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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗
DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Calibration of parallel bond parameters in bonded particle models via physics-informed adaptive moment optimisation

This study proposes an automated calibration procedure for bond parameters in bonded discrete element modelling. By exploring the underlying physical correlations between microscopic parameters of bonds and macroscopic strength parameters of the continuum to be modelled, the microscopic shear strength and tensile strength are identified as independent variables for calibration purpose. Then a physics-informed iterative scheme is proposed to automatically approximate the bond parameters by viewing the micro-macro relation as an implicitly defined mathematical mapping function. As a result of highly non-convex features of this implicit mapping, the adaptive moment estimation (Adam), which is especially suitable for problems with noisy gradients, is adopted as the basic iterative scheme, in conjunction with other numerical techniques to approximately evaluate the partial derivatives involved. The whole procedure offers a simple and effective framework for bond parameter calibration. Finally, a numerical example of SiC ceramic is provided for validation. By compared with some existing calibration methods, the proposed method shows significant advantages in terms of calibration efficiency and accuracy.

42 ENGINEERING↗
DOE OSTI2024

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic ( Allium ampeloprasum L.) by liquid chromatography tandem mass spectrometry

Abstract Introduction Giant garlic is a functional food that contains different kinds of bioactive molecules with beneficial effects on chronic noncommunicable diseases like diabetes and cardiovascular conditions. Considering biosynthesis pathways, abundance, and biological activity, alliin and S‐allyl‐cysteine were used as chemical markers of organosulphur compounds present in giant garlic. Objective To establish a chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic by liquid chromatography tandem mass spectrometry (LC–MS/MS). Methodology Samples were blanched (ca. 90°C for 10 min) to inactivate alliinase and γ‐glutamyl transpeptidase enzymes and then freeze‐dried. Chemometric optimisation was performed via response surface methodology based on central composite design (CCD). Organosulphur compound yields were determined applying a validated LC–MS/MS method in multiple reaction monitoring (MRM) mode using the following transitions: for alliin m/z 178 → 74 and for S‐allyl‐cysteine m/z 162 → 41. Results According to CCD results, under constant conditions of pressure (1500 psi) and time (20 min), the optimal conditions for pressurised liquid extraction of alliin and S‐allyl‐cysteine were 70.75 and 68.97% v/v of ethanol in extraction solvent and 76.45 and 98.88°C as extraction temperature, respectively. Multiple response optimisation for the simultaneous extraction of both organosulphur compounds was established via desirability function. Under these conditions, 2.70 ± 0.27 mg g −1 dry weight (DW) of alliin and 2.79 ± 0.22 mg g −1 DW of S‐allyl‐cysteine were extracted. Conclusions These results clearly demonstrated that pressurised liquid extraction is an efficient green technique to extract bioactive organosulphur compounds from giant garlic. Extraction yields were significantly ( p < 0.05) higher than those obtained with conventional ultra‐turrax extraction.

Peterssen‐Fonseca, Darlene↗
DOE OSTI2025

Amorphous electrocatalysts for oxygen and hydrogen evolution reactions: Advances in hydrogen production

The electrochemical splitting of water into oxygen and hydrogen is fundamental for renewable energy storage and conversion. The development of cost-effective and highly efficient electrocatalysts remains essential for industrial-scale implementation of this technology. Recent advances have highlighted the superior activity, stability and structural adaptability of amorphous electrocatalysts compared to their crystalline counterparts. This review critically examines synthesis strategies, characterisation techniques, and the electrochemical performance of amorphous materials for both oxygen evolution (OER) and hydrogen evolution (HER) reactions. Key factors influencing catalytic efficiency, including electronic structure and surface chemistry, are discussed in detail and contextualised with established literature. The review also highlights the critical role of enthalpic contributions in governing reaction energetics and catalyst performance, which aids in understanding and optimising electrocatalytic efficiency. Notably, ongoing research continues to reveal that amorphous catalysts consistently deliver improved performance in water-splitting applications, highlighting their growing relevance in electrocatalysis. The rationale for employing amorphous catalysts in water splitting is articulated, emphasising their unique advantages. By integrating recent findings and outlining future research directions, this review underscores the pivotal role of amorphous materials in advancing sustainable hydrogen production and identifies promising avenues for catalyst innovation.

Amorphous catalysts↗
DOE OSTI2022

Punzi-loss: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗
DOE OSTI2021

Residential Demand Side Aggregation of Privacy-Conscious Consumers

The increasing adoption of smart meters has led to growing concerns regarding privacy risks stemming from the high resolution measurements. This has given rise to privacy protection techniques that physically alter the consumer's energy load profile, masking private information by using localised devices, e.g. batteries or flexible loads. Meanwhile, there has also been increasing interest in aggregating the distributed energy resources (DERs) of residential consumers to provide services to the grid. In this paper, we propose an online distributed algorithm to aggregate the DERs of privacy-conscious consumers to provide services to the grid, whilst preserving their privacy. Results show that the optimisation solution from the distributed method converges to one close to the optimum computed using an ideal centralised solution method, balancing between grid service provision, consumer preferences and privacy protection. More importantly, the distributed method preserves consumer privacy, and does not require high-bandwidth two-way communications infrastructure.

ancillary services↗
DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗
DOE OSTI2023

Friction stir welding of thick 7175 aluminium alloy plates for deepwater riser applications

To substantially reduce the cost of extracting petroleum from ultra-deepwater sources, manufacturing risers from 7175 aluminium (Al) alloys rather than steels is being considered. Friction stir welding (FSW), a solid-phase joining technique, is widely applied to 7XXX Al alloys because its mechanical properties are retained within the nugget zone. And, the primary technical objective of the current study is the development of high-strength, corrosion-resistant weldments that connect 7175 Al riser flanges and pipes. However, as the welding thickness increases, the welding speed can be greatly impeded, which lowers the manufacturing efficiency and weakens the mechanical properties in the heat affected zone (next to the nugget). Therefore, obtaining high-performance thick plate 7175 Al alloy joints via FSW is challenging. To overcome this dilemma, the current work optimises the tool design and welding parameters for achieving high-performance joints between 25.4 mm thick 7175 Al alloy plates in butt joint configuration.

Wang, Tianhao↗
DOE OSTI2020

Flat-histogram extrapolation as a useful tool in the age of big data

In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

How to avoid multiple scattering in strongly scattering SANS and USANS samples

Small Angle Neutron Scattering (SANS) and Ultra Small Angle Neutron Scattering (USANS) are the only available experimental techniques to provide seamless non-destructive measurements of the geometry of the accessible and inaccessible pore structure of rocks from sub-nanopore size to the scale of macropores. They have therefore become the measurement of choice for tight reservoir rocks such as organic rich shales. A simplifying assumption in the analysis is, however, that during the path of neutrons through the sample each neutron is only scattered once. Shales are samples with a high scattering power and Multiple Scattering (MS) may occur which requires special modelling for deconvolution of the results. The approach to avoid MS is to simply reduce the sample thickness to <0.15–0.5 mm. Here, in this work, we present a systematic method on wavelength selection and preparation of samples to optimise extraction of microstructural data and minimise parasitic errors. Experimentally measured SAS transmission (TSAS) values are used as a practical criterion for estimation of the extent of MS. Generous beamtime allocations allowed robust testing revealing that sample thicknesses can be twice as thick as predicted using the standard protocol. Analysing thicker samples is particularly beneficial for statistically relevant characterisation of heterogeneous samples making the new protocol the method of choice for such samples.

(U)SANS↗
DOE OSTI2021

Ion temperature clamping in Wendelstein 7-X electron cyclotron heated plasmas

The neoclassical transport optimization of the Wendelstein 7-X stellarator has not resulted in the predicted high energy confinement of gas fueled electron-cyclotron-resonance-heated (ECRH) plasmas as modelled in (Turkin et al 2011 Phys. Plasmas 18 022505) due to high levels of turbulent heat transport observed in the experiments. The electron-turbulent-heat transport appears non-stiff and is of the electron temperature gradient (ETG)/ion temperature gradient (ITG) type (Weir et al 2021 Nucl. Fusion 61 056001). As a result, the electron temperature T e can be varied freely from 1 keV–10 keV within the range of P ECRH = 1–7 MW, with electron density n e values from 0.1–1.5 × 10 20 m –3 . By contrast, in combination with the broad electron-to-ion energy-exchange heating profile in ECRH plasmas, ion-turbulent-heat transport leads to clamping of the central ion temperature at T i ~ 1.5 keV ± 0.2 keV. In a dedicated ECRH power scan at a constant density of $\langle n_{e} \rangle$ = 7 × 10 19 m –3 , an apparent 'negative ion temperature profile stiffness' was found in the central plasma for (r/a < 0.5), in which the normalized gradient ∇T i /T i decreases with increasing ion heat flux. The experiment was conducted in helium, which has a higher radiative density limit compared to hydrogen, allowing a broader power scan. This 'negative stiffness' is due to a strong exacerbation of turbulent transport with an increasing ratio of T e /T i in this electron-heated plasma. This finding is consistent with electrostatic microinstabilities, such as ITG-driven turbulence. Theoretical calculations made by both linear and nonlinear gyro-kinetic simulations performed by the GENE code in the W7-X three-dimensional geometry show a strong enhancement of turbulence with an increasing ratio of T e /T i . The exacerbation of turbulence with increasing T e /T i is also found in tokamaks and inherently enhances ion heat transport in electron-heated plasmas. This finding strongly affects the prospects of future high-performance gas-fueled ECRH scenarios in W7-X and imposes a requirement for turbulence-suppression techniques.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Mixed integer linear programming‐based distributed energy management for networked microgrids considering network operational objectives and constraints

Abstract Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Development of arsenic doped Cd(Se,Te) absorbers by MOCVD for thin film solar cells

Recent developments in CdTe solar cell technology have included the incorporation of ternary alloy Cd(Se,Te) in the devices. CdTe absorber band gap grading due to Se alloying contributes to current density enhancement and can result in device performance improvement. Here we report Cd(Se,Te) polycrystalline thin films grown by a chamberless inline atmospheric pressure metal organic chemical vapour deposition technique, with subsequent incorporation in CdTe solar cells. The compositional dependence of the crystal structure and optical properties of Cd(Se,Te) are examined. Selenium graded Cd(Se,Te)/CdTe absorber structure in devices are demonstrated using either a single CdSe layer or CdSe/Cd(Se,Te) bilayer (with or without As doping in the Cd(Se,Te) layer). Cross-sectional TEM/EDS, photoluminescence spectra and secondary ion mass spectroscopy analysis confirmed the formation of a graded Se profile toward the back contact with a diffusion length of ~1.5 um and revealed back-diffusion of Group V (As) dopants from the CdTe layer into Cd(Se,Te) grains. Due to the strong Se/Te interdiffusion, CdSe in the Se bilayer configuration was unable to form an n-type emitter layer in processed devices. In situ As doping of the Cd(Se,Te) layer benefited the device junction quality with current density reaching 28.3 mA/cm 2 . The results provide useful insights for the optimisation of Cd(Se,Te)/CdTe solar cells.

14 SOLAR ENERGY↗
DOE OSTI2022

Tailoring Fibre Structure Enabled by X-ray Analytics for Targeted Biomedical Applications

The rising interest in designing fibres via spinning techniques combining the properties of various polymeric materials into advanced functionalised materials is directed towards targeted biomedical applications such as drug delivery, wearable sensors or tissue engineering. Understanding how these functional polymers exhibit multiscale structures ranging from the molecular level to nano-, micro-and millimetre scale is a key prerequisite for their challenging applications that can be addressed by a non-destructive X-ray based analytical approach. X-ray multimodalities combining X-ray imaging, scattering and diffraction allow the study of morphology, molecular structure, and the analysis of nano-domain size and shape, crystallinity and preferential orientation in 3D arrangements. The incorporation of X-ray analytics in the design process of polymeric fibers via their nanostructure under non-ambient conditions (i.e. temperature, mechanical load, humidity…) allows for efficient optimization of the fabrication process as well as quality control along the product lifetime under operating environmental conditions. Here, we demonstrate the successful collaboration between the laboratory of Biomimetic Textiles and Membranes and the Center of X-ray Analytics at Empa for the design, characterisation and optimisation of advanced functionalised polymeric fibrous material systems.

60 APPLIED LIFE SCIENCES↗