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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Automated experimental design of safe rampdowns via probabilistic machine learning

Abstract Typically the rampdown phase of a shot consists of a decrease in current and injected power and optionally a change in shape, but there is considerable flexibility in the rate, sequencing, and duration of these changes. On the next generation of tokamaks it is essential that this is done safely as the device could be damaged by the stored thermal and electromagnetic energy present in the plasma. This works presents a procedure for automatically choosing experimental rampdown designs to rapidly converge to an effective rampdown trajectory. This procedure uses probabilistic machine learning methods paired with acquisition functions taken from Bayesian optimization. In a set of 2022 experiments at DIII-D, the rampdown designs produced by our method maintained plasma control down to substantially lower current and energy levels than are typically observed. The actions predicted by the model significantly improved as the model was able to explore over the course of the experimental campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2020

Verification and Validation of MFiX-PIC

This work is aimed at verification and validation (V&V) of particle-in-cell (PIC) approach in the National Energy Technology Laboratory’s (NETL) Multiphase Flow with Interphase eXchanges (MFiX-PIC) which was officially released in April 2019. Under verification, the results from MFiX-PIC are compared with exact or analytical solutions, while experimental results are used for benchmarking under validation. The test suite consists of the following cases: Terminal velocity. Particle advection in time-varying flow field. Particle settling in a dense medium. Evaporation. Rayleigh-Taylor instability. Minimum fluidization. Mini-circulating fluidized bed. The cases reported in a format consistent with the existing Verification and Validation Manual (Musser et al., 2018) explore a wide range of flow velocities and concentration regimes. Only one case considered in this work verifies the implementation of heat and mass transfer routines in MFiX-PIC, while a few other studies are currently underway to further expand the validation space in reacting flows. This study highlights the need for a rigorous analysis to objectively quantify the effect of model parameters. In addition, there is a scarcity of high-quality experimental data in the literature suitable for validating PIC methodology. Future efforts would include performing experiments which provide results for a valuable database. While designing such experiments, particle count should be significant to have sufficient particles per parcel while maintaining an optimal parcel to cell volume ratio. Also, datasets should have objectively assessed experimental uncertainty whereby confidence interval in those measurements become more explicit.

42 ENGINEERING↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
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↗
DOE OSTI2020

Grand canonical inverse design of multicomponent colloidal crystals

Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. We present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.

36 MATERIALS SCIENCE↗
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 OSTI2020

Compound pulse characteristics of a heterogeneous composite scintillator in a gamma-ray field

An important aspect of radiation detection in scintillators is the time dependence of emitted fluorescence, which results in characteristic measured pulse shape. In some materials, a relatively strong dependence of the scintillation pulse shape on stopping power exists, which can provide reliable identification of the incident particle. An alternative method to realize particle identification is by combining materials with different fluorescence lifetimes into heterogeneous structures. Such composite scintillators derive their properties from both the constituent material characteristics and the geometry of the composite structure. In composite scintillators with a high degree of heterogeneous loading, a superposition of fluorescence contributions originating in multiple materials, which is observed as single waveform with an intermediate pulse shape, may affect its ability to reject rays. We measure the time dependence of light output from a scintillator composed of 6 Li-containing glass shards and scintillating polyvinyl toluene and identify events that exhibit such compound behavior when exposed to rays and fast neutrons. We develop a modeling and simulation framework that reproduces the pulse shapes in heterogeneous scintillators and use it study the effect that the weight percentage of 6 Li glass has on rejection. The modeling framework is applied to the experimentally studied scintillator, finding a good agreement. Here, the developed modeling and simulation approach will help optimize the design of heterogeneous scintillators to meet the desired trade-o between neutron capture efficiency and -ray rejection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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↗
DOE OSTI2021

2021 Q1 Project Report: Optimized Bifacial PV Systems (Q1 FY2021 Project Report)

This project has four main technical objectives: 1) Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. 2) Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

42 ENGINEERING↗
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.↗
NASA NTRS1981

Classical and modern control design of a speed-hold system for a STOL airplane

A speed-hold system for an experimental short takeoff and landing jet airplane has been designed using both classical root-locus and modern optimal control synthesis techniques. The purpose of the speed-hold system is to maintain airspeed during final approach in the presence of wind shears, gusts, engine failures, and pilot control inputs. Designs were based on using airspeed as a single measurement and two symmetrically deployed upper surface blown flaps as a single control. An optimal control law feeding back all the states and the integral of airspeed through constant gains provided superior performance in terms of speed tracking and control surface activity. However, when a constant-gain Kalman filter was inserted to estimate the states using only the measurement of airspeed, the performance of the optimal control law was reduced to the same as that of a much simpler classical proportional-path plus integral-path control law. To improve the performance of the optimal control law, additional measurements would be required.

Blight, J. D.↗
NASA NTRS1985

Evaluation of results of cell electrophoresis experiments on space shuttle STS-3 including pre-flight and post-flight laboratory experiments

The objectives of the red blood cell experiments were to provide a visual check on the electrophoretic process and especially electroosmotic flow in space as well as to provide test separations of non-degradable standard particles for comparison with the separations of the three viable cell types studied on the Apollo-Soyuz Test Project. Determination of the maximum concentrations of cells that can be separated in column electrophore was a significant goal. Two of the eight columns were available for red cell experiments, so two concentrations of human and rabbit RBC mixtures were used. The objectives of another experiment were to evaluate the reproducibility of microgravity electrophoretic separation of living kidney cells, to separate cells with highly viability despite two freeze-thaw cycles, and to optimize the physical conditions of cell separation. Owing to the uncertain heterogeneity of the starting material, the experimental design does not assess resolution in microgravity, but improved separability was sought in comparison to density-gradient electrophoresis or continuous-flow electrophoresis. Efforts were made to increase cell yield and cell viability and to assess reproducibility directly.

Todd, P. W.↗
NASA NTRS1991

Approaches for optimizing the first electronic hyperpolarizability of conjugated organic molecules

Conjugated organic molecules with electron-donating and -accepting moieties can exhibit large electronic second-order nonlinearities, or first hyperpolarizabilities, beta. The present two-state, four-orbital independent-electron analysis of beta leads to the prediction that its absolute value will be maximized at a combination of donor and acceptor strengths for a given conjugated bridge. Molecular design strategies for beta optimization are proposed which give attention to the energetic manipulations of the bridge states. Experimental results have been obtained which support the validity of this approach.

Marder, S. R.↗
NASA NTRS1992

Analysis and design technology for high-speed aircraft structures

Recent high-speed aircraft structures research activities at NASA Langley Research Center are described. The following topics are covered: the development of analytical and numerical solutions to global and local thermal and structural problems, experimental verification of analysis methods, identification of failure mechanisms, and the incorporation of analysis methods into design and optimization strategies. The paper describes recent NASA Langley advances in analysis and design methods, structural and thermal concepts, and test methods.

Starnes, James H., Jr.↗