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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Potential Early Markets for Fusion Energy

We examine potential early markets for fusion energy and their projected cost targets, based on analysis and synthesis of many relevant, recent studies and reports. Seeking to provide guidance to ambitious fusion developers aspiring to enable commercial deployment before 2040, we examine cost requirements for fusion-generated electricity, process heat, and hydrogen production based on today’s market prices but with various adjustments relating to possible scenarios in 2035, such as ‘‘business-as-usual,’’ high renewables penetration, and carbon pricing up to 100 $/tCO 2 . Key findings are that fusion developers should consider focusing initially on high-priced global electricity markets and consider including integrated thermal storage, depending on techno-economic factors, in order to maximize revenue and compete in markets with high renewables penetration. Process heat and hydrogen production will be tough early markets for fusion, but may open up to fusion as markets evolve and if fusion’s levelized cost of electricity falls below 50 $/MWh e . Finally, we discuss potential ways for a fusion plant to increase revenue via cogeneration (e.g., desalination, direct air capture, or district heating) and to lower capital costs (e.g., by minimizing construction times and interest or by retrofitting coal plants).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Toolpath considerations for 5-axis systems

There have been many studies that show how additive manufacturing (AM) has been able to improve the performance of a component, reduce manufacturing costs, or minimize lead times. As the adoption of AM significantly increased, so did the complexity of designs for AM. Many designs require advanced systems, and in response to this, there is an increasing number of systems now capable of additional degrees of motion. 5-axis systems, which include two rotary axes, are becoming more common in the additive manufacturing community. These systems are capable of depositing material on a nonplanar surface and lend themselves to many industrial applications, further extending the design space for AM. However, computer-aided manufacturing (CAM) for AM has been dominated by 3-axis slicing software that do not typically accomodate 5-axis motion. This chapter introduces 5-axis motion and presents the unique considerations that need to be accounted for when generating toolpath trajectories for 5-axis systems.

Feldhausen, Thomas↗
DOE OSTI2020

Parametrically designed surface topography on CAD models of additively manufactured lattice structures for improved design validation

Additively Manufactured (AM) lattice structures of the laser powder bed fusion (LPBF) process generally have different surface geometries depending on the overhang angle and location. This means that the design validation stage is often challenging, considering it is based on an ideal Computer Aided Design (CAD) model that is not truly representative, since the AM part will be different in terms of dimensional accuracy and surface finish. Previous studies have relied on the design of surface textures that are independent of the overhang angle, or techniques based on directly using X-ray Computed Tomography (XCT) data. In this paper, a new technique for designing surface texture on the CAD of lattices has been investigated and correlated with areal surface roughness parameters. After extracting areal surface parameters from the XCT data of the lattice, the method allowed for the design of this surface texture on the part CAD taking in consideration if its up skin or down skin, without using XCT data as the main input. By applying this method, it is possible to obtain a lattice CAD model with a designed surface texture and geometry that is more representative of the actual AM lattice. The mean deviation between the CAD model with the designed surface and the XCT was a third of the one between the XCT and the initial CAD. The proposed method allows for designing and replicating LPBF AM surfaces on the CAD of a lattice, taking into consideration the dimensional deviation caused by AM surfaces, especially on overhangs. Assuming that an LPBF AM process is stable and produces approximately the same AM surface, this method can be used to predict the geometry of a lattice, providing a cost-effective and efficient methodology that minimizes the time needed for design validation.

42 ENGINEERING↗
DOE OSTI2023

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of a liquid-helium free cryogenic sample holder with mK temperature control for autonomous electron microscopy

The automated and autonomous cryogenic transmission electron microscopy (Cryo-EM) demands a sample holder capable of maintaining temperatures below 10 K with precise control, long holding times, and minimal helium use. Rising to this challenge, we initiated an ambitious project to develop a novel closed-cycle cryocooler-based cryogenic sample holder that operates without the use of liquid helium and the consumption of gaseous helium. Here, this article presents the design, construction, and experimental testing of the initial prototype, which achieves an ultimate temperature of 5.6 K with exceptional stability close to 1mK, while providing a wide temperature control range from 295 K to 5.6 K, marking a clear advancement in cryo-EM holder development. While the prototype was not designed for atomic resolution imaging and thus lacks a sturdy support system to mitigate mechanical vibrations from the cryocooler's pulsed tube, this innovative approach successfully demonstrates proof of concept. It offers unprecedented capabilities for state-of-the-art cryogenic microscopy and microanalysis in materials and biological sciences.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Synergizing superwetting and architected electrodes for high-rate water splitting

Water splitting is one of the most promising technologies for generating green hydrogen. To meet industrial demand, it is essential to boost the operation current density to industrial levels, typically in the hundreds of mA cm -2 . However, operating at these high current densities presents significant challenges, with bubble formation being one of the most critical issues. Efficient bubble management is crucial as it directly impacts the performance and stability of the water splitting process. Superwetting electrodes, which can enhance aerophobicity, are particularly favorable for facilitating bubble detachment and transport. By reducing bubble contact time and minimizing the size of detached bubbles, these electrodes help prevent blockage and maintain high catalytic efficiency. Here, in this review, we aim to provide an overview of recent advancements in tackling bubble-related issues through the design and implementation of superwetting electrodes, including surface modification techniques and structural optimizations. We will also share our insights into the principles and mechanisms behind the design of superwetting electrodes, highlighting the key factors that influence their performance. Our review aims to guide future research directions and provides a solid foundation for developing more efficient and durable superwetting electrodes for high-rate water splitting.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring implosion designs for increased compression on the National Ignition Facility using high density carbon ablators

It has long been recognized that high compression, and hence good confinement, is essential to achieving high yields in inertial confinement fusion implosions. In pursuit of multi-megajoule yields on the National Ignition Facility (NIF), a new campaign has begun aimed at testing the hypothesis that controlling hydrodynamic stability is key to achieving effective higher compression with the high density carbon ablators currently fielded on NIF. This campaign is built around a new implosion design, called SQ-n, that is derived from the uniquely stable Bigfoot design tested on NIF in 2016–2019. While very stable and with performance that was quite close to one-dimensional expectations, Bigfoot was a relatively high adiabat, and consequently lower compression design. The goal of SQ-n is then to evolve Bigfoot toward a higher compression design but without compromising its unique stability characteristics. Specifically, SQ-n adopts a ramped foot pulse shape to minimize early time Richtmyer–Meshkov instability growth and uses an ablator dopant distribution extending all of the way to the fuel–ablator interface that simulations suggest further reduces perturbation growth. In conclusion, this paper describes the design philosophy pursued with SQ-n, the results of instability modeling of the candidate design, and the experimental campaign planned to test these ideas in the near future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Transition Core Analysis for HEU to LEU Fuel Conversion at the University of Missouri Research Reactor

A new type of low-enriched uranium (LEU) fuel based on an alloy of uranium and molybdenum is expected to allow the conversion of U.S. domestic high-performance research and test reactors requiring high density fuel from highly enriched uranium (HEU) to LEU. The University of Missouri Research Reactor (MURR (R)) has undergone design and performance calculations for conversion to this LEU fuel. Presented in this paper is the analysis of a crucial step in the conversion process: the sequence of MURR transition cores from all fresh to equilibrium burnup LEU operations. During the initial conversion from HEU to LEU fuel, MURR will operate atypically due to the lack of burned LEU elements. Given the constraints of MURR operation and experiments, a proposed transition scheme minimizes the time MURR operates atypically compared to the prototypic cycles currently run with HEU fuel and moves quickly to the same sort of equilibrium cycles for the LEU fuel.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Hardware, methodology and applications of 2 + D backscatter Mössbauer spectroscopy with simultaneous x-ray and γ-ray detection

Abstract A unique method is presented for the acquisition and analysis of 57 Fe backscatter Mössbauer spectra with simultaneous detection of the resonant 14.4 keV γ -rays and the characteristic 6.4 keV x-rays, using a custom-built multi-parameter analyser constructed on the basis of commercial analogue to digital converters and high-speed digital latches. The system allows for the simultaneous registration of Doppler-modulation velocities and photon energies, with up to 4096 and 8192 digital channels respectively. This arrangement is in contrast to most related systems, which detect at a single narrow energy window per detector. Samples of arbitrary atomic structure, morphology and surface topography can be studied without altering the setup or the analysis procedure, provided that the samples are at least micrometre sized. The hardware and software that are used to acquire data with minimal dead time are described and the custom and self-contained methods for post-measurement energy discrimination, background correction and velocity-axis folding are discussed. The data are fit using a general Hamiltonian model for the nuclear energy levels of 57 Fe and a quantum mechanical description of the angular momentum coupling is utilised, with consideration of the crystalline and chemical disorder of the sample under examination. Three examples of distinct magnetic systems, with thicknesses ranging from 5 μ m to 6 mm, that were studied using this method are presented, these are: an amorphous CoFeB-based ribbon with ultra-soft coercivity for high-frequency applications, magnetically hard Nd-Fe-B thick films on Si substrates, examined in both as-deposited and annealed states, and a sample from the nickel-rich iron meteorite NWA 6259 that contains the atomically ordered, elevated coercivity, L 1 0 phase of FeNi, tetrataenite. The wide applicability and usefulness of this method is thus demonstrated on three distinct sample morphologies that required little to no surface preparation prior to examination.

Engineering↗
DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Balancing Charging Station Utilization and Throughput in Electric Vehicle Charging Stations with Queuing Theory

The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges by integrating queuing theory with modular EVSE architectures, offering a dual strategy to optimize the operation of EV charging stations. A simulation model was developed to assess various configurations of charger capacities and outlet numbers. This model aimed to identify the optimal setup that maximizes station utilization while minimizing charging times and maximizing throughput. The model focused on charger capacities ranging from 50 to 250 kW and analyzed the different capacities’ effects on charging times and the number of vehicles served. The results indicate that a charger capacity of 125 kW is optimal, striking a balance between the charging time and the number of EVs served per hour, thus achieving the highest station utilization rate. This capacity allows for servicing a significant number of EVs with moderate increases in charging times. Lower capacities, although capable of serving more vehicles, lead to longer charging times and decreased throughput efficiency. The study underscores the effectiveness of combining queuing theory with flexible, modular charging systems that can dynamically adjust to EV charging demands.

Kumar, Praveen↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗
DOE OSTI2023

Improving Performance of III-V Solar Cells Grown on Spalled Germanium with Ex Situ Substrate Planarization

Controlled spalling allows removal of devices and provides an opportunity for cost reduction through substrate reuse. However, the fracture-based process can leave behind morphological surface features, notably river lines, that can disrupt epitaxial growth and degrade device performance. We investigate the viability of various wet etch chemistries to planarize river lines to ensure high-quality device growth and performance without mechanical repolishing, and so maintain a route towards cost-effective reuse. Etching in a HF: HNO 3 :CH 3 COOH solution effectively planarizes river lines and produces a surface that yields devices with equivalent performance to those grown on epi-ready Ge wafer surfaces. Further studies will focus on optimizing etch composition, temperature, and time to minimize material removal while maintaining a suitable surface for high-quality epitaxy.

germanium↗
DOE OSTI2024

Process damping identification using Bayesian learning and time domain simulation

Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. However, existing methods for identifying process damping models experimentally require specialized setups and/or multiple cutting tests. While the phenomenon is well known, the modeling challenges limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping models from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time domain simulation to incorporate process damping and provide a basis for test selection. Here, new strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting.

42 ENGINEERING↗
DOE OSTI2022

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗