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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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NASA NTRS2026

Preliminary Dynamic Modeling of the Quarter-Scale Distributed Electric Propulsion Aircraft

This paper describes the early-stage modeling of a quarter-scale distributed electric propulsion aircraft, based on the SUbsonic Single Aft eNgine (SUSAN) Electrofan, a transformative concept aircraft for which a model exists. The full-scale 180 passenger SUSAN concept has a single turbofan engine in the tail that both produces thrust and provides electrical power to 16 electric fans distributed across the wings utilizing a series/parallel hybrid architecture. The quarter-scale version would have an internal combustion piston engine to provide power to 17 electric fans–16 on the wings, one in the tail–in a series hybrid configuration, and no vertical or horizontal stabilizers. It would also have a reduced flight envelope in terms of both altitude and speed. The initial modeling approach is to scale down the original airframe model to capture the dynamic behavior of a much smaller aircraft, albeit with an empennage. The original powertrain model is then replaced with one representing the physical components of that of the quarter-scale vehicle. This powertrain model is suitable for control design and analysis, and the fully integrated, although preliminary, aircraft model allows flight simulator testing and evaluation. Results from simulations are presented.

Electrified powertrain↗
DOE OSTI2026

Tracing U.S. fuel life-cycle greenhouse gas emissions in a multi-sector dynamics model using LC-GCAM

Model-based analysis of fuel pathways is essential for informing energy and environmental policy. Two major model types are typically used: multi-sector dynamics models, which capture the broader energy-economy, such as GCAM (Global Change Analysis Model), and life cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies increasingly adopt hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compare its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 10% (absolute sum-based mean absolute percentage error). LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

Wolfram, Paul↗
DOE OSTI2025

Echo state network for coarsening dynamics of charge density waves

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

2-dimensional systems↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2026

Modeling the Potential Impact of Storage on the US Power Sector in a Multisector Dynamic Context

The electric power sector is expected to grow in size, importance, and complexity around the world as economies expand and electric supply technologies and demand patterns evolve in significant ways. At the same time the electric power sector may see substantial increases in VRE generating technologies which can add variability and uncertainty to the diurnal and seasonal profile of electric supply. With these forces in play, the emergence of modular, flexible electricity storage technologies may have profound impacts on the operation of electric power systems. Here we reduce a storage modeling gap in MSD models by incorporating grid-based electricity storage into the electric sector dynamics of the Global Change Analysis Model-USA (GCAM-USA) with improved power sector representation. We find a potentially significant role for storage technologies in the future of the U.S. power system, with storage capacity ranging from 7.7 to 14.7 GW in 2050 and 13.9 to 31.6 GW in 2100 across several techno-economic scenarios. We also find that storage can help to smooth variability in residual load arising from evolving electricity demands and the introduction of high shares of VRE to the electric grid. This reduces reliance on high-cost peaking generators, improves system-wide capacity factors, and limits curtailment of VRE.

Patel, Pralit L.↗
DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗
DOE OSTI2023

Correlating Impacts of Injected Fuels on Carbon Emissions in Blast Furnace with Computational Fluid Dynamics Modeling

A major challenge for steelmaking is the reduction of CO 2 emissions. In this regard, the blast furnace (BF) is critical due to the high associated CO 2 levels. This investigation assesses the impact of tuyere‐injected fuels on BF CO 2 emissions. Specifically, computational fluid dynamics results obtained previously at Purdue University Northwest are analyzed to obtain CO 2 emissions when natural gas (NG), syngas, hydrogen, or hydrogen/NG are injected. CO 2 emissions are compared with those produced when 95 kg of NG/thm is injected. Among these scenarios, the largest CO 2 reduction occurs when 102 kg of syngas/thm (COG feedstock #1) is injected at 973 K, reducing CO 2 by 190.6 kg thm −1 . The largest CO 2 reduction obtained with NG occurs when 130 kg thm −1 is injected at 600 K, reducing emissions by 65 kg thm −1 . H 2 injection also reduces CO 2 , but requires careful adjusting to reach stable operation. For instance, injecting 35 kg of H 2 /thm reduces CO 2 by 52 kg thm −1 . Increasing gaseous injection rates can significantly reduce CO 2 emissions, with fuel preheating providing an addendum, but high injection rates can lead to unstable operation. Furthermore, results show a correlation between CO 2 emissions and average temperature of shaft region for multiple fuels and injection conditions.

Metallurgy & Metallurgical Engineering↗
NASA NTRS2026

The Contaminant Footprint of Landed Spacecraft: Toward an Inventory and Modelling Framework

All spacecraft generate and carry contaminants, i.e., unwanted and potentially harmful material. When a spacecraft lands and operates in vacuum, as onto Earth’s Moon, it introduces contaminants into its environment that may compromise mission science objectives and engineering performance. Contamination may degrade sites of unique value to planetary science or in situ resource utilization. This presentation will identify and compare source terms and transport vectors for contaminants – in particular, organic material – generated by landed spacecraft. An integrated modeling framework for the organic contamination footprint of spacecraft missions will be described and presented.

Gas Dynamics↗
DOE OSTI2026

The impact of metastability on the high-pressure behavior of cerium

The structures adopted by solids under pressure are often assumed to reflect thermodynamic equilibrium, yet in many materials phase selection is strongly influenced by kinetic pathways and microstructural inheritance. Elemental cerium (Ce) exemplifies this challenge, with decades of conflicting reports describing different high-pressure crystal structures emerging under nominally identical conditions. Here we use neutron diffraction from large ( ~ 60 mm 3 ) sample volumes to follow the structural evolution in ultra-high-purity Ce during controlled pressure-temperature cycling between 85 and 295 K and up to 8 GPa. We find that the crystal structure formed at high pressure depends on the compression pathway: slow compression ( ~ 0.25 GPa hr −1 ) at room temperature favors an orthorhombic phase (α'), whereas slow ( ~ 0.25 GPa hr −1 ) and also moderately faster ( ~ 0.5 GPa hr −1 ) compression at low temperature stabilizes a pure monoclinic phase (α"). The low-temperature phase persists metastably over a wide temperature range but transforms irreversibly upon heating above ~ 280 K, or modest pressure cycling. Remarkably, the lower-pressure γ phase remains trapped far beyond the expected stability field, persisting to the highest pressures studied. These observations show that phase selection in Ce is governed by kinetics and microstructural memory rather than equilibrium thermodynamics or sample morphology alone, establishing path dependence as a defining feature of its high-pressure behavior.

Ridley, Christopher J. [Oak Ridge National Laborat↗
NASA NTRS1993

A SIMULINK Environment for Flight Dynamics and Control Analysis - Application to the DHC-2 'Beaver', Part 1. Implementation of a Model Library in SIMULINK

The design of advanced Automatic Aircraft Control Systems (AACS's) can be improved upon considerably if the designer can access all models and tools required for control system design and analysis through a graphical user-interface, from within one software environment. This MSc-thesis presents the first step in the development of such an environment, which is currently being done at the Section for Stability and Control of Delft University of Technology, Faculty of Aerospace Engineering. The environment is implemented within the commercially available software package MATLAB/SIMULINK. The report consists of two parts. Part I gives a detailed description of the AACS design environment. The heart of this environment is formed by the SIMULINK implementation of a nonlinear aircraft model in block-diagram format. The model has been worked out for the old laboratory aircraft of the Faculty, the De Havilland DHC-2 'Beaver', but due to its modular structure, it can easily be adapted for other aircraft. Part I also describes MATLAB programs which can be applied for finding steady-state trimmed-flight conditions and for linearization of the aircraft model, and it shows how the built-in simulation routines of SIMULINK have been used for open-loop analysis of the aircraft dynamics. Apart from the implementation of the models and tools, a thorough treatment of the theoretical backgrounds is presented. Part II of this report presents a part of an autopilot design process for the 'Beaver' aircraft, which clearly demonstrates the power and flexibility of the AACS design environment from part I. Evaluations of all longitudinal and lateral control laws by means of nonlinear simulations are treated in detail. The AACS design environment from part I proved to be a very useful tool for designing the control laws of the 'Beaver' autopilot within a very tight time-schedule. The autopilot design process itself will be used as a guideline for future AACS research at the Faculty of Aerospace Engineering. Flight tests of the 'Beaver' autopilot, done after evaluating the control laws in the SIMULINK package, proved to be quite successful. In the future, the AACS design package will evolve into a standardized, integrated design environment which can be applied to virtually any type of aircraft. The AACS design cycle will be shortened further by developing tools for automatically porting control laws from the MATLAB/SIMULINK environment to a piloted real-time flight simulator and the Flight Control Computers of the aircraft.

Aircraft Modelling↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2026

Assessment of Near-Angle Scatter on Exo-Earth Coronagraphy

Near-Angle Scatter (NAS) of the host star’s light into the coronagraph dark hole may limit the ability of a potential Habitable Worlds Observatory (HWO) to detect and characterize an Earth-like planet around a Sun-like star. This paper summarizes a 5-year investigation of the impact of NAS on high-contrast coronagraphy. Science requirements were flowed down to a draft flux ratio noise ratio (FRN) error budget with allocations for NAS. The modeled sources of NAS include correlated (low and mid-spatial) surface structure, uncorrelated surface microroughness and coating structure, particulate contamination, micrometeoroid impacts and polarization leakage. The paper develops specifications for these sources of scattered light that meet their FRN error budget allocations, distinguishing between scatter that can and cannot be modulated. The development process utilizes an analytical expression that predicts scatter throughput into the dark hole based on BRDF/BSDF. Using this relationship, Photon Engineering performed a FRED® straylight analysis of the HWO EAC-1 (Exploratory Analytical Case #1) coronagraph to parametrically study predicted scattered light contrast in the dark hole. The model revealed that primary mirror microroughness or contamination is not a significant source of scattered light; the most critical surfaces are the smallest and those located closest to the coronagraph focal plane mask. The assessment found that uncorrelated microstructure, contamination, and micrometeoroid impacts require more investigation and potential significant sources of unmodulated scattered light, and highlights that slow thermal wavefront drift is an order of magnitude more impactful than fast mechanical jitter.

coronagraphy↗
DOE OSTI2026

Nonlocal Effect of Percolated Particle Networks on Viscoelasticity of Polymer–Filler Nanocomposites: A Mesoscale Simulation Study

With nanoparticles (NPs) as fillers, polymer nanocomposites (PNCs) usually exhibit enhanced mechanical properties. However, a direct connection between the microscopic structural relaxation and macroscopic mechanical properties of PNCs remains to be established. To investigate the micro-to-macro connection, we develop a mesoscale model, in which the NPbridging polymer chains are represented by a dynamic bonded interaction between NPs, and the bulk polymer matrix is implicitly modeled by overdamped Langevin dynamics. Extensive equilibrium simulations are performed to quantify the microscopic dynamics of model PNCs. Systematic analyses of modified Rouse dynamics, dynamic structure factor, and relaxation modulus uncover that the microscopic relaxation dynamics of PNCs are significantly decelerated across different length scales because of nonlocal effects of percolated particle networks a phenomenon that has not been adequately captured in prior simulation studies. We find that NPvolume- fraction and NP-bonding-energy barrier are the two critical variables that affect bulk viscoelasticity the most. The proposed mesoscale model is versatile and provides a powerful framework for studying structure−property relations of different PNCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

Model Spread in Tropical Low Cloud Feedback Tied to Overturning Circulation Response to Warming

Among models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), here we show that the magnitude of the tropical low cloud feedback, which contributes considerably to uncertainty in estimates of climate sensitivity, is intimately linked to tropical deep convection and its effects on the tropical atmospheric overturning circulation. First, a reduction in tropical ascent area and an increased frequency of heavy precipitation result in high cloud reduction and upper-tropospheric drying, which increases longwave cooling and reduces subsidence weakening, favoring low cloud reduction (Radiation-Subsidence Pathway). Second, increased longwave cooling decreases tropospheric stability, which also reduces subsidence weakening and low cloudiness (Stability-Subsidence Pathway). In summary, greater high cloud reduction and upper-tropospheric drying (negative longwave feedback) lead to a more positive cloud feedback among CMIP6 models by contributing to a greater reduction in low cloudiness (positive shortwave feedback). Varying strengths of the two pathways contribute considerably to the intermodel spread in climate sensitivity.

Atmospheric dynamics↗
DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗
DOE OSTI2026

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗
DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗
NASA NTRS2022

A Transient Hydrodynamic Model of Screen Channel Liquid Acquisition Devices for In-Space Cryogenic Propellant Management

Screen channel liquid acquisition devices (LADs) will play a crucial role in future deep space travel. It is essential that vapor-free delivery of propellants during tank-to-tank transfer is ensured to maximize yield from storage tanks and prevent potential combustion instabilities. The screen channel LAD utilizes a fine screen wire mesh that can separate phases in a low Bond number (i.e. microgravity) environment using surface tension forces. This study presents the development and verification of a new model for transient screen compliance, one of the influential factors for screen channel LAD design. Screen compliance is crucial during LAD channel outflow transients because the slight deflection of the screen can provide needed mass to satisfy rapid outflow demands and reduce the pressure difference across the screen. The model is successfully verified against CFD simulations. In addition, the characteristic speed for the governing screen compliance equations is derived which allows for numerical stability criteria to be established. As shown in this study, the transient maximum pressure difference across the screen can greatly exceed the steady state maximum pressure difference across the screen in many cases.

Hydrodynamics Simulations↗