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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 289 records · Page 16

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NASA NTRS2009

Investigating Liquid CO2 as a Coolant for a MTSA Heat Exchanger Design

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO 2) control for a future Portable Life Support System (PLSS), as well as water recycling. CO 2 removal and rejection is accomplished by driving a sorbent through a temperature swing of approximately 210 K to 280 K . The sorbent is cooled to these sub-freezing temperatures by a Sublimating Heat Exchanger (SHX) with liquid coolant expanded to sublimation temperatures. Water is the baseline coolant available on the moon, and if used, provides a competitive solution to the current baseline PLSS schematic. Liquid CO2 (LCO2) is another non-cryogenic coolant readily available from Martian resources which can be produced and stored using relatively low power and minimal infrastructure. LCO 2 expands from high pressure liquid (~5800 kPa) to Mars ambient (0.8 kPa) to produce a gas / solid mixture at temperatures as low as 156 K. Analysis and experimental work are presented to investigate factors that drive the design of a heat exchanger to effectively use this sink. Emphasis is given to enabling efficient use of the CO 2 cooling potential and mitigation of heat exchanger clogging due to solid formation. Minimizing mass and size as well as coolant delivery are also considered. The analysis and experimental work is specifically performed in an MTSA-like application to enable higher fidelity modeling for future optimization of a SHX design. In doing so, the work also demonstrates principles and concepts so that the design can be further optimized later in integrated applications (including Lunar application where water might be a choice of coolant).

Paul, Heather L.↗

DOE OSTI2024

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Comparative study between experimental measurements and model predictions of bubble rise velocity in molten LiCl-KCl

This research investigated the shape and rise velocities of gas bubbles (helium, nitrogen, argon, and krypton) in molten LiCl-KCl at 500 °C using a specialized optical visualization apparatus. The apparatus features a custom quartz rectangular prism cell housed in a furnace, enabling the application of the shadowgraph measurement technique. Bubbles with diameters between 0.45 and 3.2 mm were produced via a capillary, and their equivalent diameters and rise velocities were analyzed using high-resolution imaging and centroid tracking algorithms. Important non-dimensional parameters were calculated to characterize behavior. Bubbles with 0.03 < Eo < 1.32, 17 < Re < 718, and Mo order of 10 -11 were generated in the molten LiCl-KCl, revealing that bubbles with diameters less than 1.46 mm remain spherical, whereas those with larger diameters are ellipsoidal. The transition and oscillation onset for bubble shape occurred at larger diameters than those previously observed in air–water systems. Comparative analysis of the gas-molten salt data against existing air–water bubble rise velocity correlations highlighted inaccuracies in predictions, particularly across the different bubble regimes. An existing correlation was calibrated to the molten salt data to improve the bubble velocity predictions. In conclusion, this study contributes essential experimental data for the design and safety evaluation of molten salt reactors and offers insights for the optimization of sparge systems for fission product removal.

Bubble rise velocity↗

NASA NTRS1973

Determination and evaluation of performance limit criteria of fast-response electrohydraulic servosystems

Limit criteria for determining the dynamic performance capabilities of high performance, fast-response (greater than 100 Hz) electrohydraulic servosystems are presented. A detailed analysis of the maximum load locus of these systems is used as a basis for the derivation of improved limit criteria. These criteria predict the maximum performance limits caused by system nonlinearities and physical limitations. The criteria are applied to experimental data to verify their validity. Design criteria which assist in the selection of system components for optimal performance are also discussed.

Zeller, J. R.↗

NASA NTRS1989

An empirical study of the performance of APMOVPE AM0 InP homojunction solar cells as a function of emitter thickness and doping, and base doping

Their excellent radiation resistance and conversion efficiencies greater than 20 percent, measured under global conditions, make InP shallow-homojunction solar cells very attractive for space or terrestrial application. In addition, modeling studies show that, for optimized design, efficiencies of these devices should exceed 20 percent even under AM0 conditions. However, a systematic experimental investigation of the influence of the various cell design parameters on cell performance has not as yet been made. For the n+/p/p+ structures investigated in the previous modeling study, the design parameters include the impurity concentrations and thicknesses of the emitter and base layers. In the work reported here, researchers discuss an experimental investigation of the effects on cell performance of varying the impurity concentrations of the emitter and base and thickness of the emitter.

Wanlass, M. W.↗

NASA NTRS1994

Full-scale testing and analysis of fuselage structure

This paper presents recent results from a program in the Boeing Commercial Airplane Group to study the behavior of cracks in fuselage structures. The goal of this program is to improve methods for analyzing crack growth and residual strength in pressurized fuselages, thus improving new airplane designs and optimizing the required structural inspections for current models. The program consists of full-scale experimental testing of pressurized fuselage panels in both wide-body and narrow-body fixtures and finite element analyses to predict the results. The finite element analyses are geometrically nonlinear with material and fastener nonlinearity included on a case-by-case basis. The analysis results are compared with the strain gage, crack growth, and residual strength data from the experimental program. Most of the studies reported in this paper concern the behavior of single or multiple cracks in the lap joints of narrow-body airplanes (such as 727 and 737 commercial jets). The phenomenon where the crack trajectory is curved creating a 'flap' and resulting in a controlled decompression is discussed.

Miller, M.↗

NASA NTRS2002

The Focal Surface of EUSO Telescope

The Extreme Universe Space Observatory (EUSO) is a science mission under conceptual design for the detection of extremely high energy cosmic rays and neutrinos by the observation of time-resolved images of atmospheric fluorescence photons generated along the extensive air shower, in the near ultraviolet wavelength region. A refractive telescope with double-sided double Fresnel lens will be employed to achieve a large field of view of 60 degrees. The energy and arrival direction of the primary particles will be determined by observing the time evolution of the airshower. The focal surface of the EUSO telescope will be segmented to a few hundred thousand pixels to resolve the entire field of view with the angular resolution of the order of 0.1 degree. The time evolution will be observed with the time resolution of 0.8 microsecond. A large scale array of multianode photomultiplier (MAPMT) is being studied as the EUSO focal surface. The MAPMT array is capable of detecting near ultraviolet photons at single photoelectron level. In this contribution, we will report the present status of the focal surface design including the optimization of anode segmentation and the minimization of the dead area and discuss overall experimental performance in detecting extensive airshowers.

Shimizu, H. M.↗

DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗

DOE OSTI2025

IER 516 - Zirconium Test Assembly (ZTA) Preliminary Experimental Design [Slides]

Zirconium present in large quantities in criticality safety applications such as fuel manufacturing and processing facilities, and in commercial thermal nuclear reactors. ICSBEP lacks benchmarks optimized for zirconium sensitivity to validate zirconium evaluations. Evaluators lack isotopic differential nuclear data in regions of high importance to fission systems.

K-PSO 2.0↗

DOE OSTI2025

Spatial‐Uniformity–Driven Bayesian Optimization for Rapid Development of Printed Perovskite Solar Cells

Printed metal halide perovskites can enable rapid, roll-to-roll manufacturing of a broad class of optoelectronics—flexible solar cells and imagers among them—while promising cost and speed advantages over incumbent silicon. However, though current methods offer high throughput and patterning capabilities, perovskite films’ spatial heterogeneity remains a challenge for large-area devices. Here, a spatial-uniformity-driven Bayesian optimization (BO) approach is leveraged to accelerate the development of printed perovskite solar cells and improve large-area device performance. Using a BO surrogate model, a 6D design space of ink chemistry and printing physics is explored via extensive iterative experimentation (≈100) informed by an objective function capturing spatial photoluminescence (PL) variance. It is discovered that optimizing for uniformity drives rapid advances in photovoltaic performance, yielding ≈20% power conversion efficiency (PCE) for small area (0.134 cm 2 ) devices and > 16% for large area (1 cm 2 ) devices. This machine-learning approach simultaneously enables rheological comparison of ink formulations that accelerate the leveling of Saffman-Taylor artifacts and improve film uniformity. Here, this showcases uniformity-driven BO as an efficient approach for uncovering the key printing physics and mitigating spatial heterogeneity to enable device scaling beyond small cell areas.

14 SOLAR ENERGY↗

DOE OSTI2025

Advances in Modeling Capabilities for Critical Mineral Separation Technologies: A PrOMMiS Overview

This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

critical minerals and materials↗

NASA NTRS2011

Frame-Transfer Gating Raman Spectroscopy for Time-Resolved Multiscalar Combustion Diagnostics

Accurate experimental measurement of spatially and temporally resolved variations in chemical composition (species concentrations) and temperature in turbulent flames is vital for characterizing the complex phenomena occurring in most practical combustion systems. These diagnostic measurements are called multiscalar because they are capable of acquiring multiple scalar quantities simultaneously. Multiscalar diagnostics also play a critical role in the area of computational code validation. In order to improve the design of combustion devices, computational codes for modeling turbulent combustion are often used to speed up and optimize the development process. The experimental validation of these codes is a critical step in accepting their predictions for engine performance in the absence of cost-prohibitive testing. One of the most critical aspects of setting up a time-resolved stimulated Raman scattering (SRS) diagnostic system is the temporal optical gating scheme. A short optical gate is necessary in order for weak SRS signals to be detected with a good signal- to-noise ratio (SNR) in the presence of strong background optical emissions. This time-synchronized optical gating is a classical problem even to other spectroscopic techniques such as laser-induced fluorescence (LIF) or laser-induced breakdown spectroscopy (LIBS). Traditionally, experimenters have had basically two options for gating: (1) an electronic means of gating using an image intensifier before the charge-coupled-device (CCD), or (2) a mechanical optical shutter (a rotary chopper/mechanical shutter combination). A new diagnostic technology has been developed at the NASA Glenn Research Center that utilizes a frame-transfer CCD sensor, in conjunction with a pulsed laser and multiplex optical fiber collection, to realize time-resolved Raman spectroscopy of turbulent flames that is free from optical background noise (interference). The technology permits not only shorter temporal optical gating (down to <1 s, in principle), but also higher optical throughput, thus resulting in a substantial increase in measurement SNR.

Nguyen, Quang-Viet↗

NASA NTRS2006

An Axenic Plant Culture System for Optimal Growth in Long-Term Studies: Design and Maintenance

The symbiotic co-evolution of plants and microbes leads to difficulties in understanding which of the two components is responsible for a given environmental response. Plant-microbe studies greatly benefit from the ability to grow plants in axenic (sterile) culture. Several studies have used axenic plant culture systems, but experimental procedures are often poorly documented, the plant growth environment is not optimal, and axenic conditions are not rigorously verified. We developed a unique axenic system using inert components that promotes plant health and can be kept sterile for at least 70 d. Crested wheatgrass (Agropyron cristatum cv. DII) plants were grown in sand within flow-through glass columns that were positively pressured with filtered air. Plant health was optimized by regulating temperature, light level, CO2 concentration, humidity, and nutrients. The design incorporates several novel aspects, such as pretreatment of the sand with Fe, graduated sand layers to optimize the air-water balance of the root zone, and modification of a laminar flow hood to serve as a plant growth chamber. Adaptations of several sterile techniques were necessary for maintenance of axenic conditions. Axenic conditions were verified by plating and staining leachates as well as rhizoplane stain. This system was designed to study nutrient and water stress effects on root exudates, but is useful for assessing a broad range of plant-microbe-environment interactions. Based on total organic C analysis, 74% of exudates was recovered in the leachate, 6% was recovered in the bulk sand, and 17% was recovered in the rhizosphere sand. Carbon in the leachate after 70 d reached 255 micro-g/d. Fumaric, malic, malonic, oxalic, and succinic acids were measured as components of the root exudates.

Henry, Amelia↗

NASA NTRS1991

Multi-element airfoil optimization for maximum lift at high Reynolds numbers

An experimental study has been performed to assess the maximum lift capability of a supercritical multielement airfoil representative of an advanced transport aircraft wing. The airfoil model was designed with a leading-edge slat and single or two-segment trailing-edge flaps. Optimization work was performed at various slat/flap deflections as well as gap/overhang positions. Landing configurations and the attainment of maximum lift coefficients of 4.5 with single-element flaps and 5.0 with two-segment flaps was emphasized. Test results showed a relatively linear variation of the optimum gap/overhang positioning of the slat versus slat deflection, considerable differences in optimum rigging between single and double segment flaps, and large Reynolds number effects on multielement airfoil optimization.

Valarezo, Walter O.↗

DOE OSTI2024

Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications

Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni Metal–Organic Framework

Metal–organic frameworks (MOFs), with their distinctive porous structures and tunable chemical properties, have shown immense promise in the separation and storage of gases. Currently, the accurate simulation of their adsorptive properties remains challenging, especially for systems where the molecules fit very tightly into the pores. Traditional simulation methods often approximate the frameworks as rigid and do not account for the framework flexibility seen in materials such as NbOFFIVE-1-Ni. First-principles molecular dynamics (FPMD) simulations offer the desired accuracy in modeling this flexibility but are limited by their extensive computational demands, rendering them impractical for long simulations. Conversely, classical force field-based simulations offer computational efficiency but lack the necessary accuracy. Here, to break this accuracy-efficiency trade-off, we have developed machine learning interatomic potentials trained on energies and forces from FPMD to model the framework flexibility of NbOFFIVE-1-Ni in the presence of water over nanosecond time scales. Furthermore, by integrating MLIP-driven molecular dynamics (MLIP-MD) with grand canonical Monte Carlo (GCMC) simulations, we further incorporated framework flexibility into adsorption predictions, yielding water adsorption isotherms that better align with experimental data compared to those of conventional GCMC simulations. These advances offer new opportunities for the design and optimization of MOFs in gas storage and separation applications.

adsorption↗

NASA NTRS1972

Outer planet Grand Tour missions photometry/polarimetry experiment critical components study

Work performed during this effort was limited to two primary areas of technical concern: optical design optimization, and sensor selection. An optical system concept was established, and various system components were evaluated through experimental test sequences. Photodetectors were investigated for the applicability in meeting OPGT requirements as constrained by the photometry/polarimetry team directives. The most promising (gallium arsenide PMT) was further experimentally tested to ascertain its behavior with respect to anticipated environmental conditions. Results of testing and summary of the preceding tradeoff study effort are presented.

Pellicori, S. F.↗