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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Transitions, Dynamics, and Driven States in Quantum Magnets (Final Report Revised)

Transitions into unusual electronic, optical, and magnetic states at the absolute zero of temperature display special properties that teach us about fundamental quantum mechanics and also underlie important technologies. These so-called quantum phase transitions intertwine the static and dynamical responses of the materials changing state. They are extremely sensitive to the effects of disorder and etch in high relief the role of quantum fluctuations. There are ample questions remaining about the character of such transitions and the nature of the competing quantum states. There are also opportunities to drive quantum materials out of equilibrium, with the possibility of new types of correlated and coherent order, and new ways to access the dynamical evolution of ground and excited states. We have investigated how the order develops in time and space, and the nature of the final configurations. We are able to compare and contrast classical and quantum excitations in our experimental system of choice, and thereby can trace the relative speed by which the system settles into its lowest-energy ground state. This comparison of quantum and classical annealing protocols lies at the heart of strategies for harnessing the power of quantum computers dedicated to optimization problems. A combination of magnetic susceptibility measurements at finite frequency where the magnetic spins are queried by an external ac field, dc magnetometry measurements that characterize the macroscopic strength of magnetic order and its resistance to change, noise measurements that reveal the microscopic fluctuations of the spins as they prepare to change state, and microwave spectroscopy to look at the response of both the electronic and nuclear degrees of freedom, have been performed as functions of temperature, magnetic field, frequency, excitation amplitude, magnetic spin concentration, and disorder. This combination of probes addresses issues of stability over time, overlap between spin states, the ability of the spins to respond independently or coherently, and the promise of control on the nanometer length scale. The research reported here provides insights into fundamental quantum physics. It also addresses the question of how best to use complex systems, such as magnetic solids, to stably process quantum information.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Plasmonic photoconductive terahertz focal-plane array with pixel super-resolution

Abstract Imaging systems operating in the terahertz part of the electromagnetic spectrum are attractive due to their ability to penetrate many opaque materials and provide unique spectral signatures of various chemicals. However, the use of terahertz imagers in real-world applications has been limited by the slow speed, large size, high cost and complexity of present systems, largely due to the lack of suitable terahertz focal-plane array detectors. Here we report a terahertz focal-plane array that can directly provide the spatial amplitude and phase distributions, along with the ultrafast temporal and spectral information of an imaged object. It consists of a two-dimensional array of ~0.3 million plasmonic photoconductive nanoantennas optimized to rapidly detect broadband terahertz radiation with a high signal-to-noise ratio. We utilized the multispectral nature of the amplitude and phase data captured by these plasmonic nanoantennas to image different objects, including super-resolved etched patterns in a silicon substrate and defects in battery electrodes. By eliminating the need for raster scanning and spatial terahertz modulation, our terahertz focal-plane array offers more than a 1,000-fold increase in the imaging speed compared with the state of the art and potentially suits a broad range of applications in industrial inspection, security screening and medical diagnosis, among others.

Optics↗
DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Experimental study of airpath electrification in an opposed-piston two stroke (OP2S) engine architecture

The opposed-piston two stroke (OP2S) engine shows potential as an alternative engine architecture to the conventional four stroke engine due to its high-power density, thermal efficiency, and versatile airpath management system. Since the pistons of a two-stroke engine do not pump the air into and out of the cylinder like in a four-stroke engine, the selection of the air induction devices and airpath actuators becomes critical to optimize engine performance. Both the pumping losses and the in-cylinder combustion process can be affected by the scavenging process in a two-stroke engine. Therefore, this study compares two different airpath configurations for the same family of OP2S engines and investigates performance metrics like scavenging control, pumping work, net indicated and brake efficiencies, and engine-out emissions associated with each airpath. Data was collected on a 3.2 L, two-cylinder OP2S engine with an electrically assisted turbocharger (EAT) and a 4.9 L displacement, three-cylinder engine with a variable geometry turbocharger (VGT) and a supercharger. The experiments consisted of speed and load sweeps for both engines at the same operating conditions to compare scavenge control in both architectures. For the three-cylinder layout, the SE sweep range was much higher, and the intake pressure could be independently varied with air flowrate, thus providing more flexibility for scavenging control. The supercharger and the VGT usage was optimized based on its efficiency map and thus, this layout had lower pumping losses compared to the EAT. The two-cylinder engine had a higher overall SE as compared to the three-cylinder engine, but the intake pressure and air flowrate could not be decoupled, leading to over scavenging and increased short circuiting of fresh charge into the exhaust.

Bhatt, Ankur [Clemson University, Clemson, SC, USA↗
DOE OSTI2023

Rapid Droplet Sampling Interface for Low-Volume, High-Throughput Mass Spectrometry Analysis

Here, we present a rapid droplet sampling interface (RDSI) electrospray ionization mass spectrometry (ESI-MS) system as a high-throughput, low-volume, noncontact, and minimal-carryover approach for characterization of liquids. Liquid characterization was achieved by combining droplet ejection with an open-face microflow capillary with a 2.5 μL/min continuous flow of carrier solvent. Through this implementation, single 0.3 nL droplets containing the analyte effectively mix with 4–8 nL of carrier solvent and create a combined electrospray plume. The carrier solvent continuously cleaned the system, eliminating carryover. A sampling rate of 5 Hz was achieved for droplets containing 1 μM propranolol or 5 μM leu-enkephalin with each droplet fully baseline-resolved (138 ± 32 ms baseline-to-baseline). Using a SCIEX API4000 mass spectrometer, a lower limit of quantification (LLOQ) of propranolol was 15 nM, corresponding to 1.16 fg of propranolol in the droplet, and was linear across 3 orders of magnitude. Further, quantitation could be achieved by adding an isotopically labeled internal standard, as done in conventional ESI. Signal transients were faster than the acquisition speed of the mass spectrometer, resulting in artificially high reproducibility of 15–30% RSD droplet-to-droplet. Analyte–solvent mixing ratios could be controlled by adjusting droplet positioning along the open-face capillary with an optimal position about 0.4 mm from the tip end. The range of analyte coverage was exemplified by measures of peptides and drugs in methanol, water, and buffer solutions. In a comparison to the Open Port Sampling Interface (OPSI) implemented on the same system, the RDSI had 78× greater sensitivity, 6× greater throughput and used significantly less carrier solvent.

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

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
DOE OSTI2022

Skewing the CMB×LSS: a fast method for bispectrum analysis

Upcoming cosmic microwave background (CMB) lensing measurements and tomographic galaxy surveys are expected to provide us with high-precision data sets in the coming years, thus paving the way for fruitful cross-correlation analyses. Here, in this paper we study the information content of the weighted skew-spectrum, a nearly-optimal estimator of the angular bispectrum amplitude, as a means to extract non-Gaussian information on both bias and cosmological parameters from the bispectra of galaxies cross-correlated with CMB lensing, while gaining significantly on speed. Our results show that for the combination of the Planck satellite and the Dark Energy Spectroscopic Instrument (DESI), the difference in the constraints on bias and cosmological parameters from the skew-spectrum and the bispectrum is at most 17%. We further compare and find agreement between our theoretical skew-spectra and those estimated from N-body simulations, for which it is important to include gravitational non-linearities beyond perturbation theory and the post-Born effect for CMB lensing. We define an algorithm to apply the skew-spectrum estimator to the data and, as a preliminary step, we use the skew-spectra to constrain bias parameters and the amplitude of shot noise from the simulations through a Markov chain Monte Carlo likelihood analysis, finding that it may be possible to reach percent-level estimates for the linear bias parameter b 1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine learning models for PDE constrained optimization

Partial differential equation (PDE)-constrained optimization problems arise in a variety of scientific and engineering applications, such as topology optimization, electrodynamics, fluid dynamics, and structural dynamics. However, these problems are often challenging and computationally expensive to solve, due to the need to solve the PDEs within the optimization loop. One approach to reducing the computational cost of these methods while providing convergence guarantees is through inexact trust region methods; this method uses lower fidelity solutions of the PDE at early stages of the optimization and adjusts the required accuracy of inexact PDE solvers as the optimization progresses. In this work, we explore the use of machine learning based surrogate models with these inexact trust region methods. We first demonstrate the potential of this approach by using Gaussian processes as the surrogate model and test this on a simple PDE-constrained optimization problem. We then document explorations into improving the computational costs of evolutional deep neural network / neural Galerkin methods, with the eventual goal of using these methods with the inexact trust region algorithms. We are able to speed up these approaches, albeit at the cost of lower accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Wien filter with integrated vacuum pump

An integral Wien filter and vacuum pump for separating charged particles or for orienting their spin direction while maintaining optimal beamline vacuum. The vacuum pump is an ion pump including one or more cylindrical Penning cells to trap and expel electrons. The Wien filter includes orthogonal electric and magnetic fields to direct particles with the desired speed through the device while deflecting particles at undesired speeds. The Wien filter includes two electrodes, one biased positive and one biased negative, a dipole magnet, and means for reversing polarity of the electrodes to flip the spin of the charged particles. Metal plates on either side of the Penning cells embed gas that is ionized by trapped electrons in the Penning cell thus creating vacuum by turning gas into solid. The two metal plates can be configured to obtain vacuum pumping via chemical gettering and for removal of noble gases.

Poelker, Bernard Matthew↗
DOE OSTI2021

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. Here, in this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.

Machine learning↗
DOE OSTI2021

Experimental study on rheological and settling properties of shape memory polymer for fracture sealing in geothermal formations

This article studies the rheology and annular flow of a smart lost circulation material (LCM) at 160 °C, using a high-temperature flow loop. The smart LCM is a shape memory polymer (SMP) that activates at high temperatures, and its particle size increases to seal fractures in geothermal formations. SMP was mixed with the base fluid in two different concentrations, 1.0 and 3.0 wt%, to study rheology, wellbore hydraulics, activation process, and settling behavior under different testing conditions. The results of this study showed that the SMP could be activated at high temperatures. An increase of 80–100% in the particle size was observed at 160 °C. The mud samples showed a high shear-thinning behavior at the two concentrations with a Power-law flow index (n) ranging between 0.025 and 0.101. No additional frictional pressure losses were observed when SMP was added to the base fluid. SMP particles showed an excellent suspension at 1.0 wt% while, at 3.0 wt%, a bed was formed at a low flow rate and without pipe rotation. Increasing the drill pipe rotational speed or flow rate effectively removed the bed and homogeneously dispersed the SMP particles, ensuring a better sealing efficiency. SMP particle dispersion in inclined wells was better than in horizontal wells. Moreover, the findings of this study help optimize the lost circulation treatment by considering a wide range of operating parameters that can further be extended to different systems and geometries.

02 PETROLEUM↗
DOE OSTI2022

Progress toward picosecond on-chip magnetic memory

Here, we offer a perspective on the prospects of ultrafast spintronics and opto-magnetism as a pathway to high-performance, energy-efficient, and non-volatile embedded memory in digital integrated circuit applications. Conventional spintronic devices, such as spin-transfer-torque magnetic-resistive random-access memory (STT-MRAM) and spin–orbit torque MRAM, are promising due to their non-volatility, energy-efficiency, and high endurance. STT-MRAMs are now entering into the commercial market; however, they are limited in write speed to the nanosecond timescale. Improvement in the write speed of spintronic devices can significantly increase their usefulness as viable alternatives to the existing CMOS-based devices. In this article, we discuss recent studies that advance the field of ultrafast spintronics and opto-magnetism. An optimized ferromagnet–ferrimagnet exchange-coupled magnetic stack, which can serve as the free layer of a magnetic tunnel junction (MTJ), can be optically switched in as fast as ~3 ps. Integration of ultrafast magnetic switching of a similar stack into an MTJ device has enabled electrical readout of the switched state using a relatively larger tunneling magnetoresistance ratio. Purely electronic ultrafast spin–orbit torque induced switching of a ferromagnet has been demonstrated using ~6 ps long charge current pulses. We conclude our Perspective by discussing some of the challenges that remain to be addressed to accelerate ultrafast spintronics technologies toward practical implementation in high-performance digital information processing systems.

42 ENGINEERING↗
DOE OSTI2024

Development of Additive Manufacturing for Ceramic Matrix Composite Vanes

This report discusses the development of additive manufacturing for polymer-derived ceramic materials to create novel cooled gas turbine airfoils made from ceramics. Gas turbine engines have extremely high temperatures in the hot section of the engine that exceed the melting point of nickel superalloy materials that the engine is made from. Advanced cooling technologies have been developed over decades for metallic parts. Ceramic materials have higher temperature capabilities than superalloys, but are difficult to shape in the complex designs used in modern turbines. Several thrusts were investigated during the project, including development of higher material strength resins, improvement of ultraviolet (UV) photopolymerization and post-processing techniques to increase survivability of ceramic parts, thermomechanical modeling of novel ceramic airfoil designs for higher cooling effectiveness, and experimental validation of the modeling and manufacturing in a turbine vane test facility at nondimensional conditions that are relevant to modern gas turbines. Some major findings of the work include a significant increase in ceramic survivability during pyrolysis (a high temperature process that converts the organic material to a ceramic) by adjusting the composition ratios of the resins, as well as a tolulene soak after printing to remove unreacted polymer material. Thermomechanical optimization of the internal cooling structure indicated that a high density pin fin array would enable an 80% increase in overall cooling effectiveness relative to a baseline geometry, which was later verified during experimental testing in a high speed linear cascade. Ceramic vanes were tested at Mach numbers of up to 0.9 which is relevant to modern gas turbines. High temperature capability of the ceramic vanes was not tested in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Development of a High-Pressure Fuel Injection System for Use with Propane-DME

Paper details the design approach and performance of a high-pressure common rail fuel injection system used with Propane-DME mixtures targeting high injection pressures on a light duty 2.2L inline-4 compression ignition engine. The study estimates the bulk modulus of elasticity based on the hardware geometry and operating pressures to assess the compressibility of Propane and DME across a range of pressures typical of the LD engine application. The compressibility factor ranges from 500 to 3000 bar, significantly lower than the theoretical values for the conditions tested. The high-pressure pump performance is optimized via the implementation of an inlet metering valve operated on a crank-angle open/close sequence to control pressure at the common rail. The application of a model-based controller and the use of high-speed sampling of rail pressure indicate that pressure at the rail can be attained with the pump metering valve, and without use of the pressure relieve valve present in the rail. Results show a significant reduction on pumping work parasitics and of fuel heating, on the order of 40°C, at typical working conditions with the proposed approach. Injector rates of injection with Propane-DME are very similar to those of Diesel. Paper compares bench data of rates of injection versus simulation predictions across operating pressures of 200 to 1000bar and from injection commands spanning 0.20 to 2.0ms. Furthermore, the accuracy of the model predictions are attributed in part by to the updated fuel compressibility modulus descriptions attained during the testing.

42 ENGINEERING↗