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All sources NASA NTRS DOE OSTI

DOE OSTI2021

A Deterministic Multivariate Clustering Method for Drive Cycle Generation from In-Use Vehicle Data

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data. This paper explores the development and initial validation of a method of generating representative drive cycles from large collections of real-world vehicle data using a deterministic multivariate clustering approach. Starting with theory and diving into the methodology behind representative cycle generation, the paper aims to also present graphical and tabular results of initial validation via vehicle simulation and chassis dynamometer testing. Additional topics for further research and areas for ongoing development will also be presented.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Positron beams at Ce+BAF

Positron beams would provide a new and meaningful probe for the experimental program at the Thomas Jefferson National Accelerator Facility (JLab). The JLab Positron Working Group, formed in 2018 and now with over 250 members from 75 institutions, continues to develop an experimental program with high duty-cycle positron beams including but not limited to future hadronic physics and dark matter experiments. Critical requirements involve generating positron beams with a high degree of spin polarization, sufficient intensity and a continuous-wave (CW) bunch train compatible with acceleration to 12 GeV at the Continuous Electron Beam Accelerator Facility (CEBAF). In this presentation we describe a start-to-end layout for positron beams at 12 GeV CEBAF utilizing the Low Energy Research Facility (LERF) at Jefferson Lab to build two new injectors. A GaAs dc high voltage photo-gun first generates >1 mA of polarized electrons which are then accelerated to 80-150 MeV and directed to a high-power spinning W target for polarized bremsstrahlung and positron pair creation. A second injector then collects, bunches and accelerates the positrons to 123 MeV. The positron beams are transported by a new beam line and injected into the CEBAF acceptance for acceleration to the end stations with energies up to 12 GeV. The layout is optimized to provide Users with positron spin polarization >60% and intensity greater than >100 nA, and with higher intensities when polarization is not required.

Benesch, J.↗

DOE OSTI2023

Hierarchical multi-time-scale predictive thermal management and fuel optimization for heavy-duty compression ignition engines

For heavy-duty diesel engines, NO X emissions reduction is strongly constrained by fuel efficiency. This paper presents a hierarchical model predictive controller (H-MPC) for coordinated control of tailpipe NO X emissions and fuel consumption. The H-MPC uses the separation of slow and fast dynamics that exist in the engine and its aftertreatment system. The controller is synthesized with an architecture in which a high-level MPC uses a longer prediction horizon compared to the low-level predictive controller which tracks the high-level controller command and manages the thermal dynamics of the aftertreatment system. Engine load preview enables the high-level controller to estimate the desired catalyst temperature ahead of time and addresses the selective catalytic reduction (SCR) slow thermal dynamics. Calculated by the high-level controller, the intake manifold pressure, and the start of injection (SOI) crank angle is used as reference trajectories in the low-level controller that regulates fast dynamical behaviors such as engine out NO X emissions. Hardware-in-the-loop (HIL) validation of this integrated H-MPC on a rapid prototype controller shows that when the SCR catalyst temperature is above light-off temperature (warmed-up condition), the engine operation is shifted to operate with the best fuel economy since the warmed-up SCR can efficiently reduce the engine-out NO X emissions. Results indicate that up to 0.8% benefit in cycle averaged BSFC along with a 13% reduction in tailpipe NO X compared to a stock engine calibration can be achieved with the coordinated engine and aftertreatment system through H-MPC.

Engineering↗

DOE OSTI2025

Design of a High-Pressure Fuel System for Use with Dimethyl Ether

The paper documents the modeling and experimental work on a common rail fuel injection system for Dimethyl Ether, a potential diesel substitute with a low carbon intensity signature. The DME fuel system is deployed on a light duty 2.2L compression ignition engine. The paper describes the injector optimization to shift to higher flows to account for the lower heating value and density of the DME when compared to diesel. The type of the injection system used for the DME application is an advanced rendering of the Common rail noted for a one-piece piston-needle injector construction and a solenoid driven spill valve featuring a pressure balanced poppet. A dedicated high-pressure fuel pump designed to pressurize DME is used. The design results in a fast acting open and close injection event, reduced leakage, with reduced cavitation in the fuel injector volume. Design parameters for system optimization included fill and spill orifices, needle lift, bias spring, and injector hole size. The design model provides good correlation of the instantaneous rates of injection with experiments across a wide range of pressure and injection timings. Proposed performance milestones for the design included similar DME injection duration to the diesel counterpart for same fuel energy injected into the cylinder to retain high engine cycle efficiency. Here, the dedicated DME design provided reduced hydraulic delays of 50%. Tests demonstrated sustained operation at pressures of 1000 bar, with capability to reach 1500bar. Durability tests showed no cavitation-deterioration over a 200-hour test cycle by means of spray imaging and hardware inspection.

De Ojeda, William [WM International Engineering, L↗

DOE OSTI2023

Enabling Recycling of Composites: Understanding the Impacts of Multiple Thermal Processing Cycles

When considering the utilization of recycled short carbon fiber feedstock materials for advanced manufacturing, understanding the material degradation behavior is essential in determining how many times a composite material can be effectively reprocessed and remanufactured. This study characterizes the degradation behavior of short carbon fiber acrylonitrile butadiene (CF-ABS) that has been reprocessed five times with twin screw extrusion. Parallel plate rheology was completed to observe the degradation in complex viscosity of the recycled feedstock materials. Gel permeation chromatography (GPC) was utilized to characterize the changes in molecular weight distribution of the recycled materials as a result of thermal and mechanical degradation during the re-processing steps. Rheological characterization, GPC, and twin-screw processing data help inform the process optimizations required to process the recycled feedstock material. Successful characterization of the degradation behavior of short fiber composite feedstock materials aids in increased understanding of the lifespan of high value carbon fiber composite materials and aids in process optimization of recycled composite materials.

Walker, Roo↗

DOE OSTI2021

Creep Behavior of Compact γ′-γ″ Coprecipitation Strengthened IN718-Variant Superalloy

The development of high-temperature heavy-duty turbine disk materials is critical for improving the overall efficiency of combined cycle power plants. An alloy development strategy to this end involves superalloys strengthened by ‘compact’ γ′-γ″ coprecipitates. Compact morphology of coprecipitates consists of a cuboidal γ′ precipitate such that γ″ discs coat its six {001} faces. The present work is an attempt to investigate the microstructure and creep behavior of a fully aged alloy exhibiting compact coprecipitates. We conducted heat treatments, detailed microstructural characterization, and creep testing at 1200 °F (649 °C) on an IN718-variant alloy. Our results indicate that aged IN718-27 samples exhibit a relatively uniform distribution of compact coprecipitates, irrespective of the cooling rate. However, the alloy ruptured at low strains during creep tests at 1200 °F (649 °C). At 100 ksi (689 MPa) load, the alloy fails around 0.1% strain, and 75 ksi (517 MPa) loading causes rupture at 0.3% strain. We also report extensive intergranular failure in all the tested samples, which is attributed to cracking along grain boundary precipitates. The results suggest that while the compact coprecipitates are indeed thermally stable during thermomechanical processing, the microstructure of the alloy needs to be optimized for better creep strength and rupture life.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions

Propane has demonstrated significant potential for reductions in greenhouse gas and pollutant emissions in medium- and heavy-duty engine applications, but further improvements require accurate, compact, and scalable chemical kinetic mechanisms to design the next generation of propane fueled engines, particularly at the boosted operating conditions necessary to meet the power density demand of medium- and heavy-duty applications. In this work, six key chemical reactions were identified in a reduced mechanism with 70 species and 352 reactions through a sensitivity analysis performed at conditions typical of thermodynamic trajectories observed in a high compression ratio, long stroke engine operated on propane from throttled to boosted operating conditions. While the original mechanism was validated against rapid compression machine (RCM) data, it was found to overpredict experimental autoignition tendencies in 2-zone, 0-D SI engine simulations performed in Chemkin Pro. Subsequently, a genetic algorithm approach was used to optimize the six reaction rate parameters within established uncertainty bounds by performing RCM simulations and comparing to two independent sets of literature ignition delay times for propane, thus generating two new kinetic mechanisms. The first optimization achieved a mean absolute percent error (MPE) reduction in 2nd stage ignition delay of 61.4% in seven generations, while the second optimization utilized a newer experimental RCM dataset, and achieved MPE reduction of 56.7% in seven generations, and further marginal improvement to 57.8% reduction in 34 generations. Finally, the two mechanisms were then evaluated again in the 2-zone 0-D SI engine model in Chemkin Pro comparing typical mean and knocking cycle trajectories, and it was found that the second optimized mechanism provided better prediction of knock onset at the representative conditions evaluated in this work, particularly for higher load operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2010

Computation of Earth Science Products on Spaceborne Platforms

Spaceborne sensors like NASA's Hyperion hyperspectral imager generate huge data volumes, and several near-term trends indicate that data volumes will only increase. Next-generation hyperspectral missions, such as NASA's Hyperspectral Infrared Imager (HyspIRI), will operate at higher duty cycles and higher data rates, and their users will expect products to be generated from the data in near real time [1]. Barring a sudden advance in satellite downlink capacity, these trends point to a need to process data and generate products onboard the spacecraft. Rather than downlink an entire hyperspectral image cube, onboard processing enables satellites to downlink partial or completed scientific data products, which are often one to two orders of magnitude smaller than the original image. In addition, a satellite with onboard data processing resources and direct broadcast transmission equipment could send data products directly to first responders, research scientists or other users on the ground. Next-generation space-capable data processors will have a combination of reconfigurable gate arrays, digital signal processors and general-purpose CPUs. Correctly programmed and configured, these resources are sufficient to run sophisticated data analysis programs, including hyperspectral image processing algorithms that commonly run on desktop computers [2]. This paper describes how we implemented one such program, the HSEG hierarchical image segmentation algorithm, software commonly used on desktop and parallel processors, on a hardware platform designed to mimic a next-generation space-capable data processor [3]. We also describe our approach to porting the algorithm to and optimizing it for the new platform, and determine the expected performance gains enabled by our design. This extended abstract will describe the HSEG algorithm and hardware platform in greater detail, provide an analysis of the key function within the algorithm that required hardware acceleration, and describe our implementation of that function in hardware.

Fisher, Kevin↗

NASA NTRS2013

Neutron Characterization for Additive Manufacturing

Oak Ridge National Laboratory (ORNL) is leveraging decades of experience in neutron characterization of advanced materials together with resources such as the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) shown in Fig. 1 to solve challenging problems in additive manufacturing (AM). Additive manufacturing, or three-dimensional (3-D) printing, is a rapidly maturing technology wherein components are built by selectively adding feedstock material at locations specified by a computer model. The majority of these technologies use thermally driven phase change mechanisms to convert the feedstock into functioning material. As the molten material cools and solidifies, the component is subjected to significant thermal gradients, generating significant internal stresses throughout the part (Fig. 2). As layers are added, inherent residual stresses cause warping and distortions that lead to geometrical differences between the final part and the original computer generated design. This effect also limits geometries that can be fabricated using AM, such as thin-walled, high-aspect- ratio, and overhanging structures. Distortion may be minimized by intelligent toolpath planning or strategic placement of support structures, but these approaches are not well understood and often "Edisonian" in nature. Residual stresses can also impact component performance during operation. For example, in a thermally cycled environment such as a high-pressure turbine engine, residual stresses can cause components to distort unpredictably. Different thermal treatments on as-fabricated AM components have been used to minimize residual stress, but components still retain a nonhomogeneous stress state and/or demonstrate a relaxation-derived geometric distortion. Industry, federal laboratory, and university collaboration is needed to address these challenges and enable the U.S. to compete in the global market. Work is currently being conducted on AM technologies at the ORNL Manufacturing Demonstration Facility (MDF) sponsored by the DOE's Advanced Manufacturing Office. The MDF is focusing on R&D of both metal and polymer AM pertaining to in-situ process monitoring and closed-loop controls; implementation of advanced materials in AM technologies; and demonstration, characterization, and optimization of next-generation technologies. ORNL is working directly with industry partners to leverage world-leading facilities in fields such as high performance computing, advanced materials characterization, and neutron sciences to solve fundamental challenges in advanced manufacturing. Specifically, MDF is leveraging two of the world's most advanced neutron facilities, the HFIR and SNS, to characterize additive manufactured components.

Watkins, Thomas↗

NASA NTRS2013

Thermal Performance of ATLAS Laser Thermal Control System Demonstration Unit

The second Ice, Cloud, and Land Elevation Satellite mission currently planned by National Aeronautics and Space Administration will measure global ice topography and canopy height using the Advanced Topographic Laser Altimeter System {ATLAS). The ATLAS comprises two lasers; but only one will be used at a time. Each laser will generate between 125 watts and 250 watts of heat, and each laser has its own optimal operating temperature that must be maintained within plus or minus 1 degree Centigrade accuracy by the Laser Thermal Control System (LTCS) consisting of a constant conductance heat pipe (CCHP), a loop heat pipe (LHP) and a radiator. The heat generated by the laser is acquired by the CCHP and transferred to the LHP, which delivers the heat to the radiator for ultimate rejection. The radiator can be exposed to temperatures between minus 71 degrees Centigrade and minus 93 degrees Centigrade. The two lasers can have different operating temperatures varying between plus 15 degrees Centigrade and plus 30 degrees Centigrade, and their operating temperatures are not known while the LTCS is being designed and built. Major challenges of the LTCS include: 1) A single thermal control system must maintain the ATLAS at 15 degrees Centigrade with 250 watts heat load and minus 71 degrees Centigrade radiator sink temperature, and maintain the ATLAS at plus 30 degrees Centigrade with 125 watts heat load and minus 93 degrees Centigrade radiator sink temperature. Furthermore, the LTCS must be qualification tested to maintain the ATLAS between plus 10 degrees Centigrade and plus 35 degrees Centigrade. 2) The LTCS must be shut down to ensure that the ATLAS can be maintained above its lowest desirable temperature of minus 2 degrees Centigrade during the survival mode. No software control algorithm for LTCS can be activated during survival and only thermostats can be used. 3) The radiator must be kept above minus 65 degrees Centigrade to prevent ammonia from freezing using no more than 135 watts of heater power. 4) The LHP reservoir control heater power is limited to 15 watts with a 70 percent duty cycle. 5) The voltage of the power supply can vary between 26 volts direct current and 34 volts direct current during the spacecraft lifetime. A design analysis shows that a single LTCS can satisfy these requirements. However, shutdown of· the LHP is particularly challenging and the shutdown heater must be wired in series with two reservoir thermostats and two CCHP thermostats at different set points. An LTCS demonstration unit has been tested to verify these performance characteristics experimentally prior to proceeding to the final LTCS design and fabrication. Test results showed that the LHP shutdown scheme would be able to shut down the LHP as designed and the reservoir control heater can maintain the ATLAS mass simulator within the plus or minus 1 degrees Centigrade accuracy under various combinations of the heat load, sink temperature, and power supply voltage.

Ku, Jentung↗

DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗

NASA NTRS2013

What Reliability Engineers Should Know about Space Radiation Effects

Space radiation in space systems present unique failure modes and considerations for reliability engineers. Radiation effects is not a one size fits all field. Threat conditions that must be addressed for a given mission depend on the mission orbital profile, the technologies of parts used in critical functions and on application considerations, such as supply voltages, temperature, duty cycle, and redundancy. In general, the threats that must be addressed are of two types-the cumulative degradation mechanisms of total ionizing dose (TID) and displacement damage (DD). and the prompt responses of components to ionizing particles (protons and heavy ions) falling under the heading of single-event effects. Generally degradation mechanisms behave like wear-out mechanisms on any active components in a system: Total Ionizing Dose (TID) and Displacement Damage: (1) TID affects all active devices over time. Devices can fail either because of parametric shifts that prevent the device from fulfilling its application or due to device failures where the device stops functioning altogether. Since this failure mode varies from part to part and lot to lot, lot qualification testing with sufficient statistics is vital. Displacement damage failures are caused by the displacement of semiconductor atoms from their lattice positions. As with TID, failures can be either parametric or catastrophic, although parametric degradation is more common for displacement damage. Lot testing is critical not just to assure proper device fi.mctionality throughout the mission. It can also suggest remediation strategies when a device fails. This paper will look at these effects on a variety of devices in a variety of applications. This paper will look at these effects on a variety of devices in a variety of applications. (2) On the NEAR mission a functional failure was traced to a PIN diode failure caused by TID induced high leakage currents. NEAR was able to recover from the failure by reversing the current of a nearby Thermal Electric Cooler (turning the TEC into a heater). The elevated temperature caused the PIN diode to anneal and the device to recover. It was by lot qualification testing that NEAR knew the diode would recover when annealed. This paper will look at these effects on a variety of devices in a variety of applications. Single Event Effects (SEE): (1) In contrast to TID and displacement damage, Single Event Effects (SEE) resemble random failures. SEE modes can range from changes in device logic (single-event upset, or SEU). temporary disturbances (single-event transient) to catastrophic effects such as the destructive SEE modes, single-event latchup (SEL). single-event gate rupture (SEGR) and single-event burnout (SEB) (2) The consequences of nondestructive SEE modes such as SEU and SET depend critically on their application--and may range from trivial nuisance errors to catastrophic loss of mission. It is critical not just to ensure that potentially susceptible devices are well characterized for their susceptibility, but also to work with design engineers to understand the implications of each error mode. -For destructive SEE, the predominant risk mitigation strategy is to avoid susceptible parts, or if that is not possible. to avoid conditions under which the part may be susceptible. Destructive SEE mechanisms are often not well understood, and testing is slow and expensive, making rate prediction very challenging. (3) Because the consequences of radiation failure and degradation modes depend so critically on the application as well as the component technology, it is essential that radiation, component. design and system engineers work togetherpreferably starting early in the program to ensure critical applications are addressed in time to optimize the probability of mission success.

DiBari, Rebecca↗