in different momentum frames and irreducible representations. Using the relativistic two- and three-body finite-volume formalism, we constrain the two- and three-meson K matrices, including not only the leading 𝑠 wave, but also 𝑝 and 𝑑 waves. By solving the three-body integral equations, we determine, for the first time, the physical-point scattering amplitudes for 3⁢𝜋 + , 3⁢𝐾 + , 𝜋 + ⁢𝜋 + ⁢𝐾 + , and 𝐾 + ⁢𝐾 + ⁢𝜋 + systems. These are determined for total angular momentum 𝐽 𝑃 = 0 − , 1 + , and 2 − . We also obtain accurate results for 2⁢𝜋 + , 𝜋 + ⁢𝐾 + , and 2⁢𝐾 + phase shifts. We compare our results to chiral perturbation theory and to phenomenological fits.

FOS: Physical sciences↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗
DOE OSTI2026

Optical stochastic cooling at Fermilab’s IOTA ring

Stochastic cooling (SC) constitutes one of the greatest conceptual and technological achievements in particle beam cooling. Numerous SC systems have been built in the microwave regime and used to extend the science reach of accelerator facilities worldwide. The ability to sense and correct the particle ensemble using high-bandwidth feedback systems is at the core of the concept. Here, we describe the first realization of SC at optical frequencies and bandwidths. The demonstration was recently carried out at Fermilab’s integrable optics test accelerator (IOTA) storage ring using the transit-time method of optical stochastic cooling (OSC) and achieved an integrated system bandwidth of approximately 20 THz, which is more than 2000 times that of conventional SC systems. This demonstration establishes the foundation for more advanced OSC experiments with high-gain amplification, currently underway at Fermilab, and the eventual application of OSC to colliders and other accelerator facilities.

Jarvis, Jonathan D. [Fermilab]↗
DOE OSTI2024

Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗