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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing and Understanding the Spatial and Energy Distributions of Plasmonic Hot Carriers via Single-Molecule Quantum Transport

The generation of hot carriers in plasmonic nanostructures offers new transformative opportunities for a range of applications, including solar energy harvesting, photochemistry, and photodetection. To better understand such phenomena, this project aims to develop an experimental approach to study and understand hot carrier distributions in photoexcited plasmonic nanostructures. The developed experimental approach, combining scanning probe-based single-molecule quantum transport measurements with plasmonic excitation methods, leverages nanoscopic single molecules as quantum energy filters to selectively access hot carriers in a variety of plasmonic nanostructures. This report summarizes the key outcomes, impacts, and lessons learned in the first two years of the project. Over the past two years, important progress was made in achieving the project's goals, including the development of the scanning probe-based experimental platform, fabrication of plasmonic nanostructures, and design and synthesis of the desired molecular candidates. Insights from this research will pave the path toward the development of technologies that will improve the way we harvest solar energy and drive chemical reactions and ultimately help transition the U.S. to a clean and sustainable society.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Device-Centric Firmware Malware Detection for Smart Inverters using Deep Transfer Learning

Since future power grids are inverter-dominant grids and inverters are getting smarter by incorporating remote access and seamless firmware update, it is anticipated that malware attackers will directly target smart inverters. However, malware threats targeting smart inverters have been less studied yet. This paper explores potential malware attacks targeting smart inverters and proposes a deep transfer-learning (DTL)-based malware detection framework for smart inverters. The proposed DTL method can significantly reduce development time and efforts for an artificial intelligence-based malware detection algorithm while improving detection accuracy. The experimental result shows that the proposed method achieves 98% of firmware malware detection accuracy. Furthermore, this approach will be transformative to other smart grid devices enabling seamless firmware update.

artificial intelligence↗
DOE OSTI2020

Thermal Image Processing for Feature Extraction from Encapsulated Phase Change Materials

Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

25 ENERGY STORAGE↗
DOE OSTI2025

PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to an approximately 7 nm Co3O4 nanoparticle on an approximately 30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover ‘missing wedge’ information. Applied directly to biological particles, it enhances reconstruction quality and reduces artifacts, establishing a standard for routine 3D imaging with phase contrast.

Biophysics↗
DOE OSTI2024

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ∼12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Implementing Arbitrary/Common Concurrent Writes of CRCW PRAM

The Parallel Random Access Machines (PRAM) abstraction is the simplest and most elegant algorithmic model for the design and analysis of parallel algorithms. It consists of different models categorized based on the underlying memory access mode used, the most powerful of which is the Concurrent Read Concurrent Write (CRCW) model. A PRAM algorithm describes a series of rounds, each of which consists of a collection of operations that can be executed concurrently within the same time step. However, the lack of support for concurrent memory accesses and the prevalence of asynchronous programming models led to the belief that implementing CRCW PRAM algorithms is unattainable and prompted many to avoid this model except for theoretical studies of optimal performance.In this work, we study the arbitrary and common concurrent writes in the CRCW PRAM model and explore implementation challenges on general-purpose systems. Moreover, we examine current practices for implementing common/arbitrary concurrent writes and propose a new efficient lightweight and thread-safe method to implement concurrent writes through leveraging atomic instructions. To demonstrate the efficacy of our method, we developed OpenMP kernels for classical CRCW PRAM algorithms and provide experimental results and comparisons based on run time performance measured over the x86 multicore architecture. Our results show a performance speedup compared to current practices up to 4.5x across all our benchmarks.

Ghanim, Fady↗
DOE OSTI2022

Compressor is A Sensor: A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The primary goal of this report is to develop a universal charge fault detection method that requires only a few experimental data with high prediction accuracy. Currently, pump-down operations is typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repair. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The new charge fault detection and diagnostics method innovatively utilizes the typical pump down operation, the compressor’s low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.

42 ENGINEERING↗
DOE OSTI2024

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ~12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Scalable Asynchronous Domain Decomposition Solvers

We discuss how parallel implementations of linear iterative solvers generally alternate between phases of data exchange and phases of local computation. Increasingly large problem sizes and more heterogeneous compute architectures make load balancing and the design of low latency network interconnects that are able to satisfy the communication requirements of linear solvers very challenging tasks. In particular, global communication patterns such as inner products become increasingly limiting at scale. We explore the use of asynchronous communication based on one-sided Message Passing Interface primitives in the context of domain decomposition solvers. In particular, a scalable asynchronous two-level Schwarz method is presented. We discuss practical issues encountered in the development of a scalable solver and show experimental results obtained on a state-of-the-art supercomputer system that illustrate the benefits of asynchronous solvers in load balanced as well as load imbalanced scenarios. Using the novel method, we can observe speedups of up to four times over its classical synchronous equivalent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Radial Steering Displacement Method to Measure Dispersion

The measurement of the dispersion function in a transfer line plays the crucial role for the development of an accurate optics model for the beam tracking simulation. In this technical note, we describe an experimental method to measure the dispersion value at multi-wire location MW063 in the NASA Space Radiation Laboratory (NSRL) transfer line. Finally, we use our MADX model to calculate the dispersion function at the same multi-wire location. The ultimate goal of this measurement and calculation is to understand the right value of dispersion function and the beam size (emittance) at the entrance of NSRL beam line.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Combined speckle- and propagation-based single shot two-dimensional phase retrieval method

Single-shot two-dimensional (2D) phase retrieval (PR) can recover the phase shift distribution within an object from a single 2D x-ray phase contrast image (XPCI). Two competing XPCI imaging modalities often used for single-shot 2D PR to recover material properties critical for predictive performance capabilities are: speckle-based (SP-XPCI) and propagation-based (PB-XPCI) XPCI imaging. However, PR from SP-XPCI and PB-XPCI images are, respectively, limited to reconstructing accurately slowly and rapidly varying features due to noise and differences in their contrast mechanisms. Herein, we consider a combined speckle- and propagation-based XPCI (SPB-XPCI) image by introducing a mask to generate a reference pattern and imaging in the near-to-holographic regime to induce intensity modulations in the image. We develop a single-shot 2D PR method for SPB-XPCI images of pure phase objects without imposing restrictions such as object support constraints. It is compared against PR methods inspired by those developed for SP-XPCI and PB-XPCI on simulated and experimental images of a thin glass shell before and during shockwave compression. Reconstructed phase maps show improvements in quantitative scores of root-mean-square error and structural similarity index measure using our proposed method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Machine learning-enhanced model-based scenario optimization for DIII-D

Abstract Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Artificial-intelligence-assisted analysis of 28 Si * → 7⁢𝛼 breakup data

Mid-weight 𝛼-conjugate nuclei are predicted to possess exotic toroid like resonances with high angular momenta. The search for these states in 28 Si* is the main point of two published experimental investigations of the peripheral 28 Si + 12 C reaction by Cao and collaborators and by Hannaman and collaborators. In this work, we develop a novel artificial intelligence (AI) based machine learning method utilizing the Gaussian Mixture Model (GMM) to analyze available experimental and theoretical data. Here, we additionally study the reaction with the Hybrid 𝛼-Cluster (H⁡𝛼⁢C) model. In all the examined data, our results suggest the presence of underlying structure which is close to that predicted for toroidal states.

Breakup reactions↗
DOE OSTI2022

CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework

Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2023

Deep-Learning Electron Diffractive Imaging

Here, we report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene, and a gold nanoparticle with comparable quality to those reconstructed by a conventional ptychographic algorithm. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a resolution in the range of 0.70 and 0.55 Å. We further develop CNNs to recover the probe function from the experimental data. The ability to replace iterative algorithms with CNNs and perform real-time atomic imaging from coherent diffraction patterns is expected to find applications in the physical and biological sciences.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Experimental demonstration of coupled multi-peak Bragg coherent diffraction imaging with genetic algorithms

Bragg coherent diffraction imaging has the potential to provide significant insight into the structure-properties relationship for crystalline materials by imaging, with nanoscale resolution, three-dimensional strain fields within individual grains and nanoparticles. The capability of present-day synchrotrons to locate and measure a multiplicity of Bragg reflections from a single grain makes it possible to recover the full strain tensor with nanometer resolution. Recent methods for coupling reconstructions from several peaks to determine the strain tensor have been developed and applied to synthetic data, but have not been applied to experimental data. Here, using a coupled genetic reconstruction algorithm, we reconstruct an experimental data set and demonstrate improvements in the ability to resolve vector-valued displacement fields internal to the particle as compared to what is achieved with a noncoupled approach. The coupled approach developed in this work was also validated on simulated data sets. In both simulated and experimental data, reconstructions from our coupled Bragg peak algorithm show improvements over the noncoupled independent reconstruction method of 5% in terms of accuracy and 53% in terms of consistency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗