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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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Designing and prototyping extensions to the Message Passing Interface in MPICH
As HPC system architectures and the applications running on them continue to evolve, the MPI standard itself must evolve. The trend in current and future HPC systems toward powerful nodes with multiple CPU cores and multiple GPU accelerators makes efficient support for hybrid programming critical for applications to achieve high performance. However, the support for hybrid programming in the MPI standard has not kept up with recent trends. The MPICH implementation of MPI provides a platform for implementing and experimenting with new proposals and extensions to fill this gap and to gain valuable experience and feedback before the MPI Forum can consider them for standardization. Here, in this work, we detail six extensions implemented in MPICH to increase MPI interoperability with other runtimes, with a specific focus on heterogeneous architectures. First, the extension to MPI generalized requests lets applications integrate asynchronous tasks into MPI’s progress engine. Second, the iovec extension to datatypes lets applications use MPI datatypes as a general-purpose data layout API beyond just MPI communications. Third, a new MPI object, MPIX_Stream, can be used by applications to identify execution contexts beyond MPI processes, including threads and GPU streams. MPIX stream communicators can be created to make existing MPI functions thread-aware and GPU-aware, thus providing applications with explicit ways to achieve higher performance. Fourth, MPIX Streams are extended to support the enqueue semantics for offloading MPI communications onto a GPU stream context. Fifth, thread communicators allow MPI communicators to be constructed with individual threads, thus providing a new level of interoperability between MPI and on-node runtimes such as OpenMP. Lastly, we present an extension to invoke MPI progress, which lets users spawn progress threads with fine-grained control to adapt the communication performance to their application designs. We describe the design and implementation of these extensions, provide usage examples, and highlight their expected benefits with performance results.
Inflow/outflow boundary conditions for particle-based blood flow simulations: Application to arterial bifurcations and trees
When blood flows through a bifurcation, red blood cells (RBCs) travel into side branches at different hematocrit levels, and it is even possible that all RBCs enter into one branch only, leading to a complete separation of plasma and R- BCs. To quantify this phenomenon via particle-based mesoscopic simulations, we developed a general framework for open boundary conditions in multiphase flows that is effective even for high hematocrit levels. The inflow at the inlet is duplicated from a fully developed flow generated in a pilot simulation with periodic boundary conditions. The outflow is controlled by adaptive forces to maintain the flow rate and velocity gradient at fixed values, while the particles leaving the arteriole at the outlet are removed from the system. Upon valida- tion of this approach, we performed systematic 3D simulations to study plasma skimming in arterioles of diameters 20 to 32 microns. For a flow rate ratio 6:1 at the branches, we observed the \all-or-nothing" phenomenon with plasma only entering the low flow rate branch. We then simulated blood-plasma separation in arteriolar bifurcations with different bifurcation angles and same diameter of the daughter branches. Our simulations predict a significant increase in RBC flux through the main daughter branch as the bifurcation angle is increased. Lastly, we demonstrated the new methodology for simulating blood flow in ves- sels with multiple inlets and outlets, constructed using an angiogenesis model.
Assessing The Energy Impact Of Traffic Management and Vehicle Hybridization
We provide a review of methodologies previously used to evaluate impacts of transportation systems and changes in transportation infrastructure on energy consumption. We present a new framework that allows estimating the energy impacts of managed traffic lanes in the context of vehicle automation. The presented framework relies on two major components, an integrated transportation system simulator and a powertrain simulator. For the transportation system simulator we propose using integrated transportation system simulator POLARIS. For the powertrain simulator we use AUTONOMIE, a tool funded by the U.S. Department of Energy. Both tools are developed at Argonne National Laboratory. We demonstrate our approach by modelling a transportation corridor along a major highway. Two scenarios are considered, unmanaged, when both trucks and cars use all the lanes of the highway and managed, under which one of the highway lanes is a dedicated lane for truck traffic and trucks are forming platoons using adaptive cruise control technology. We provide the numerical results of the experiment at the end of the paper. We also present the impact of vehicle hybridisation combined with automation on the energy consumption.
WholeTraveler Anonymized Data Phase 1
Phase 1 of the WholeTraveler Study data collection consisted of an online-only survey. This survey captured data on three categories of observable variation in the population relevant to transportation decisions. First, the survey collected traditional demographic data such as age, gender, income, and education level. Second, it collected data across personality, psychological, and preference categories. This included: 1. The "Big Five" inventory personality traits: openness to new experience, conscientiousness, extroversion, agreeableness, and neuroticism; 2. Risk and time preferences; and 3. Environmental preferences. Third, the survey collected data on historical behavior patterns including: 1. Adoption of (as well as interest in) new technologies or innovations (e.g., smartphones, PEVs, solar panels, adaptive cruise control [ACC]); 2. Car ownership history and current car ownership status; 3. Recent mode use across different time scales (e.g., previous week, previous month, previous year); and 4. Timing of major life events such as starting a family as well as overall lifecycle trajectory patterns. Data from Phase 1 and Phase 2 are linked by a unique respondent identifier. Anonymized versions of the Phase 1 and Phase 2 data are both available on Livewire.
Grid-tied Multilevel Inverter with Phase-locked Loop Algorithm
A multilevel inverter is an electronic device capable of changing direct current energy to alternant current energy with a voltage and frequency established by the user. They are ideal for connecting renewable energy sources to the AC grid, energy plants, and smart grids. The voltage must be balanced and synchronized with the electrical network for adequate performance. This paper shows the voltage synchronization between an inverter output voltage and the AC grid using a phase-locked loop based on an adaptive observer. The proposed algorithm can perform under grid uncertainties such as noise and generates a reference signal for the modulation used in the inverter. The algorithm is robust and computationally efficient and can be implemented through basic elements such as operational amplifiers, resistors, and capacitors, reducing its difficulty in executing it in a system.
“Translational Opportunities in CPS Transportation”
This talk will describe opportunities for translating research from open-road experiments with modified adaptive cruise controllers. The data and controllers used for the previous research are based on use cases and example drives from within the US. We explore processes, data sharing, experiments, and other techniques that we think will drive trans-Pacific partnerships with driving data.
Evaluating The Impact Of Connected Vehicle Technology On Heavy Duty Vehicle Emissions
Vehicles with advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) could use connected (V2X) data to improve fuel economy Eco driving generates an energy efficient speed profile by minimizing acceleration events SwRI demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks by using nearby vehicle and traffic signal information How does eco driving impact vehicle emissions and aftertreatment (AT) systems?
Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing
This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.
Delivery Ring Spill Characterization and Impulse Study
High-intensity particle physics experiments require uniform beam extraction to prevent instantaneous rate spikes from overwhelming detector systems. By analyzing accelerator parameters and extracted beam dynamics, we directly inform spill regulation systems that make real-time adjustments to minimize non-uniformity. This Department of Energy Visiting Faculty Program project transitioned from characterizing Main Injector half-integer slow extraction for SpinQuest to Delivery Ring third-integer slow extraction for Mu2e. Working alongside the Fast Adaptive Neural Control (FANC) group, we developed an automated pipeline that aligns asynchronous instrument channels, embeds quality metrics, and isolates clean spill populations. Analyzing baseline spills alongside a dedicated quadrupole impulse study allowed us to quantify noise structures while mapping time-varying beam response and transit-delay dynamics. These empirical measurements directly ground digital twin models, supporting FANC’s deployment of real-time, FPGA-based neural network controllers in the Mu2e Spill Regulation System.
Closed-form solutions towards real-time energy-optimal and collision-free speed planning with limited information
Under real-world driving conditions, available information is limited due to communication range, and connected and automated vehicles (CAVs) must plan and follow energy-optimal and collision-free speed trajectory with a high updating rate. To this end, this paper presents a holistic design of a speed planning algorithm using analytical closed-form optimal solutions. Using the simplest vehicle model, we derive closed-form solutions as a function of boundary conditions, and summarize them without and with state constraints imposed by speed limits and vehicle in front. Then, we introduce multiple modes (e.g., eco-approach to traffic signal) to properly respond dynamically changing situations, and present how to set boundary conditions for each mode, while guaranteeing nature of globally optimal solutions. Finally, we perform a large-scale simulation study to identify and quantify the energy impacts of CAVs for real-world driving routes. A simple but effective algorithm based on closed-form solutions shows a significant energy saving potential in comparison with human-driven vehicle and adaptive cruise controlled vehicle with connectivity.
Performance of DSRC V2V Communication Networks in an Autonomous Semi-Truck Platoon Application
Autonomy for multiple trucks to drive in a fixedheadway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity. Road grade - antenna may have limited vertical coverage. Our results, which include multiple plots and graphs and their analysis, build on those reported by others and could be of interest to researchers, practitioners, and end-users in the broader autonomous-connected vehicle community.
Narrow linewidth picosecond pulsed laser with mega-watt peak power at UV wavelength
We demonstrate a master oscillator power amplifier (MOPA) burst mode laser system to generate 66 ps/402.5 MHz pulses with mega-watt peak power at 355 nm. The seed laser is based on a direct electro-optic modulation of a fiber laser output. A very high extinction ratio (45 dB) has been achieved by using an adaptive bias control. The multi-stage Nd:YAG amplifier system allows a uniformly temporal shaping of macropulses with tunable pulse duration. The light output form the amplifier is converted to 355 nm and over 1 MW UV peak power is obtained when the laser is operating in a 5- s/10-Hz macropulse mode. The laser output has a transform limited spectrum bandwidth with a very narrow linewidth of individual laser mode. The immediate application of the laser system is the laser assisted hydrogen ion beam stripping for the Spallation Neutron Source (SNS).
Approaching Maximum Power Conversion with Exergy-based Adaptive Wave-by-Wave Control of a Wave Energy Converter.
Abstract not provided.
Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms
Scientific simulations in high-performance computing (HPC) environments are producing vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for post-analysis. Unlike the traditional data reduction schemes (such as deduplication or lossless compression), not only can error-controlled lossy compression significantly reduce the data size but it can also hold the promise to satisfy user demand on error control. Point-wise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications in the lossy compression, since error control can adapt to the precision in the dataset automatically. Point-wise relative error bounded compression is complicated and time consuming. In this work, we develop efficient precomputation-based mechanisms in the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error bounded compression with excellent compression ratios. In addition, our mechanisms also help reduce traversing operations for Huffman decoding, and thus significantly accelerate the decompression process in SZ. Experiments with four well-known real-world scientific simulation datasets show that our solution can improve the compression rate by about 30% and decompression rate by about 70% in most of cases, making our designed lossy compression strategy the best choice in class in most cases.
Analyzing the Effects of Geometric Lane Constraints on Radar-Based Sensing of Available Vehicle Headway Using Mapped Lane Geometry and Camera Registration of Lane Position
Commercial trucks are currently equipped with a single front-facing RADAR mounted on the front bumper, as this is a sensor useful for Cooperative Adaptive Cruise Control, Emergency Braking Systems, and many similar Connected and Automated vehicle functions that require longitudinal vehicle control. This paper investigates the use of a bumper-mounted RADAR to perform traffic characterization around the ego-vehicle to obtain an estimate of the furthest headway that can be considered as a reliable estimate of open maneuvering space, such that there are no vehicles within the same lane as the ego-vehicle. This available headway in front of a vehicle is an important parameter in an ongoing study whose goal is to obtain improvements in fuel economy for highway driving of a tractor-trailer. But headway availability depends on the correct attribution of RADAR measured vehicles to be either within the ego-lane, or outside the lane. The attribution of lane designations to specific RADAR targets depend strongly on lane geometry and the ability to align RADAR measurements to the ego-lane. This work investigates how knowledge of lane geometries, as well as sensor performance characteristics, may improve the trust in a RADAR measurement of open headway distance in front of a vehicle. Specifically, several strategies for associating local traffic either within or excluded from the ego lane are considered, and the possible sources of error in headway calculations are investigated for each strategy. The strategies differ by the availability of lane geometry information and RADAR target association to lane constraints, which in turn is assumed to be supplied by the use of map data and secondary camera measurements. It is observed that combining RADAR with lane-geometry maps help detect headway up to and beyond RADAR range limit with exceptions tied to a particular road. These errors can further be minimized by using camera measurements, and this reduction in error is analyzed assuming errors associated with registering images to lane markers. Here, the results, based on actual highway routes measured with our test vehicle, reveal: that RADAR data without lane geometry strongly limits the trusted headway measurement from this sensor; that lane geometry maps can greatly increase the trusted range, usually beyond the RADAR’s range limits; and that image-based registration of lane markers to lane geometry should have accuracies allowing full-range trust in the RADAR target measurements of headway.
Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.
Chaos control of the micro-electro-mechanical resonator by using adaptive dynamic surface technology with extended state observer
This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Putting tracking differentiator into chaos controller solves the ‘explosion of complexity’ of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.