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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Investigating Temperature Uniformity and Accuracy in PV Module Lamination: A Verification Study

This study investigates the temperature uniformity and accuracy of a photovoltaic (PV) module lamination process by addressing inconsistencies identified in 2017 data where irregular temperature changes were observed across setpoints. The 2017 data showed a notable drop in temperature upon bladder initiation, except for the 145 degrees Celsius profile. This inconsistency indicated potential inaccuracies in manual data recording methods. To address this concern, a verification experiment was conducted to evaluate temperature uniformity across the 2014 Bent River SPL2828 laminator platen and within test samples. Thermocouples, paired with Omega data acquisition software, were deployed to measure temperatures at multiple platen locations and within test samples. The experiment compared lamination temperatures of polyethylene-co-vinyl acetate (EVA) encapsulant when paired with solite glass or TPE backsheets. The methodology included verifying temperature uniformity directly on the platen and by using a large glass/EVA/glass sample using multiple thermocouples. Smaller samples were built with glass/EVA/glass and glass/EVA/backsheet configurations with one centered thermocouple to verify and compare sample temperatures. This verification aims to refine lamination temperature profiles, enhance data accuracy and provide insights into optimal process control for uniform module lamination. Ensuring consistent and uniform lamination may improve the accuracy and reliability of research outcomes.

14 SOLAR ENERGY↗
DOE OSTI2024

A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument

Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2025

Fiber orientation and porosity in large-format extrusion process: The role of processing parameters

Controlling fiber orientation and porosity in short-fiber thermoplastic composites is important for enhancing mechanical, electrical and thermal properties in large-format additive manufacturing. This study employs a factorial design of experiments (DoE) to assess the effects of nozzle diameter (5.08 mm–10.16 mm), temperature (230–250 °C), and extruder screw speed (150–280 rpm) on flow rate, shear rate, porosity, fiber orientation, fiber length and tensile strength in 20 % carbon fiber-filled acrylonitrile butadiene styrene. ANOVA results show that screw speed significantly impacts flow rate, while nozzle diameter and temperature have lesser effects. Shear rate increases with smaller nozzles and higher speeds. Porosity decreases from 5.58 % with a 10.16 mm nozzle to 3.11 % with a 5.08 mm nozzle at 150 rpm due to increased shear rates, which induce shear thinning, reducing viscosity and facilitating gas escape. Larger nozzles (10.16 mm) produce larger, more heterogeneous pores, while smaller nozzles (5.08 mm) yield smaller, uniform pores. Beads produced with the 5.08 mm nozzle exhibit longer fiber lengths due to reduced residence time, lower shear stress, and better alignment. Fiber orientation improves with smaller nozzles due to higher shear rates but decreases with higher screw speeds (280 rpm) due to shorter residence times. The highest fiber alignment (A xx ∼ 0.65) and low porosity (∼3%) were achieved with a 5.08 mm nozzle at 150 rpm, while equivalent additive manufacturing-compression molding samples exhibited better tensile strength (∼93 MPa) under these conditions. In conclusion, these findings emphasize the importance of optimizing processing parameters to enhance fiber alignment and reduce porosity for improved mechanical performance.

Design of experiments↗
DOE OSTI2024

Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

Science & Technology - Other Topics↗
NASA NTRS2010

Constraints and Opportunities in GCM Model Development

Over the past 30 years climate models have evolved from relatively simple representations of a few atmospheric processes to complex multi-disciplinary system models which incorporate physics from bottom of the ocean to the mesopause and are used for seasonal to multi-million year timescales. Computer infrastructure over that period has gone from punchcard mainframes to modern parallel clusters. Constraints of working within an ever evolving research code mean that most software changes must be incremental so as not to disrupt scientific throughput. Unfortunately, programming methodologies have generally not kept pace with these challenges, and existing implementations now present a heavy and growing burden on further model development as well as limiting flexibility and reliability. Opportunely, advances in software engineering from other disciplines (e.g. the commercial software industry) as well as new generations of powerful development tools can be incorporated by the model developers to incrementally and systematically improve underlying implementations and reverse the long term trend of increasing development overhead. However, these methodologies cannot be applied blindly, but rather must be carefully tailored to the unique characteristics of scientific software development. We will discuss the need for close integration of software engineers and climate scientists to find the optimal processes for climate modeling.

Schmidt, Gavin↗
DOE OSTI2024

Optimizing the Heisenberg Vortex Tube for Hydrogen Cooling (Final Technical Report)

Hydrogen utilization at Plug Power sites ranges between 70-93% with the remainder vented and lost to the atmosphere. The goal of this project was to improve hydrogen utilization at Plug Power fulfillment centers via the patented Heisenberg vortex tube (HVT). The HVT combines conventional counterflow vortex tubes with para-orthohydrogen conversion catalyst to allow the cooling of hydrogen flows with no moving parts or external energy inputs. In year 1 of this project the HVT was evaluated to improve hydrogen utilization via the following concepts: 1) increasing liquid hydrogen pump volumetric efficiency by 20% through vapor separation and subcooling of the liquid, 2) reducing liquid hydrogen storage tank boil-off losses by 20% through thermal vapor shielding (TVS), and 3) increase isentropic efficiency of supercritical hydrogen expansion at 40-50 K, from 31% to over 40%. The TVS concept was selected as the most favorable application for further development. While the conceptual analysis was underway, the cryocatalysis hydrogen experiment facility (CHEF) was retrofitted with a new cryocooler, higher pressure condenser tanks, and in-situ fiber-optic Raman probes for ortho-parahydrogen composition analysis at the inlets and outlets of the HVT. To our knowledge this is the first in-situ implementation of cryogenic Raman probes for ortho-parahydrogen analysis. Subsequent testing of catalyzed and non-catalyzed HVT identified a low flow rate, high-conversion efficacy regime well suited for TVS development. Computational fluid dynamics (CFD), Reduced order modeling (ROM), and quantum Monte Carlo atomistic simulations were applied to optimize the design of the HVT implemented for TVS. The simulations and analysis identified a ruthenium-based catalyst as the most optimal for high conversion with little pressure losses and was matched to experimental measurements. These results indicated the design could achieve the 20% boil-off reduction target. An HVT field-trial was designed and constructed by Plug Power and implemented at a customer site. However, budget limitations reduced the amount of catalyst that could be applied to the HVT. With the reduced amount of catalyst, CFD analysis estimated about 3% reduction in boil-off. A similar amount of boil-off reduction was observed during preliminary measurements during tank commissioning. However, the results have a large margin for error and were operated at relatively low tank temperatures when the HVT has the lowest potential for improvement. This project demonstrated the use of para-orthohydrogen catalysis for reducing liquid hydrogen tank boil-off losses. The field trial system is anticipated to provide long-term experimental measurements on tank operational performance that will allow Plug Power to instrument additional tanks in the future. Recommendations for future work include the development of higher activity, lower cost para-orthohydrogen conversion catalyst for use in the HVT. Tank process optimizations could maximize the efficacy of the HVT and further improve hydrogen utilization.

08 HYDROGEN↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2024

Adaptive immersed isogeometric level-set topology optimization

Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.

36 MATERIALS SCIENCE↗
NASA NTRS1993

An optimal GPS data processing technique for precise positioning

A mathematical formula to optimally combine dual-frequency GPS pseudorange and carrier phase (integrated Doppler) data streams into a single data stream is derived in closed form. The data combination reduces the data volume and computing time in the filtering process for parameter estimation by a factor of 4 while preserving the full data strength for precise positioning. The resulting single data stream is that of carrier phase measurements with both data noise and bias uncertainty strictly defined. With this mathematical formula the single stream of optimally combined GPS measurements can be efficiently formed by simple numerical calculations. Carrier phase ambiguity resolution, when feasible, is strengthened due to the preserved full data strength with the optimally combined data and the resulting longer wavelength for the ambiguity to be resolved.

Wu, Sien-Chong↗
DOE OSTI2024

Tuning Microstructure of Mesophase Pitch Carbon Fiber by Altering the Carbonization Ramp Rate

The microstructure of mesophase pitch carbon fibers (CFs) are tuned by varying ramp rates from 1 to 50 °C min –1 up to 1000 °C to study the effect of ramp rate on CFs’ microstructure, thermal and mechanical properties with the goal of offsetting the cost by decreasing cycle time. The ramp rates represent carbonization times ranging from 16.4 to 1.17 h, not including cool down. Differential scanning calorimetry, thermogravimetric analysis, and derivative thermogravimetry are used to investigate the impact ramp rate has on the thermal properties of mesophase pitch. It is found that lower ramp rates are endothermic in nature with a lower temperature onset and maximum weight loss. Higher ramp rates possess an exothermic nature with higher temperatures resulting in maximal weight loss over a smaller range of temperatures. Mechanical testing shows varying CF strengths and moduli dependent on ramp rate and an optimized process is developed to produce the strongest CF. Furthermore, microstructural characterization revealed that faster ramp rates lead to smaller interplanar spacings and larger crystallites but possessed greater disorder.

36 MATERIALS SCIENCE↗
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

Polymer Deconstruction and Redesign Strategies for Plastics Recycling

Advancing plastics recycling requires both the selective deconstruction of existing polymers and the design of new materials that enable efficient reuse without loss of performance. This perspective highlights an integrated approach that is rooted in polymer chemistry, catalysis, and process engineering which can enable a circular plastics economy. Here, we outline recent advances in catalytic, solvolytic, and enzymatic pathways for plastic deconstruction, and examine the molecular design principles driving next-generation recyclable-by-design and bio-based polymers. Despite these advances, major knowledge gaps remain in understanding the evolution of polymer morphology and catalyst structure during deconstruction, assessing deconstruction processes with realistic polymers, and offering redesigned polymers with competitive cost and environmental advantage over conventional plastics. United States Department of Energy (U.S. DOE) national laboratories offer unique capabilities to address these challenges through in situ and operando characterization, high-throughput experimentation, environmental studies, technoeconomic and life cycle assessment, scale-up support, and collaboration networks. Advances made in understanding plastic deconstruction mechanisms and structure-property correlations of redesigned polymers inform emerging research directions including autonomous experimentation, real-time feedback-enabled process optimization, and protein engineering for enzymatic depolymerization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Balancing Doses of EL222 and Light Improves Optogenetic Induction of Protein Production in Komagataella phaffii

ABSTRACT Komagataella phaffii, also known asPichia pastoris, is a powerful host for recombinant protein production, in part due to its exceptionally strong and tightly controlled P AOX1 promoter. MostK. phaffiibioprocesses for recombinant protein production rely on P AOX1 to achieve dynamic control in two‐phase processes. Cells are first grown under conditions that repress P AOX1 (growth phase), followed by methanol‐induced recombinant protein expression (production phase). In this study, we propose a methanol‐free approach for dynamic metabolic control inK. phaffiiusing optogenetics, which can help enhance input tunability and flexibility in process optimization and control. The light‐responsive transcription factor EL222 fromErythrobacter litoralisis used to regulate protein production from the P C120 promoter inK. phaffiiwith blue light. We used two system designs to explore the advantages and disadvantages of coupling or decoupling EL222 integration with that of the gene of interest. We investigate the relationship between EL222 gene copy number and light dosage to improve production efficiency for intracellular and secreted proteins. Experiments in lab‐scale bioreactors demonstrate the feasibility of the outlined optogenetic systems as potential alternatives to conventional methanol‐inducible bioprocesses usingK. phaffii.

Biotechnology & Applied Microbiology↗
DOE OSTI2026

Effects of Cooling Rates on Self-Reinforced Polyethylene Composites via Multiscale Experimental Characterizations

This study investigates cooling rate effects on self-reinforced polyethylene composites (PE-SRCs), focusing on macroscopic thermal and mechanical properties, and micro- to sub-micron morphologies. Unlike conventional fiber-reinforced composites (e.g., carbon and glass fiber based), cooling rate effects on SRCs remain less studied. PE-SRCs were fabricated from ultrahigh molecular weight polyethylene (UHMWPE) fabric and high-density polyethylene (HDPE) matrix with cooling rates from 0.76°C/min to 425°C/min. Tensile properties, including modulus (~9.5 GPa), strength (~360 MPa), and ultimate elongation (~6%), showed no significant cooling rate dependence. However, thermal analysis revealed melting temperature of HDPE matrix decreased from ~130°C to ~126.5°C with total crystallinity reducing from 84% to 75%. Wide-angle X-ray scattering confirmed decreasing crystallinity but at lower values (67%–59%), as thermal analysis might overestimate crystallinity. Small-angle X-ray scattering showed long period decreased from 24.3 to 17.4 nm, attributed to thinner lamellae and closer packing, suggesting limited crystal growth and faster nucleation. While cooling rates alter melting point and crystalline structures of HDPE matrix, these changes do not translate to substantial impact on macroscale mechanical properties of PE-SRCs. This insensitivity might be related to low interfacial shear strength at PE/PE interfaces, enabling process optimization while maintaining mechanical performance.

Cooling rate↗
DOE OSTI2026

Characterization of NBCMA—A DAPO Precursor

As part of our investigation into the process optimization of 2,6-diaminopyrazine-1-oxide (DAPO), small-scale sensitivity testing (impact, friction, and ESD) was conducted on the precursor, N-nitroso-bis(cyanomethyl)amine (NBCMA). NBCMA exhibited moderate sensitivity to friction, whereas no sensitivity to impact or spark was observed.

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

Results of an interlaboratory study on the working curve in vat photopolymerization II: Towards a standardized method

The working curve measurement in photopolymer additive manufacturing is a ubiquitous measure of the cure depth of a printing resin as a function of radiant exposure of light. The fit parameters from this measurement (the depth of light penetration D p and the critical exposure E c ) are used to evaluate and report a resin’s printability, optimize processing parameters, and inform print and resin quality control. Despite its widespread use, the working curve lacks a standard measurement method. Here, following up on our paper “Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization” from last year, an interlaboratory study on the working curve was performed using calibrated, reproducible, bandpass filtered light sources. With these light sources, the variability between labs in measured working curves was dramatically reduced from the initial interlaboratory study. Aggregate data from this experiment produced reliable D p and E c measurements at 385 nm of 39.2 ± 3.7 µm and 12.3 ± 3.0 mJ cm −2 , respectively. At 405 nm the values of D p and E c are 69.3 ± 3.8 µm and 17.9 ± 2.3 mJ cm −2 , respectively. The results are agnostic to the thickness measurement tool utilized by participants, ensuring broad applicability across laboratories. We also tested the generalizability of the proposed method of using a filtered light source by filtering a commercial 405 nm light source and obtaining a working curve in agreement with the aggregate data from the interlaboratory study. This interlaboratory study provides a basis for a documentary standard for the working curve, so that the entire photopolymer additive manufacturing industry can share reproducible and interoperable working curve data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hydrocyclone pre-processing of wastewater algae: A strategy for inorganic ash separation

Microalgae cultivation on wastewater can provide remediation and generate valuable feedstocks for biofuel production. Wastewater algae typically have a high percentage of inorganic ash, which can reduce yield and quality of biocrude produced during hydrothermal liquefaction (HTL). Here, in this work, we evaluated the ability of hydrocyclone pre-processing to remove inorganic ash from wastewater algae. The pH of the algae slurry was adjusted to 9.5 to encourage the formation of precipitates and create a density differential between ash particles and algal cells. Hydrocyclone processing successfully concentrated ash particles in the underflow fraction and reduced the total ash percentage in the overflow fraction. Overall, hydrocyclone processing reduced the total ash by 21%, while only 8% of organics were lost. Elemental and mineral analysis showed that Mg and P were concentrated in the underflow in the form of baricite (an isomorph of vivianite). Future research should focus on improving vivianite and/or baricite formation, and therefore ash removal, by providing a reducing environment. The addition of multiple hydrocyclones in series could also improve the removal of ash. We concluded that hydrocyclone treatment of wastewater algae is a feasible method to remove inorganic ash, but further process optimization is required.

09 - BIOMASS FUELS↗