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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mobile genetic elements shape microbial diversity and functions in thawing permafrost soils

Ecosystems are shaped by communities of microorganisms whose niches and impacts depend on functional profiles influenced by gene gains and losses. Culture-based experiments demonstrate that mobile genetic elements (MGEs) can mediate gene flux, but quantitative understanding of these dynamics in natural systems remains limited. Here we develop and apply a systematic, meta-omic framework to investigate MGEs in a complex natural system using an 8-year soil time series collected at Stordalen Mire, in Sweden’s thawing permafrost margin. In this climate-critical peatland, we identify ~2.1 million MGE recombinases across 89 microbial phyla and assess ecological distributions, affected functions, past mobility and current activity. This revealed an active mobilome that shapes natural genetic diversity via differential impacts on major phyla and affects a wide range of functions, including metabolic genes involved in carbon flux and nutrient cycling. These findings and this analytic framework suggest avenues towards a better understanding of MGE diversity, activity, mobility and impacts across ecosystems.

Biological and medical sciences↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Atomic lift-off of epitaxial membranes for cooling-free infrared detection

Recent breakthroughs in ultrathin, single-crystalline, freestanding complex oxide systems have sparked industry interest in their potential for next-generation commercial devices. However, the mass production of these ultrathin complex oxide membranes has been hindered by the challenging requirement of inserting an artificial release layer between the epilayers and substrates. Here we introduce a technique that achieves atomic precision lift-off of ultrathin membranes without artificial release layers to facilitate the high-throughput production of scalable, ultrathin, freestanding perovskite systems. Leveraging both theoretical insights and empirical evidence, we have identified the pivotal role of lead in weakening the interface. This insight has led to the creation of a universal exfoliation strategy that enables the production of diverse ultrathin perovskite membranes less than 10 nm. Our pyroelectric membranes demonstrate a record-high pyroelectric coefficient of 1.76 × 10 −2 C m −2 K −1 , attributed to their exceptionally low thickness and freestanding nature. Moreover, this method offers an approach to manufacturing cooling-free detectors that can cover the full far-infrared spectrum, marking a notable advancement in detector technology.

nanoscale materials↗
DOE OSTI2026

Complex Electrical Conductivity of a Single‐Fractured Rock: Fracture‐and‐Matrix Coupling Mechanism and Aperture Size Predictions

Fractured rocks play a crucial role in myriad natural and engineered systems, including Earth's critical zone, oil/gas/geothermal reservoirs, and geological CO 2 /H 2 /waste storage systems. While complex electrical conductivity is extensively used to estimate the pore and grain sizes of conventional porous rocks and soils, it is rarely used to predict the aperture size of fractured rocks and this remains poorly understood. Here, integrating theory, simulations, and experiments, we show that under external fields, fractured rocks follow the fracture-and-matrix coupling to make the bulk complex conductivity non-linear with respect to water conductivity. We find that the relaxation time and quadrature conductivity for porous media do not apply to fractured rocks, but, instead, reasonably accurate predictions of aperture size can be made based on the true formation factor. This study unravels the fundamental mechanism governing conduction and polarization of fractured rocks and paves the way for the non-invasive investigation of global fractured rocks.

58 GEOSCIENCES↗
DOE OSTI2026

Generating entangled steady states in multistable open quantum systems via initial state control

Entanglement underpins the power of quantum technologies, yet it is fragile and typically destroyed by dissipation. Paradoxically, the same dissipation, when carefully engineered, can drive a system toward robust entangled steady states. However, this engineering task is nontrivial, as dissipative many-body systems are complex, particularly when they support multiple steady states. Here, we derive analytic expressions that predict how the steady state of a system evolving under a Lindblad equation depends on the initial state, without requiring integration of the dynamics. These results extend Refs. [V. V. Albert and L. Jiang, Phys. Rev. A 89, 022118 (2014); V. V. Albert et al., Phys. Rev. X 6, 041031 (2016)], showing that while the steady-state manifold is determined by the Liouvillian kernel, the weights within it depend on both the Liouvillian and the initial state. We identify a special class of Liouvillians for which the steady state depends only on the initial overlap with the kernel. Our framework provides analytical insight and a computationally efficient tool for predicting steady states in open quantum systems. As an application, we propose schemes to generate metrologically useful entangled steady states in spin ensembles via balanced collective decay.

Dissipative dynamics↗
DOE OSTI2026

Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism

Microbial metabolism is a complex, emergent system driven by the coordinated interplay of intricate and dynamic molecular processes. To elucidate cellular behavior and enable biotechnological applications, quantitative models that address the inherent complexity of metabolism have been developed from a resource allocation perspective. Here, we synthesize recent advances in coarse-grained resource allocation frameworks and their applications in understanding microbial physiology and guiding gene circuit design. Here, these frameworks reveal global regulatory constraints and predict cellular adaptation to nutrient and environmental changes. In addition, they enable the quantification of metabolic costs, the dissection of circuit–host interactions, and the development of strategies for burden mitigation. Collectively, these modeling frameworks provide a powerful platform for uncovering quantitative principles of microbial growth and engineering robust synthetic biological systems.

coarse-grained modeling↗
DOE OSTI2024

A Distributionally Robust Optimization Framework for Stochastic Assessment of Power System Flexibility in Economic Dispatch

Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximum range of net load uncertainty across all buses. In this paper, we consider the stochastic nature of the net load and introduce a distributionally robust optimization framework that assesses system flexibility stochastically, accommodating a minimal extent of system violations. We verify the proposed method by solving the flexibility of the economic dispatch problem on four distinct IEEE standard test systems. Compared to traditional deterministic flexibility evaluations, our approach consistently yields less conservative flexibility outcomes.

distributionally robust optimization↗
DOE OSTI2024

Macroscopic modeling of gas permeability in hierarchical micro/nanoporous media: A unified characterization of rarefaction using Klinkenberg theory and equivalent diameter

Estimating gas transport through a hierarchical micro/nanoporous system is challenging due to non-equilibrium gas dynamics. The primary difficulty lies in determining the rarefaction level, because identifying a representative flow dimension in a complex porous system with multiple pore scales is not straightforward. Our study performed a pore-level analysis for gas permeability in dual-scale porous media with varying porosity, throat size, and secondary pore size under different rarefaction conditions. We found that secondary porosity negatively affects permeability due to increased friction forces, with this influence growing as the secondary pore size and porosity increase until the secondary pore becomes comparable to the throat. However, rarefaction reduces the effects of secondary pores due to boundary slip. Traditional Knudsen number (Kn) calculations based on Darcy-defined height failed to accurately describe the rarefaction effects on gas permeability. Instead, we introduced an equivalent diameter to calculate the Kn, which provided an accurate normalization of apparent gas permeability independent of pore geometry. Furthermore, the extended Kozeny–Carman–Klinkenberg model developed in our previous study successfully yielded a macroscopic model for apparent gas permeability in hierarchical micro/nanoporous systems as a function of the traditional Darcy height and porosity.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Generalized Quasi-Static Mooring System Modeling with Analytic Jacobians

This paper presents a generalized and efficient method for quasi-static analysis of mooring systems, including complex scenarios such as when shared mooring lines interconnect multiple floating wind or wave energy devices. While quasi-static mooring models are well established, most published formulations are focused on specific applications, and no publicly available implementations provide efficient handling of large mooring system networks. The present formulation addresses these gaps by: (1) formulating solutions for edge cases not typically supported by quasi-static models; (2) creating a fully generalized model structure such that any combination of mooring lines, point masses, and floating bodies can be assembled; and (3) deriving analytic expressions for the system Jacobians (stiffness matrices) so that systems with many degrees of freedom can be solved efficiently. These techniques form the theory basis of MoorPy, an open-source mooring analysis library. The model is demonstrated on nine scenarios of increasing complexity with features of interest for offshore renewable energy applications. When compared with steady-state results from a lumped-mass dynamic model, the results show that the quasi-static formulation accurately calculates profiles and tensions and that its analytic approach provides more efficient and reliable computation of system stiffness matrices than finite-differencing methods. These results verify the accuracy of the MoorPy model.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Classifying topology in photonic crystal slabs with radiative environments

Abstract In the recent years, photonic Chern materials have attracted substantial interest as they feature topological edge states that are robust against disorder, promising to realize defect-agnostic integrated photonic crystal slab devices. However, the out-of-plane radiative losses in those photonic Chern slabs has been previously neglected, yielding limited accuracy for predictions of these systems’ topological protection. Here, we develop a general framework for measuring the topological protection in photonic systems, such as in photonic crystal slabs, while accounting for in-plane and out-of-plane radiative losses. Our approach relies on the spectral localizer that combines the position and Hamiltonian matrices of the system to draw a real-picture of the system’s topology. This operator-based approach to topology allows us to use an effective Hamiltonian directly derived from the full-wave Maxwell equations after discretization via finite-elements method (FEM), resulting in the full account of all the system’s physical processes. As the spectral FEM-localizer is constructed solely from FEM discretization of the system’s master equation, the proposed framework is applicable to any physical system and is compatible with commonly used FEM software. Moving forward, we anticipate the generality of the method to aid in the topological classification of a broad range of complex physical systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) Integration into Model- Based Systems Engineering (MBSE)

Engineering design in the field of industrial engineering, such as designing automated factories or warehouses, is critical for the effective operation of facilities. Any design flaws introduced early can result in significant capital expenses to correct. However, early-stage engineering design is inherently complex. The systems are not yet built, requiring designers to integrate various aspects, including digital engineering and cybersecurity, to support virtual representations throughout the design process. In this study, we propose an approach to integrate Cyber-Informed Engineering (CIE) principles into model-based systems engineering (MBSE). This approach facilitates the development of a digital thread for engineering systems, ensuring secure digital artifacts in the design of industrial engineering systems.

42 - ENGINEERING↗
DOE OSTI2025

High-Fidelity Building Emulator for Integrated Comfort and Energy Analysis using EnergyPlus and Radiance

The growing need for smart, energy-efficient, and occupant-centric buildings has created a demand for advanced control systems that can optimize building operations to balance energy savings, demand flexibility, and comfort. However, current building energy simulation tools, such as EnergyPlus, have limitations that hinder the development and evaluation of these complex control systems. To address this challenge, we introduce a high-fidelity building emulator that dynamically couples EnergyPlus with Radiance for enhanced daylight modeling. The introduced workflow allows researchers and practitioners to rapidly develop and evaluate innovative control solutions. An example study looking at a south-facing office zone revealed up to 67% deviation in predicted light levels, which can significantly impact building assessment.

Yu, Tammie↗
DOE OSTI2025

Exploring Water System Vulnerabilities in California's Central Valley Under the Late Renaissance Megadrought and Climate Change

Abstract California faces cycles of drought and flooding that are projected to intensify, but these extremes may impact water users across the state differently due to the region's natural hydroclimate variability and complex institutional framework governing water deliveries. To assess these risks, this study introduces a novel exploratory modeling framework informed by paleo and climate‐change based scenarios to better understand how impacts propagate through the Central Valley's complex water system. A stochastic weather generator, conditioned on tree‐ring data, produces a large ensemble of daily weather sequences conditioned on drought and flood conditions under the Late Renaissance Megadrought period (1550–1580 CE). Regional climate changes are applied to this weather data and drive hydrologic projections for the Sacramento, San Joaquin, and Tulare Basins. The resulting streamflow ensembles are used in an exploratory stress test using the California Food‐Energy‐Water System model, a highly resolved, daily model of water storage and conveyance throughout California's Central Valley. Results show that megadrought conditions lead to unprecedented reductions in inflows and storage at major California reservoirs. Both junior and senior water rights holders experience multi‐year periods of curtailed water deliveries and complete drawdowns of groundwater assets. When megadrought dynamics are combined with climate change, risks for unprecedented depletion of reservoir storage and sustained curtailment of water deliveries across multiple years increase. Asymmetries in risk emerge depending on water source, rights, and access to groundwater banks.

Gupta, Rohini S. [School of Civil and Environmenta↗
DOE OSTI2024

Generalized quantum master equations can improve the accuracy of semiclassical predictions of multitime correlation functions

Multitime quantum correlation functions are central objects in physical science, offering a direct link between the experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometimes impossible, depending on the system’s complexity. A natural tool to employ is the generalized quantum master equation (GQME), which can offer computational savings by extending reference dynamics at a comparatively trivial cost. However, dynamical methods that can tackle chemical systems with atomistic resolution, such as those in the semiclassical hierarchy, often suffer from poor accuracy, limiting the credence one might lend to their results. By combining work on the accuracy-boosting formulation of semiclassical memory kernels with recent work on the multitime GQME, here we show for the first time that one can exploit a multitime semiclassical GQME to dramatically improve both the accuracy of coarse mean-field Ehrenfest dynamics and obtain orders of magnitude efficiency gains.

Chemistry↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling Distributed Computing Infrastructures for HEP Applications

Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.

Horzela, Maximilian↗