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DOE OSTI2023

NISQ algorithm for the matrix elements of a generic observable

The calculation of off-diagonal matrix elements has various applications in fields such as nuclear physics and quantum chemistry. In this paper, we present a noisy intermediate scale quantum algorithm for estimating the diagonal and off-diagonal matrix elements of a generic observable in the energy eigenbasis of a given Hamiltonian without explicitly preparing its eigenstates. By means of numerical simulations we show that this approach finds many of the matrix elements for the one and two qubits cases. Specifically, while in the first case, one can initialize the ansatz parameters over a broad interval, in the latter the optimization landscape can significantly slow down the speed of convergence and one should therefore be careful to restrict the initialization to a smaller range of parameters.

Physics↗

DOE OSTI2022

Three-Photon Optical Pumping for Trapped Ion Quantum Computing

In this report we describe the testing of a novel scheme for state preparation of trapped ions in a quantum computing setup. This technique optimally would allow for similar precision and speed of state preparation while allowing for individual addressability of single ions in a chain using technology already available in a trapped ion experiment. As quantum computing experiments become more complicated, mid-experiment measurements will become necessary to achieve algorithms such as quantum error correction. Any mid-experiment measurement then requires the measured qubit to be re-prepared to a known quantum state. Currently this involves the protected qubits to be moved a sizeable distance away from the qubit being re-prepared which can be costly in terms of experiment length as well as introducing errors. Theoretical calculations predict that a three-photon process would allow for state preparation without qubit movement with similar efficiencies to current state preparation methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Pulse Strengthened and Laser Edge Sealed Vacuum Insulation Glazing

A scalable, low-cost processing strategy for glass strengthening and sealing is proposed to improve the thermal performance of insulating glass units and develop innovative window technologies. In this project, a combination of large area photonic processing, additive manufacturing, and laser encapsulation techniques is reported to realize a vacuum glazing technology to meet the cost, performance, reliability, and throughput demands. A modeling framework for evaluating the mechanical and thermal response was established to verify the structural deformation and thermal conductance. Micro-size glass frits in printing ink and a continuous-wave laser curing were employed to allow the formation of a hermetic bonding layer and led to improvement of glass edge sealing. Various sealing parameters, including laser traveling speed, spot diameter, and laser power were optimized. The water seepage and mechanical strength of the resulting glass-to-glass bonding were examined as well. These results showcase the development of a practical vacuum insulating glazing with several unique features through a combination of photonic processing, laser encapsulation, and additive manufacturing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Empirical Investigation of Properties for Additive Manufactured Aluminum Metal Matrix Composites

Laser additive manufacturing with mixed powders of aluminum alloy and silicon carbide (SiC) or boron carbide (B4C) is investigated in this experiment. With various mixing ratios of SiC/Al to form metal matrix composites (MMC), their mechanical and physical properties are empirically investigated. Parameters such as laser power, scan speed, scan pattern, and hatching space are optimized to obtain the highest density for each mixing ratio of SiC/Al. The mechanical and thermal properties are systematically investigated and compared with and without heat treatment. It shows that 2 wt% of SiC obtained the highest strength and Young’s modulus. Graded composite additive manufacturing (AM) of MMC is also fabricated and characterized. Various types of MMC devices, such as heat sink using graded SiC MMC and grid type three-dimensional (3D) neutron collimators using boron carbide (B4C), were also fabricated to demonstrate their feasibility for applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Performance assessment of 3D printed multi-material energy absorber for automotive bumper: pedestrian lower extremity protection

Designing an energy absorber for automotive bumpers involves balancing low-speed and high-speed impacts to ensure safety, reduce repair costs, and meet regulatory standards. Here, this study explores a novel design using multi-material 3D printing and structural optimization to fabricate a lightweight and cost-efficient energy absorber. The design effectively dissipates energy in low-speed collisions and minimizes force transmission in high-speed pedestrain impacts, helping to meet both safety and performance requirements. The energy absorber design combines 20% carbon fiber-reinforced acrylonitrile butadiene styrene (CF-ABS) and thermoplastic polyurethane (TPU) for optimal stiffness and flexibility. It uses 3D-printed lattice structures optimized through finite element simulations to help meet both low-speed and high-speed impact requirements. Full-scale energy absorbers were 3D-printed using optimized CF-ABS/TPU blends and tested under high-speed impact using the Flexible Pedestrian Legform Impactor (Flex-PLI). For fair comparison, a baseline bumper with a traditional triangular lattice structure, also 3D-printed from the same CF-ABS/TPU materials, was similarly tested. Interestingly, both the optimized and baseline 3D-printed energy absorbers showed nearly identical performance, successfully meeting injury limits. Their performances were also benchmarked against an injection-molded energy absorber. While both 3D-printed and injection-molded designs met injury limits, the 3D-printed absorber exhibited a higher tibia bending moment, indicating an opportunity for further optimization. A Techno-Economic Analysis compared the costs of producing energy absorbers using traditional manufacturing and 3D printing. The analysis highlighted that 3D printing offers cost benefits for low to medium production volumes, with the total cost per energy absorber at ∼ $\$$74, compared to traditional methods that become economical beyond 2000 units.

Additive manufacturing↗

DOE OSTI2022

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Zhao, Junfeng↗

DOE OSTI2023

Simulating and Optimizing Quantum State Transport

The goal of this project was to produce an accurate simulator of the “pitch and catch” process of sending quantum states between SRF cavities, and to find a method for optimizing the control pumps. We developed a program in C++ to integrate the equations of motion with high speed and precision, and used this as a basis for finding an optimal control function to maximize the state transfer efficiency. To demonstrate this we have shown an example of an optimal drive function and the resulting system dynamics, as well as a heat map of the cost function for a range of drive amplitudes.

Weston, Jacob↗

DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Melt Pool and Heat Treatment Optimization for the Fabrication of High-Strength and High-Toughness Additively Manufactured 4340 Steel

Additively manufactured (AM) components offer superior design flexibility compared to their conventionally manufactured counterparts, and optimizing processing parameters is key to achieving high-quality depositions with desirable and predictable mechanical properties. This study was focused on 4340 steel fabricated using laser powder bed fusion (LPBF), and 42 laser power and scan speed combinations have been systematically investigated to determine an optimized melt pool geometry that would ensure fully-dense parts. The AM material was compared with a wrought 4340 equivalent and studied in two customized heat treated conditions, optimized for strength and toughness, respectively. The microstructures of the as-fabricated and heat treated AM and wrought materials were characterized to assess differences introduced by the layer-by-layer fabrication process and subsequent heat treatment. Tensile properties of both materials were also evaluated and demonstrate that the AM materials offer equal or superior properties compared to the wrought equivalents. Differences in fracture surface morphologies indicate the distinct failure mechanisms associated with the materials’ characteristic microstructures, and the role of inclusions in the failures was studied to elucidate these differences. Complementary to the experimental investigations, the dataset was leveraged to make recommendations for future design of experiments to optimize AM build parameters in other material systems. A statistical Monte Carlo analysis was used to predict the interpolation error produced using reduced datasets and to enable informed processing parameters selection. These findings are discussed to make recommendations for the use of AM materials for high-integrity structural applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Impact of breech geometry and propellant flow on the release of large pellets for the ITER disruption mitigation system

Studies have been performed on the release mechanism for large pellets using high pressure gas in a shattered pellet injector. Typically, pellets are dislodged from the cryogenic surface and accelerated down a barrel using high pressure gas delivered by a fast-acting propellant valve. The pellets impact an angled surface which shatters the pellet into many small fragments before entering the plasma. This technique was initially demonstrated on DIII-D (Commaux et al 2016 Nucl. Fusion 56 046007) and is now deployed on JET, KSTAR, ASDEX-Upgrade, and other tokamaks around the world in support of ITER's disruption mitigation system design and physics basis. The large hydrogen, 28.5 mm diameter, 2 length-to-diameter ratio, pellets foreseen for ITER SPI operation have low material strength and low heat of sublimation, which cause the pellets to be fragile and highly reactive to the impact of warm propellant gas. Due to the size of the pellets, significantly more propellant gas is required to dislodge and accelerate them. This creates a potentially significant propellant gas removal issue as 2–6 bar-L of gas is expected to be required for release and speed control. The research presented in this paper is an in-depth exploration of the parameters that are keys to reliable pellet release and speed control. Computational fluid dynamics (CFD) modeling of propellant flows through various breech designs was conducted to determine the force generated on the back surface of a pellet. These simulations assumed the use of the ORNL designed flyer plate valve. CFD modeling combined with experimental measurements provide adequate insight to determine a path to an optimal valve and breech design for ITER SPI pellet release and speed control while minimizing propellant gas usage.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Dynamic sparse x-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst

Tomographic imaging of time-evolving samples is a challenging yet important task for various research fields. At the nanoscale, current approaches face limitations of measurement speed or resolution due to lengthy acquisitions. We developed a dynamic nanotomography technique based on sparse dynamic imaging and 4D tomography modeling. We demonstrated the technique, using ptychographic x-ray computed tomography as its imaging modality, on resolving the in situ hydration process of polymer electrolyte fuel cell (PEFC) catalyst. The technique provides a 40-time increase in temporal resolution compared to conventional approaches, yielding 28 nm half-period spatial and 12 min temporal resolution. The results allow a quantitative characterization of the water intake process inside PEFC catalysts with nanoscale resolution, which is crucial for understanding their electrochemical mechanisms and optimizing their performance. Our technique enables high-speed operando nanotomography studies and paves the way for wider application of dynamic tomography at the nanoscale.

Science & Technology - Other Topics↗

DOE OSTI2022

Multicolor multifocal 3D microscopy using in-situ optimization of a spatial light modulator

Abstract Multifocal microscopy enables high-speed three-dimensional (3D) volume imaging by using a multifocal grating in the emission path. This grating is typically designed to afford a uniform illumination of multifocal subimages for a single emission wavelength. Using the same grating for multicolor imaging results in non-uniform subimage intensities in emission wavelengths for which the grating is not designed. This has restricted multifocal microscopy applications for samples having multicolored fluorophores. In this paper, we present a multicolor multifocal microscope implementation which uses a Spatial Light Modulator (SLM) as a single multifocal grating to realize near-uniform multifocal subimage intensities across multiple wavelength emission bands. Using real-time control of an in-situ-optimized SLM implemented as a multifocal grating, we demonstrate multicolor multifocal 3D imaging over three emission bands by imaging multicolored particles as well as Escherichia coli ( E. coli ) interacting with human liver cancer cells, at $$\sim 2.5$$ ∼ 2.5 multicolor 3D volumes per second acquisition speed. Our multicolor multifocal method is adaptable across SLM hardware, emission wavelength band locations and number of emission bands, making it particularly suited for researchers investigating fast processes occurring across a volume where multiple species are involved.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Mesoscale Modeling to Characterize Eagle Soaring Habitat

Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Vibroacoustic Process Monitoring of 5-Stage Centrifugal Contactors

Vibroacoustic monitoring was investigated to aid in informing process operators with parameters regarding aqueous separation techniques. These techniques have a wide range of applications including nuclear fuel reprocessing. A small-scale solvent extraction system was set up with five magnetic drive contactors and two piston pumps to collect realistic operation. Vibration sensors were placed on three of the contactors and both pumps, and a microphone was placed near the system. Contactor rotational speed and pump flow rate were varied with known values to compare against measured vibroacoustic signatures. Spectral analysis allowed for the determination of contactor rotational speed and the ratio of organic to aqueous flow rate, an important parameter for ensuring optimal extraction. Vibration metrics such as kurtosis and crest factor provided additional details regarding individual rotational speeds and have potential in examination of faults for predictive maintenance. Overall, results indicate the capability of vibroacoustic monitoring to improve operator awareness and process outcomes.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Machine learning enhanced characterization and optimization of photonic cured MAPbI 3 for efficient perovskite solar cells

Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC conditions for fabricating methylammonium lead iodide (MAPbI 3 ) films by quantitatively comparing their ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI3 film with an absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However, when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI 3 and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI 3 , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the optimization of PC processing conditions for MAPbI 3 films.

14 SOLAR ENERGY↗

DOE OSTI2023

Loose Belt Fault Detection and Virtual Flow Meter Development Using Identified Data-driven Energy Model for Fan Systems

An energy model that correlates fan airflow, head, speed, and system power input is essential to detect device faults and optimize control strategies in fan systems. Since the application of variable-frequency drives (VFDs) makes the motor-efficiency data published by manufacturers inapplicable for VFD–motor–fan systems, the fan efficiency and drive (belt–motor–VFD) efficiency must be identified for each individual system to obtain accurate energy models. The objectives of this paper are to identify an energy model of existing VFD–motor–fan systems using available experimental data and demonstrate its applications in loose belt fault detection and virtual airflow meter development for optimal control. First, an approach is developed to identify the fan head, fan efficiency, and drive-efficiency curves using available fan head, speed, and system power input as well as temporarily measured airflow rate without measuring shaft power. Then, the energy model is identified for an existing VFD–motor–fan system. Finally, the identified model is applied to detect the slipped belt faults and develop the virtual airflow meter. The experiment results reveal that the developed approach can effectively obtain the energy model of VFD–motor–fan systems and the model can be applied to effectively detect slipped belt faults and accurately calculate the fan airflow rate.

42 ENGINEERING↗

DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗