Engineering Papers⌕ Search

DOE OSTI · 1545218

A Directional Detector Response Function for Anisotropic Detectors

Abstract

For a radiation detector that is not isotropic, a directional detector response is needed to accurately account for the variation in a detector’s behavior depending on the incoming particle direction. The concept of the detector response function has been extended to include particle direction using a set of pregenerated detector responses based on the orientation of the incoming radiation and the detector. This directional detector response function (DDRF) then can be applied to the flux and current tallies computed by a Monte Carlo simulation. Validation of the new approach has been done by comparing simulated count rates processed with the DDRF to measured count rates taken with a 5.08 × 10.16 × 40.64-cm NaI(Tl) detector. The comparisons show that the applied method produces good agreement with both background and source measurements with a 137 Cs source. Furthermore, separation of the detector response generation from Monte Carlo particle transport calculations provides greater flexibility in locating single or multiple detectors without any interference in the model and also enables simulation of various models using the same detector response without the need for generating additional detector responses if the same detector is being used.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Celik, Cihangir, Peplow, Douglas E., Davidson, Gregory G., Swinney, Mathew W.. 2019-07-12. A Directional Detector Response Function for Anisotropic Detectors. https://doi.org/10.1080/00295639.2019.1631028

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗