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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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7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Thermoregulation in Erythrocebus patas - A thermal balance study

The ability of nonacclimated patas monkeys (Erythrocebus patas) to maintain a constant core temperature at six ambient temperatures from 15 to 40 C is investigated experimentally in 3 male and 3 female animals weighing 3.9-6.0 kg. The monkeys were permitted to reach equilibrium at the test Ta for at least 2 h before O2-uptake, CO2 output, weighted skin temperature (Tsk), rectal temperature (Tre), respiratory evaporative water loss (Eresp) and total evaporative water loss (Etot) were measured for 30 min. The results are presented in tables and graphs. Tsk is found to vary (from about 30.7 to about 37.2 C) with Ta, while Tre increases only from 37.6 to 38.4 C; Etot increases from about 9 to about 76 W/sq m, mainly due to eccrine sweating. Total body conductance, Tsk, and Tre are shown to be lower (the conductance significantly) at 40 C than those of rhesus monkeys. It is suggested that the enhanced heat tolerance of E. patas makes this species an appropriate subject for further studies of primate temperature regulation.

Kolka, M. A.↗
DOE OSTI2020

Materials Data on PaTa by Materials Project

PaTa is Magnesium-derived structured and crystallizes in the hexagonal P-6m2 space group. The structure is three-dimensional. Pa is bonded to six equivalent Pa and six equivalent Ta atoms to form PaPa6Ta6 cuboctahedra that share corners with eighteen equivalent PaPa6Ta6 cuboctahedra, edges with six equivalent PaPa6Ta6 cuboctahedra, edges with twelve equivalent TaPa6Ta6 cuboctahedra, faces with eight equivalent PaPa6Ta6 cuboctahedra, and faces with twelve equivalent TaPa6Ta6 cuboctahedra. All Pa–Pa bond lengths are 3.07 Å. All Pa–Ta bond lengths are 3.22 Å. Ta is bonded to six equivalent Pa and six equivalent Ta atoms to form TaPa6Ta6 cuboctahedra that share corners with eighteen equivalent TaPa6Ta6 cuboctahedra, edges with six equivalent TaPa6Ta6 cuboctahedra, edges with twelve equivalent PaPa6Ta6 cuboctahedra, faces with eight equivalent TaPa6Ta6 cuboctahedra, and faces with twelve equivalent PaPa6Ta6 cuboctahedra. All Ta–Ta bond lengths are 3.07 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

Physics↗
DOE OSTI2025

Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders

We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Diolkos: Improving ethernet throughput through dynamic port selection

In large networked systems, a sudden increase in traffic could slowdown the network significantly, impacting network quality for multiple users. We present Diolkos, a system that leverages smart switches to dynamically re-reroute data flows in response to drops in performance. In contrast to other techniques, our tool predicts the future throughput at each port in a switch if a data flow were to be sent through it, and updates which port should be taken to maximize throughput. We use several techniques to predict network switch performance on a software defined network (SDN) mimicking topologies commonly found in datacenters. Experimentally, we demonstrate the effectiveness of choosing a port to send flows through based on predicted performance. We found that using a distributed predictive technique achieves a 24% improvement over using a traditional heuristic technique.

port selection, performance enhancement, Neural Ne↗