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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Capillary barriers for staged loading of microfluidic devices

Various aspects of the present disclosure are directed toward methods and apparatuses for interacting a first liquid and a second liquid in one or more fluidic channels of a capillary structure. The methods and apparatuses can include providing at least one capillary barrier that positions a meniscus of the first liquid at a fluid-interface region using capillary forces within the capillary structure. Additionally, a path is provided along one of the channels for the second liquid to flow toward the fluid-interface region. Additionally, gas pressure is released, via a gas-outflow port, from the fluid-interface region while flow of the first liquid is arrested. Further, the first liquid and the second liquid contact in the fluid-interface region with the capillary barrier holding the first liquid at the fluid-interface region.

Santiago, Juan G.↗

NASA NTRS2024

Shape Servoing of Deformable Objects Using Model Estimation and Barrier Lyapunov Function

An adaptive shape servoing control method is presented in this article to manipulate a deformable object into a desired shape in 3-D. A finite-point-based representation of the deformable object is used and the deformation Jacobian matrix is approximated using Fourier series basis functions. The unknown parameters of the deformation Jacobian are learned by using the velocity applied to a control point on the object and corresponding change of positions of the points describing the entire object. An integral concurrent learning (ICL)-based parameter update law is designed along with a constrained controller to satisfy the state constraints on the motion of the control point using Barrier Lyapunov function analysis. ICL-based parameter update law uses data history of velocity and corresponding positions of the points along with their current values. An efficient algorithm to update the history stack using singular value maximization is proposed based on the structure of the regressor matrix. Simulations using a physical simulator and experiments using a robot platform are performed to validate the performance of the proposed controller on two different deformable objects.

Vrithik Raj Guthikonda↗

NASA NTRS2002

Needs and Opportunities for Uncertainty-Based Multidisciplinary Design Methods for Aerospace Vehicles

This report consists of a survey of the state of the art in uncertainty-based design together with recommendations for a Base research activity in this area for the NASA Langley Research Center. This report identifies the needs and opportunities for computational and experimental methods that provide accurate, efficient solutions to nondeterministic multidisciplinary aerospace vehicle design problems. Barriers to the adoption of uncertainty-based design methods are identified. and the benefits of the use of such methods are explained. Particular research needs are listed.

Zang, Thomas A.↗

NASA NTRS2013

Absolute Thickness Measurements on Coatings Without Prior Knowledge of Material Properties Using Terahertz Energy

This study investigates the applicability of a novel noncontact single-sided terahertz electromagnetic measurement method for measuring thickness in dielectric coating systems having either dielectric or conductive substrate materials. The method does not require knowledge of the velocity of terahertz waves in the coating material. The dielectric coatings ranged from approximately 300 to 1400 m in thickness. First, the terahertz method was validated on a bulk dielectric sample to determine its ability to precisely measure thickness and density variation. Then, the method was studied on simulated coating systems. One simulated coating consisted of layered thin paper samples of varying thicknesses on a ceramic substrate. Another simulated coating system consisted of adhesive-backed Teflon adhered to conducting and dielectric substrates. Alumina samples that were coated with a ceramic adhesive layer were also investigated. Finally, the method was studied for thickness measurement of actual thermal barrier coatings (TBC) on ceramic substrates. The unique aspects and limitations of this method for thickness measurements are discussed.

Nondestructive evaluation↗

NASA NTRS2021

Safe Tracking Control of an Uncertain Euler-Lagrange System with Full-State Constraints using Barrier Functions

This paper presents a novel, safe tracking control design method that learns the parameters of an uncertain Euler-Lagrange (EL) system online using adaptive learning laws. A barrier function (BF) is first used to transform the full-state constrained EL-dynamics into an equivalent unconstrained dynamics. An adaptive tracking controller is then developed along with the parameter update law in the transformed state space such that the states remain bounded for all time within a prescribed bound. A stability analysis is developed that considers the EL-dynamics’ uncertainty, yielding a semi-globally uniformly ultimately bounded (SGUUB) tracking error and the parameter estimation error. The controller design is validated in simulations using a two-link planar manipulator. The results show the proposed method’s ability to track the reference trajectory while remaining inside each of the predefined state bounds.

Robots↗

DOE OSTI2021

Combined Dimensional and Topology Optimization of Interior Permanent Magnet Synchronous Machine Rotors Using a Permanent Magnet Function Interpolation Method

This paper presents a magneto-structural combined dimensional and topology optimization technique for interior permanent magnet synchronous machine (IPMSM) rotors. Dimensional changes to the permanent magnet (PM) location or size are accomplished by interpolating or projecting a smoothed Heaviside rectangular function representing the presence of PM material onto the IPMSM rotor design domain mesh. A density based Solid Isotropic with Material Penalization (SIMP) topology optimization approach is then used to vary the presence of electrical steel in mesh elements to form flux barriers around the PM. The proposed method enforces a defined shape for the PM without requiring the mesh in the design domain to be deformed. Three examples are presented to demonstrate the technique: two flat bar IPMSM and one V-shaped IPMSM.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Microgrids in Emerging Markets - Private Sector Perspectives

This quick read assesses the barriers and opportunities for private sector entry into microgrid development. Information presented in this paper is based on qualitative and quantitative data collection and analysis methods to provide an empirical understanding of barriers to private sector clean energy investment in emerging markets.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Combined Dimensional and Topology Optimization of Synchronous Machine Rotors Using a Material Density Interpolation Method

This article presents a magneto-structural combined dimensional and topology optimization technique for interior permanent magnet synchronous machine (IPMSM) and wound field synchronous machine (WFSM) rotors. Dimensional changes to the permanent magnet (PM) and rotor winding location or size are accomplished by interpolating or projecting a smoothed Heaviside rectangular function representing the presence of PM material onto the IPMSM rotor design domain mesh, whereas the presence of copper material for WFSM rotor is interpolated in the same manner. A density based Solid Isotropic Material with Penalization (SIMP) topology optimization approach is then used to vary the presence of electrical steel in mesh elements to form flux barriers around the PM. The proposed method enforces a defined shape for the PM without requiring the mesh in the design domain to be deformed. Four examples are presented to demonstrate the technique: Two flat bar IPMSM, one V-shaped IPMSM, and a WFSM. Here, a comparative study is performed on one of the flat bar IPMSMs using Metamodel of Optimal Prognosis (MOP) based method.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1987

Probabilistic load model development and validation for composite load spectra for select space propulsion engines

A major task of the program to develop an expert system to predict the loads on selected components of a generic space propulsion engine is the design development and application of a probabilitic loads model. This model is being developed in order to account for the random nature of the loads and assess the variable load ranges' effect on the engine performance. A probabilistic model has been developed. The model is based primarily on simulation methods, but also has a Gaussian algebra method (if all variables are near normal), a fast probability integrator routine (for the calculation of low probability events), and a separate, stand alone program for performing barrier crossing calculations. Each of these probabilistic methods has been verified with theoretical calculations using assumed distributional forms.

Kurth, R.↗

DOE OSTI2025

Bubbling Water–Treating DBD Plasma Device Optimization Using Experimental and Computational Methods

A dry air atmospheric pressure volume dielectric barrier discharge is employed to fix nitrogen in water. Producing nitrate for use as nitrogen fertilizer is the primary motivation. A 0D chemistry model is developed and informed by the electrical, and geometric characteristics of the device and the plasma gas temperature. Modeled ozone and nitrate densities are compared to those measured experimentally in the plasma effluent and treated liquid for a range of gas temperatures. Modeled and measured ozone densities are in good agreement; however, the model lacks the liquid chemistry to properly represent the measured nitrate density. A gas temperature-based shift from ozone to NO x producing regimes is observed in both experiment and model, and the reactions responsible are evaluated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2019

Rapid Prediction of Installed Jet Noise from RANS

A new method of computing jet noise, called mSrc, was developed on the general principles of acoustic analogies. In the method, the problem of translating turbulent flow energy into acoustic energy at a far-field observer is broken into two parts, a calculation of acoustic source strengths and then their propagation. The acoustic sources are related to turbulent quantities in the jet plume in a robust manner. The propagation, which is more properly computed using a Green's function accounting for nonuniform speed of sound and solid surfaces, is instead modeled using commonly observed features of jet acoustic far field directivity, and by diffraction barrier theory for surfaces. The mSrc method does not require gradients of the predicted flow field, allowing it to make use of robust unstructured RANS CFD methods, including embedded boundary codes. Such codes do not require specification of surface meshes, and auto-refine their grid to resolve flow gradients, putting resolution where it is required without a priori user input. The ability of mSrc to use such radically unstructured flow input results in an efficient method of estimating noise from jet flows from complex nozzles installed on aircraft. Many validation cases are presented to demonstrate the accuracy and range of applicability of the mSrc method for representative jet noise applications.

Bridges, James↗

NASA NTRS2019

Rapid Prediction of Installed Jet Noise From RANS

Bridges, James↗

DOE OSTI2025

Energy Barriers for H 2 Uptake and Release by Defects in Boron Nitride

Density functional theory simulations (DFT) have been used in combination with the climbing image nudged elastic band method (CI-NEB) to determine the energy barriers required to dissociate H 2 at four types of h-BN defects and evaluate the potential for reacted H species to hop to adjacent available sites. The defects were selected based on previous work in which ab initio thermodynamics was used to identify specific defects with optimal thermodynamics for reversible H 2 storage, that is, the defects would dissociate molecular H 2 while not binding H species too strongly so that hydrogen release is favorable. The four defects investigated are the N and B monovacancies, which only offer either B or N defective sites respectively, as well as larger cluster vacancy defects named 3V(1B2N) and hexagonal 6V(3B3N), which present a mixture of B and N defect sites available for H binding. While homogeneous H 2 dissociation by B and N monovacancies have energy barriers larger than 0.4 eV, we found that mixed BN terminated defects have energy barrier lower than 0.4 eV for H 2 dissociation. In particular, H 2 dissociation by the hexagonal 6V(3B3N) defect, exclusively made of Frustrated Lewis Pairs (FLPs), is almost barrierless. The energy barrier for subsequent H hopping to available adjacent B or N sites of the defect were also calculated. It was found that the hopping of H species generally involves high energy barriers ranging from 0.26 eV to 2.57 eV. In conclusion, this work completes previous thermodynamic theoretical investigations and provides a more comprehensive picture of H 2 reaction with defective h-BN for potential H 2 storage and recovery.

Sassi, Michel J. [Pacific Northwest National Labor↗

NASA NTRS1991

Lateral tunneling through voltage-controlled barriers

The paper reports on a detailed experimental investigation of lateral tunneling between electrodes of a two-dimensional electron gas separated by the voltage-controlled barrier of a nanometer Schottky gate. The experimental data are modeled using the WKB method to calculate the tunneling probability of electrons through a barrier whose shape is determined from a solution of the two-dimensional Poisson equation. This model is in excellent agreement with the experimental data over a two order of magnitude range of current.

Manion, S. J.↗

DOE OSTI2025

Optimized NiTe 2 /Ni back contacts via chemical deposition for high performance CdTe solar cells

The development of efficient and stable back contacts remains a major challenge in achieving high performance and long-term stability of CdTe thin-film solar cells. Here, this work revisits the formation of NiTe 2 by chemical bath deposition (CBD) as a back contact for CdTe devices. An optimized CBD recipe, based on high-purity precursors and the addition of copper chloride directly into the bath, was developed and applied to fabricate Cu-doped CdTe solar cells. A modified Cu-free methodology was also applied to Group V doped absorbers. The process included pinhole filling, ion milling, CBD, annealing, and sputtering to form a low-barrier back contact. Devices fabricated using this method achieved consistent open-circuit voltages (V oc ) above 800 mV and fill factors (FF) exceeding 70%. The best Cu-doped devices reached power conversion efficiencies (PCE) above 18 %, and preliminary results with Group V-doped material demonstrated compatibility of the method with high-efficiency, state-of-the-art CdTe devices. This study shows that NiTe 2 /Ni back contacts, formed via an optimized chemical process followed by sputtering of Ni, represent a promising pathway for achieving low-barrier and potentially stable back contacts in modern CdTe photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2022

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗

DOE OSTI2021

Molecular coupling competing with defects within insulator of the magnetic tunnel junction-based molecular spintronics devices

Nearly 70 years old dream of incorporating molecule as the device element is still challenged by competing defects in almost every experimentally tested molecular device approach. This paper focuses on the magnetic tunnel junction (MTJ) based molecular spintronics device (MTJMSD) method. An MTJMSD utilizes a tunnel barrier to ensure a robust and mass-producible physical gap between two ferromagnetic electrodes. MTJMSD approach may benefit from MTJ's industrial practices; however, the MTJMSD approach still needs to overcome additional challenges arising from the inclusion of magnetic molecules in conjunction with competing defects. Molecular device channels are covalently bonded between two ferromagnets across the insulating barrier. An insulating barrier may possess a variety of potential defects arising during the fabrication or operational phase. This paper describes an experimental and theoretical study of molecular coupling between ferromagnets in the presence of the competing coupling via an insulating tunnel barrier. We discuss the experimental observations of hillocks and pinhole-type defects producing inter-layer coupling that compete with molecular device elements. We performed theoretical simulations to encompass a wide range of competition between molecules and defects. Monte Carlo Simulation (MCS) was used for investigating the defect-induced inter-layer coupling on MTJMSD. Our research may help understand and design molecular spintronics devices utilizing various insulating spacers such as aluminum oxide (AlOx) and magnesium oxide (MgO) on a wide range of metal electrodes. This paper intends to provide practical insights for researchers intending to investigate the molecular device properties via the MTJMSD approach and do not have a background in magnetic tunnel junction fabrication.

36 MATERIALS SCIENCE↗

DOE OSTI2024

From Text to Maps: LLM-Driven Extraction and Geotagging of Epidemiological Data

Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.

Harrod, Karly↗