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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Spray coating for washout tooling by binder jet additive manufacturing

We report washout tooling is a process in which a removable mold or fixture is disintegrated using a solvent. Binder Jet (BJ) additive manufacturing holds promise for creating customized washout molds from common materials. BJ-based washout tooling provides an advanced strategy in manufacturing fiber-reinforced composites (FRCs), especially for customized hollow FRCs. However, washout tools formed by BJ require an impermeable barrier between the porous tooling surface and the parts laid up on the surface to prevent the resin from infiltrating the tooling surface and bonding the tool to the FRC. The conventional method for preventing resin migration into the washout mold from the FRC is winding Teflon® tape over the entire surface, which is labor-intensive and very difficult to deploy for specific geometries. Here, we report the development of a polymer spray coating method to seal the tooling surface and prevent resin infiltration without affecting the washout properties needed for sacrificial tooling. Two water-soluble polymers, poly(sodium 4-styrenesulfonate) (PSS–Na) and poly(vinylsulfonic acid, sodium salt) (PVS–Na), were investigated for spray coating. PVS-Na maintained tighter geometric tolerances and provided excellent thermal stability with the onset of decomposition at 320 °C and glass transition temperature of 280 °C. Spray coating with PVS-Na streamlines the process of forming a protective layer for the washout molds by BJ, reduces migration of the resins from the FRC, and significantly improves the production efficiency of complex washout tools.

42 ENGINEERING↗
DOE OSTI2023

Core-shell structured tow-pregs enabled additive manufacturing of continuously reinforced thermoset composites

One of the major challenges in thermally curable polymer composite additive manufacturing is the slow curing time of thermally curable resins, coupled with a significant drop in thermoset viscosity upon heating, making the formation of complex shapes difficult. To tackle these issues, we have introduced a novel rapid composite preformation strategy called Tow-Preg Cladding (TPC). This technique involves the use of a thin and rapidly curable dual-cure resin, which combines a thermally curable resin (such as epoxy) with a fast photo-curable resin. The dual-cure resin is applied as a coating over a thermally curable resin tow-preg, enabling the formation of a rigid cladding that supports and shapes the tow-pregs into the desired geometries and patterns. The key advantage of this approach lies in the sequential formation of an interpenetrating polymer network (IPN) at the interlayer between laminates, resulting from the curing of the photocurable resin and the thermally curable resin sequentially. This IPN formation enhances interlaminar bonding in the composite, contributing to its overall strength and performance. The composites produced using this technique exhibited impressive mechanical properties, including longitudinal tensile strength of 1058.2 MPa, longitudinal tensile modulus of 74.2 GPa, flexural strength of 1080.4 MPa, and flexural modulus of 41.7 GPa. Overall, our Tow-Preg Cladding approach shows great promise in overcoming the challenges associated with thermally curable polymer composite additive manufacturing. It offers a viable solution to creating strong, shape-specific thermoset composites efficiently with enhanced mechanical properties.

42 ENGINEERING↗
DOE OSTI2024

High plasticity in refractory composite fabrication by ultrasonic additive manufacturing

Refractory metal composites are desirable for use in extreme environments that require materials with high specific strengths and resilience to external environments such as that found in nuclear and aerospace. However, due to the high melting temperature of refractories, liquid state joining processes such as welding remains difficult. Ultrasonic additive manufacturing (UAM) provides a potential route for processing refractory composites because it is a solid-state (i.e., no-melting) process and allows for intermittent machining operations to be performed between welds. Further, to demonstrate refractory composite fabrication, this study utilized UAM to machine a cavity to locate and sequester a Mo foil in a Zircaloy-4 (Zry-4) baseplate and additively build over the top with Zry-4 foils, thereby embedding the Mo in Zry-4 matrix. Significant deformation of the Zry-4 microstructure was observed: this deformation caused adiabatic heating and subsequent dynamic recrystallization through the transformation from α→β and then back to α as the material cooled. Flexural testing of the Zr–Mo composite revealed no delamination or failure, but the strength was not as expected, falling lower than a cold-worked Zry-4 sample. Finite element analysis supported that some bonding must have existed between the Zry-4 and Mo. There was indeed an interdiffusion zone at the Zry-4 foil–Mo foil interface, observing a metastable body-centered cubic β-Zr lathe. It was determined that sufficient strain energy was present to encourage the nucleation of the β-Zr grain along α-Zr grains. Future work is warranted to investigate UAM for refractory composite fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2022

Optimization of matrix viscosity improves polypropylene fiber dispersion and properties of engineered cementitious composites

Engineered cementitious composites (ECC) is a durable cementitious material with high tensile ductility and strain-hardening characteristics. Although considered as a cost-effective fiber for ECC, polypropylene (PP) fiber is reportedly difficult to disperse in mortar matrix due to its high aspect ratio and hydrophobicity. In this study, the matrix viscosity of a low-carbon ECC based on limestone calcined clay cement was tailored as a variable to improve PP fiber dispersion under a pre-determined mixing protocol. The effect of matrix viscosity on the composite fresh and hardened properties was investigated experimentally. Results suggested an optimal range of matrix viscosity (10.3–11.5 Pa . s) favors the composite tensile strength and strain capacity at 28 days. At the optimal state with a 0.1 % viscosity modifying admixture (VMA)-to-binder mass ratio, PP-ECC achieved 7.0 % tensile strain capacity and 3.5 MPa ultimate tensile strength. When matrix viscosity falls outside the desired range, both ultimate tensile strength and strain capacity were diminished. By tailoring the VMA dosage, the matrix viscosity can be adjusted for desired fiber dispersion, workability, and mechanical properties. Finally, the findings of this study provide a technical reference for the practical design and application of PP-ECC.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A signal compensation based cascaded PI control for an industrial heat exchange system

Since the characteristics of the industrial heat exchange system (HES) are drastically affected by variations of the steam pressure, ambient temperature and water quality, it is difficult for the classical cascade PI control system to meet the desirable control objectives. Therefore, in this paper, a deterministic linear model with unmodeled dynamics is used to describe the dynamic characteristics of the HES. It should be noted that the unmodeled dynamics at the previously sampled instant can be calculated using actual measurements and that the tracking error, which includes effects of the rate of changes of unmodeled dynamics on the controlled model, can be accurately determined. An additional compensating signal is obtained with the feedforward control together with a one-step optimal control law. Consequently, the cascade PI control strategy with the signal compensation is established. Furthermore, the simulation and industrial application show that the proposed method can effectively improve the performance of the cascade PI control system, especially when the unmodeled dynamics is subjected to unknown variations.

42 ENGINEERING↗
DOE OSTI2026

Bridging semantics, control specifications and assessment: A library for scalable demand flexibility controls

There is growing recognition that Demand Flexibility (DF) can play a major role in enhancing grid reliability, with building control applications emerging as key enablers for DF. However, the traditional approach to deploying new control applications in buildings, including those for DF, remains largely manual and tailored to individual buildings, making it difficult to scale. While research efforts have explored semantics-driven portability, DF controls specification, and assessment approaches, these initiatives are fragmented and limited in scope. This paper proposes a novel methodology, grounded in design science research, to integrate these elements and create a comprehensive DF controls library for both industry and academia. This approach is applied to develop the Demand FLEXibility controls LIBrary using Semantics (DFLEXLIBS), an extensible open-source library that provides DF controls for HVAC systems in Python. DFLEXLIBS enables portable, easy-to-deploy controls that abstract building-specific data points, facilitating assessment across diverse buildings. DFLEXLIBS features nine different control applications, and it is successfully implemented and tested across four virtual and two real buildings, bridging the gap between semantics-driven portability, DF controls specification, and rigorous performance assessment. Its benefits are measured by a reusability ratio greater than 90% and a functional overlap ratio of around 70% for the most common functions used in the library, significantly reducing time for deploying new controls.

Controls library↗
DOE OSTI2021

Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities

Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. Furthermore, this model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Perspectives for self-driving labs in synthetic biology

Self-driving labs (SDLs) combine fully automated experiments and data collection with artificial intelligence (AI) and control algorithms that decide not only the set of parameters for the next experiment, but also potentially which scientific hypotheses to test. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. Whereas there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single, easily accessible, target for affecting the incredibly wide repertoire of biological cell behavior. Since they can provide large amounts of high-quality data, SDLs can be a platform for AI to develop approaches to systematically convert data into scientific knowledge systems. These knowledge systems can be used both to understand the biological world and to design bioengineered systems to fit a desired specification (inverse design). However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Plastic homogeneity in nanoscale heterostructured binary and multicomponent metallic eutectics: An overview

Heterostructured materials comprised of relatively soft/hard disparate phases typically exhibit composite strengthening but lack plastic deformability at ambient temperatures. However, heterostructured systems comprised of nanoscale phases can simultaneously enhance yield strength and strain hardening, thereby promoting uniform distribution of plastic flow. In this review, the atomic-scale deformation mechanisms in model systems of eutectic alloys, Al-Al 2 Cu and Al-Si, refined to nanoscales via laser rapid solidification are discussed, and compared with literature on multi-component (high entropy) eutectics such as Ni-Al-Fe-based with Cr and/or Co additions. The nano-lamellar Al-Al 2 Cu structures exhibit unit defect mechanisms not reported in monolithic Al 2 Cu intermetallic: localized shear on {0 1 1} and shear-induced faults on {1 2 1} planes, constrained by closely-spaced dislocation arrays in Al confined by Al/Al 2 Cu interfaces. The unexpected plasticity mechanisms are enabled by slip continuity in nanoscale Al-Al 2 Cu eutectics associated with the orientation relationship and interface habit planes. In nano-fibrous Al-Si eutectic, tensile ductility at strength approaching 600 MPa is observed resulting from dislocation plasticity in the nano-Al channels and cracking in Si nanofibers. Molecular dynamics simulations show that Al dislocations easily cross-slip (screw) or climb (edge) along Al-Si interfaces, making slip transmission difficult. The propagation of nano-cracks is suppressed by surrounding strain hardening Al, retaining good ductility of the sample, in spite of lack of direct slip transmission. Finally, the critical unit mechanisms of slip transmission and interface-enabled plasticity observed in nanoscale eutectic binary systems can also explain the strength-ductility relationship in multi-component eutectics and homogeneously distributed plastic flow with increasing microstructural heterogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulating low-energy neutrino interactions with MARLEY

Monte Carlo event generators are a critical tool for the interpretation of data obtained by neutrino experiments. Several modern event generators are available which are well-suited to the GeV energy scale used in studies of accelerator neutrinos. However, theoretical modeling differences make their immediate application to lower energies difficult. In this paper, I present a new event generator, MARLEY, which is designed to better address the simulation needs of the low-energy (tens of MeV and below) neutrino community. The code is written in C++14 with an optional interface to the popular ROOT data analysis framework. The current release of MARLEY (version 1.2.0) emphasizes simulations of the reaction 40 Ar ( ν e , e − ) 40 K ⁎ but is extensible to other channels with suitable user input. This paper provides detailed documentation of MARLEY's implementation and usage, including guidance on how generated events may be analyzed and how MARLEY may be interfaced with external codes such as Geant4. Further information about MARLEY is available on the official website at http://www.marleygen.org. Program title:MARLEY 1.2.0 CPC Library link to program files:https://doi.org/10.17632/4v7zxnc8j3.1 Developer's respository link:http://github.com/MARLEY-MC/marley Code Ocean capsule:https://codeocean.com/capsule/9868179 Licensing provisions: GNU General Public License 3.0 Programming language: C++14 External routines/libraries used: GNU Scientific Library [1,2] (required), ROOT [3,4] (optional) Nature of problem: Simulation of neutrino-nucleus scattering events at energies of tens-of-MeV and below Solution method: Initial two-to-two scattering kinematics are sampled using the allowed approximation differential cross section and tables of precomputed nuclear matrix elements. Subsequent de-excitations of the remnant nucleus are simulated using a Monte Carlo implementation of the Hauser-Feshbach statistical model and tabulated γ-ray decay schemes for discrete nuclear levels. Additional comments including restrictions and unusual features: Input data are provided with the code that are suitable for producing simulations of the charged-current reaction 40 Ar ( ν e , e − ) 40 K ⁎ , coherent elastic neutrino-nucleus scattering on spin-zero target nuclei, and neutrino-electron elastic scattering on any atomic target. Preparation of new reaction input files (whose format is documented in Appendix B) would enable other reaction channels and nuclear targets to be handled by the existing code framework. Although there is no maximum neutrino energy enforced by the code itself, realistic neutrino-nucleus scattering events may be generated up to roughly 50 MeV. Above this energy, the effects of forbidden nuclear transitions, which are neglected in the current treatment of the cross sections (see section 2.1), become increasingly important.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Mutual information prediction for strongly correlated systems

We have trained a new machine-learning (ML) model which predicts mutual information (MI) for strongly correlated systems. This is a complex quantity, which is much more difficult to predict than one-site entropies, but carries important information about the correlation structure inside electronic systems. In this work, we replaced the expensive density matrix renormalization group (DMRG) calculations by newly trained ML model for prediction of the mutual information. We show the performance of the model on two important tasks: (a) to determine the correlation structure and (b) to determine ordering of orbitals for accurate DMRG calculations. In conclusion, the results are compared with the MI obtained from accurate DMRG calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Genetic determinants of switchgrass-root-associated microbiota in field sites spanning its natural range

A fundamental goal in plant microbiome research is to determine the relative impacts of host and environmental effects on root microbiota composition, particularly how host genotype impacts bacterial community composition. Most studies characterizing the effect of plant genotype on root microbiota undersample host genetic diversity and grow plants outside of their native ranges, making the associations between host and microbes difficult to interpret. Here, we characterized the root microbiota of a large diversity panel of switchgrass, a North American native C4 bioenergy crop, in three field locations spanning its native range. Our data, composed of 1,961 samples, suggest that field location is the primary determinant of microbiome composition; however, substantial heritable variation is widespread across bacterial taxa, especially those in the Sphingomonadaceae family. Despite diverse compositions, relatively few highly prevalent taxa make up the majority of the switchgrass root microbiota, a large fraction of which is shared across sites. Local genotypes preferentially recruit/filter for local microbes, supporting the idea of affinity between local plants and their microbiota. Using genome-wide association, we identified loci impacting the abundance of >400 microbial strains and found an enrichment of genes involved in immune responses, signaling pathways, and secondary metabolism. We found loci associated with over half of the core microbiota (i.e., microbes in >80% of samples), regardless of field location. Finally, we show a genetic relationship between a basal plant immunity pathway and relative abundances of root microbiota. Furthermore, this study brings us closer to harnessing and manipulating beneficial microbial associations via host genetics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mineral Recovery Enhanced Desalination (MRED) process: An innovative technology for desalinating hard brackish water

Desalination of water containing high concentrations of calcium, dissolved carbon dioxide, sulfate, silica and other sparingly soluble salts is difficult because of the scaling potential. An innovative pre-treatment scheme was investigated at bench scale that selectively removes these constituents and produces a soft water to enable desalination with high feed water recovery. The process first removes dissolved carbon dioxide by air stripping at low pH. Magnesium is removed by precipitation at high pH. Calcium is removed by ion exchange (IX), and sulfate is removed by nanofiltration (NF). Cation regenerant from IX, containing calcium, and concentrate from NF, containing sulfate, is combined to precipitate gypsum. Concentrate from the desalination process consisting of a concentrated NaCl solution is used to regenerate the IX resins. The selective precipitation, IX, and NF processes were tested in laboratory experiments and produced magnesium hydroxide and gypsum at greater than 90% and 95% purity respectively. A process model was developed to calculate process performance, mass and liquid flow rates. Finally, the MRED process offers the following benefits: 1) greater recovery of brackish feedwater by a desalination process, 2) recovery of marketable commodities and 3) reduction in the volume and mass of waste products from the treatment process.

42 ENGINEERING↗
DOE OSTI2021

Empirical prediction of saline water atomization pressure loss and spray phase change using local flow pressure analysis

A pressure analysis technique was developed to obtain spray spatial evaporation profile in a thermal desalination process. The technique can replace temperature-based evaporation measurement methods that are challenged by liquid-phase interference. It also provides an alternative to optical methods with high cost and complexity. Fundamental analysis was developed to determine local humidity ratios from changes in local static and dynamic pressures. The analysis was applied to data obtained from an external mixing air-assist atomizer. An empirical model was developed for the pressure loss in atomization process and was used in the analysis of humidity ratio. The results were used to develop empirical correlations for local humidity ratio in spray mediums using inlet conditions. Utilizing inlet conditions to predict evaporation profiles is an important contribution as it eliminates the need to take local spray measurements. Obtaining local data is often difficult and expensive; Our method and correlation circumvent the need for that. Finally, the correlations presented in this paper apply to saline water sprays with total dissolved salt of 0-10 wt%. The salinity-specific correlations are accurate to within ±10% of experimental data. The individual models were aggregated into a unified correlation for salinities within 0-10 wt%. The unified correlation is accurate to within ±18% of experimental data.

42 ENGINEERING↗
DOE OSTI2022

Comparison of vapour pressure osmometry, freezing point osmometry and direct membrane osmometry for determining the osmotic pressure of concentrated solutions

Osmometry is the analytical method of measuring osmotic pressure ($π$) and related colligative properties. Accurately measuring $π$ is essential for applications that use osmosis, including drug delivery systems and water filtration methods such as forward osmosis. The three most common forms of directly or indirectly measuring $π$ are vapour pressure osmometry (VPO), freezing point osmometry (FPO), and direct membrane osmometry (DMO). In this report, the performance of VPO, FPO, and DMO are compared for various aqueous solutions, including small molecule and polymer solutes, and potentially more difficult samples such as concentrated, carbonated, or viscous solutions. Furthermore, potential problems with each of the three methods are examined. Good practices can avoid inaccuracies in DMO due to external concentration polarization and solute diffusion through the membrane. Similarly, it is important to be aware of and avoid weaknesses in VPO and FPO in order to obtain reliable results. Even with care, however, the three methods still give different values of $π$ for concentrated aqueous solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Advancements in conventional and 3D printed feed spacers in membrane modules

Plate & frame and spiral-wound modules are used for gas separation, pervaporation, reverse osmosis, nanofiltration, ultrafiltration, microfiltration, electro-dialysis, electro-deionization, membrane distillation and forward osmosis membrane processes. Feed channel spacers are an integral part of both module types – providing mechanical support for a cross-flow channel through the module and, in most cases, promoting mixing to enhance mass transfer to reduce concentration polarization and fouling. However, enhanced mass transfer comes at a cost of increased hydraulic pressure losses and stagnant zones wherever a spacer filament touches a membrane surface. Further, these stagnant zones exacerbate membrane fouling and make cleaning more difficult. Efforts to improve feed spacer performance largely focus on adjusting the chemistry or geometry of the spacer to mitigate these challenges. Additive manufacturing (i.e. 3D printing) offers new degrees of freedom in feed spacer design and production, which opens up a new area of research in membrane technology. This review critically assesses the peer-reviewed literature on conventional net- or mesh-style feed spacers in addition to various novel spacer geometries and chemistries produced via 3D printing. We further review and evaluate conventional spacer manufacturing methods and discuss advantages and disadvantages of 3D printed spacers.

3D printing↗