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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2024

Representing farmer irrigated crop area adaptation in a large-scale hydrological model

Abstract. Large-scale hydrological models (LHMs) are commonly used for regional and global assessment of future water shortage outcomes under climate and socioeconomic scenarios. The irrigation of croplands, which accounts for the lion's share of human water consumption, is critical in understanding these water shortage trajectories. Despite irrigation's defining role, LHM frameworks typically impose trajectories of land use that underlie irrigation demand, neglecting potential dynamic feedbacks in the form of human instigation of and subsequent adaptation to water shortages via irrigated crop area changes. We extend an LHM, MOSART-WM, with adaptive farmer agents, applying the model to the continental United States to explore water shortage outcomes that emerge from the interplay between hydrologic-driven surface water availability, reservoir management, and farmer irrigated crop area adaptation. The extended modeling framework is used to conduct a hypothetical computational experiment comparing differences between a model run with and without the incorporation of adaptive farmer agents. These comparative simulations reveal that accounting for farmer adaptation via irrigated crop area changes substantially alters modeled water shortage outcomes, with US-wide annual water shortages being reduced by as much as 42 % when comparing adaptive and non-adaptive versions of the model forced with US climatology from the period 1950–2009.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Emergent Sp(3,$\mathbb{R}$) Dynamical Symmetry in the Nuclear Many-Body System from an Ab Initio Description

Ab initio nuclear theory provides not only a microscopic framework for quantitative description of the nuclear many-body system, but also a foundation for deeper understanding of emergent collective correlations. In this work, a symplectic Sp(3, $\mathbb{R}$) $\supset$ U(3) dynamical symmetry is identified in ab initio predictions, from a no-core configuration interaction approach, and found to provide a qualitative understanding of the spectrum of 7 Be. Low-lying states form an Elliott SU(3) spectrum, while an Sp(3, $\mathbb{R}$) excitation gives rise to an excited rotational band with strong quadrupole connections to the ground state band.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Cooperative On-Ramp Merging with Time-Varying Vehicle-to-Vehicle Communication Delay Compensation via a Model-Free Approach

Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays-an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A co-simulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low CO2 emissions and fuel consumption, under communication delay across different communication frequencies. In conclusion, its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.

Accounting↗
DOE OSTI2020

Real-Time Time-Dependent Nuclear–Electronic Orbital Approach: Dynamics beyond the Born–Oppenheimer Approximation

The quantum mechanical treatment of both electrons and nuclei is crucial in nona- diabatic dynamical processes such as proton-coupled electron transfer. The nuclear electronic orbital (NEO) method provides an elegant framework for including nuclear quantum effects beyond the Born-Oppenheimer approximation. To enable the study of nonequilibrium properties, we derive and implement a real-time NEO (RT-NEO) approach based on time-dependent Hatree-Fock or density functional theory, in which both the electronic and nuclear degrees of freedom are propagated in a time-dependent variational framework. Nuclear and electronic spectral features can be resolved from the time-dependent dipole moment computed using the RT-NEO method. The test cases demonstrate the dynamical interplay between the quantum nuclei and the electrons through vibronic coupling. Moreover, vibrational excitation in the RT-NEO approach is demonstrated by applying a resonant driving eld, and electronic excitation is demonstrated by simulating excited state intramolecular proton transfer. This work shows that the RT-NEO approach is a promising tool to study nonadiabatic quantum dynamical processes within a time-dependent variational description for the coupled electronic and nuclear degrees of freedom.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

An Agent-Based Modeling Approach for Spatiotemporal Optimization of Electric Vehicle Fast-Charging Station Demand

With increasing electric vehicle (EV) adoption, managing public fast-charging demand effectively is crucial to avoid grid strain. This study investigates the potential of using dynamic pricing schemes to address this challenge. Presented in this study is a scalable agent-based simulation framework, which is applied to a case study in Richmond, Virginia, that assumes a 50% EV adoption rate in 2040. Two pricing schemes are compared: (1) a dynamic-pricing scheme based on station utilization and (2) a dynamic-pricing scheme based on peak power at the station. These schemes are compared to two baseline scenarios: (1) unscheduled first-come, first-served and (2) scheduled with constant price. The study’s results suggest that dynamic pricing has the potential to influence EV charging behavior, inducing both spatial and temporal shifts, but does so at the cost of inducing inconvenience to EV drivers. The results suggest the peak-power dynamic pricing scheme has the potential to mitigate peak demand pressures on the grid with minimal inconvenience, offering a promising approach for sustainable EV charging infrastructure expansion.

33 - ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2026

Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission

Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA’s Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.

Geocorona↗
DOE OSTI2021

Ultrahigh thermal conductivity in three-dimensional covalent organic frameworks

Covalent organic frameworks (COFs) are a new, growing class of crystalline polymers with exceptional characteristics such as low density, mechanical strength, and flexibility. Taking three-dimensional (3D) COF-300 derivatives as an example, we apply molecular dynamics simulations to show that ultrahigh thermal conductivity (>15 W/(mK)) is achieved at a pore size of d = 0.63 nm. This is the first observation of such an outstanding intrinsic thermal conductivity of a 3D polymer in all directions. Further temperature-dependent thermal conductivity calculations and X-ray diffraction (XRD) patterns highlight the importance of maintaining microscopic order for a large thermal conductivity. We also demonstrate that the tunability and anisotropy in COFs provide sufficient room to engineer and obtain desired thermal conductivity. These findings provide a fundamental understanding of structure-thermal conductivity relation in COFs and shine a light on high-thermal-conductivity COFs for thermal management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗
DOE OSTI2025

ITreeForeCast: An integrated modeling software to simulate tree level growth and forest carbon storage

Healthy trees in forest act as a natural carbon sink, capturing carbon. As they grow, they store carbon in their trunks, leaves and roots. Not all trees store carbon at the same rate, or in the same quantities, as it depends on a variety of biophysical and climatic factors. Furthermore, although carbon estimation in trees can be complex, the precision of estimates is tightly linked to trees growth, both in diameter and height. However, the simulation of carbon uptake by forest and forest growth has each been modeled separately, and independently at differing levels of detail and spatial resolution. In this paper, we introduce ITreeForeCast, a simulation model combining the two types of modeling on a unified platform, enabling the investigation of impacts of management strategies on carbon sequestration and wood products. ITreeForeCast is a user-extendable framework that offers new opportunities to model, simulate, and visualize the dynamics of individual trees in a forest, simulate management strategies over time, and carbon uptake.

09 - BIOMASS FUELS↗
DOE OSTI2025

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (pdb-ihm.org), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology.

IHMCIF↗
DOE OSTI2026

Light-Driven Iodine Loss and Photoluminescence Homogenization in Mixed-Halide Perovskite Semiconductors

Carrier-induced instabilities in lead halide perovskites are often investigated as either transient phenomena, e.g., photoinduced halide segregation or permanent performance changes, e.g., photodegradation, while the mechanistic links between them remain unclear. Here, we aim to connect these observations by studying a model mixed-halide system, MAPb(Br x I 1–x ) 3 . By combining grazing-incidence X-ray diffraction, photothermal deflection spectroscopy, and photoluminescence measurements with hyperspectral microscopy, we investigate the role of mobile halide defects and local chemistry on reversible and long-term instabilities in these materials. Our results show that mixed-halide perovskites are uniquely susceptible to photoinduced changes with illumination driving initial iodide redistribution (i.e., halide segregation), eventual selective iodine expulsion, and subsequent changes in photoinduced halide segregation behavior. By quantifying structural and compositional changes, we estimate an approximately 3–5% iodine loss in our mixed-halide samples after only 24 h of illumination. Further, using microscale measurements, we identify pre-existing iodide-rich domains as key contributors to both the observed transient photostability and permanent iodine loss in MAPbBrI 2 , and see evidence that extended light soaking results in iodide redistribution that improves optoelectronic homogeneity. Overall, our results emphasize the importance of carrier-induced halide oxidation in creating a dynamic defect landscape in mixed-halide perovskites and provide a framework for interpreting apparent light-driven changes in optoelectronic behavior through the lens of permanent compositional changes.

electrical conductivity↗
DOE OSTI2020

Simulating the mechanisms of serrated flow in interstitial alloys with atomic resolution over diffusive timescales

Abstract The Portevin-Le Chatelier (PLC) effect is a phenomenon by which plastic slip in metallic materials becomes unstable, resulting in jerky flow and the onset of inhomogeneous deformation. The PLC effect is thought to be fundamentally caused by the dynamic interplay between dislocations and solute atoms. However, this interplay is almost always inaccessible experimentally due to the extremely fine length and time scales over which it occurs. In this paper, simulations of jerky flow in W-O interstitial solid solutions reveal three dynamic regimes emerging from the simulated strain rate-temperature space: one resembling standard solid solution strengthening, another one mimicking solute cloud formation, and a third one where dislocation/solute coevolution leads to jerky flow as a precursor of dynamic strain aging. The simulations are carried out in a stochastic framework that naturally captures rare events in a rigorous manner, providing atomistic resolution over diffusive time scales using no adjustable parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nonequilibrium charge-density-wave order beyond the thermal limit

The interaction of many-body systems with intense light pulses may lead to novel emergent phenomena far from equilibrium. Recent discoveries, such as the optical enhancement of the critical temperature in certain superconductors and the photo-stabilization of hidden phases, have turned this field into an important research frontier. Here, we demonstrate nonthermal charge-density-wave (CDW) order at electronic temperatures far greater than the thermodynamic transition temperature. Using time- and angle-resolved photoemission spectroscopy and time-resolved X-ray diffraction, we investigate the electronic and structural order parameters of an ultrafast photoinduced CDW-to-metal transition. Tracking the dynamical CDW recovery as a function of electronic temperature reveals a behaviour markedly different from equilibrium, which we attribute to the suppression of lattice fluctuations in the transient nonthermal phonon distribution. A complete description of the system’s coherent and incoherent order-parameter dynamics is given by a time-dependent Ginzburg-Landau framework, providing access to the transient potential energy surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

World beyond the nearest neighbors

The structure beyond the nearest neighbor atoms in liquid and glass is characterized by the medium-range order (MRO). In the conventional approach, the MRO is considered to result directly from the short-range order (SRO) in the nearest neighbors. To this bottom–up approach starting with the SRO, we propose to add a top–down approach in which global collective forces drive liquid to form density waves. The two approaches are in conflict with each other, and the compromise produces the structure with the MRO. The driving force to produce density waves provides the stability and stiffness to the MRO, and controls various mechanical properties. This dual framework provides a novel perspective for description of the structure and dynamics of liquid and glass.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Energy transfer in random-matrix ensembles of Floquet Hamiltonians

Here, we explore the statistical properties of energy transfer in ensembles of doubly driven random-matrix Floquet Hamiltonians based on universal symmetry arguments. The energy-pumping efficiency distribution P⁡($\overline{E}$) is associated with the Hamiltonian parameter ensemble and the eigenvalue statistics of the Floquet operator. For specific Hamiltonian ensembles, P⁡($\overline{E}$) undergoes a transition which cannot be associated with a symmetry breaking of the instantaneous Hamiltonian. The Floquet eigenvalue spacing distribution indicates the considered ensembles constitute generic nonintegrable Hamiltonian families. As a step towards Hamiltonian engineering, we develop a machine-learning classifier to understand the relative parameter importance in resulting high-conversion efficiency. We propose random Floquet Hamiltonians as a general framework to investigate frequency conversion effects in a class of generic dynamical processes beyond adiabatic pumps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗