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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2023

Extraction Kinetics of Rare Earth Elements from Ion-Adsorbed Underclays

Citric acid has been identified as an environmentally sustainable organic acid capable of leaching up to ~30% of easily accessible REEs from underclay material. An analysis of the leaching profiles was performed to discern the reaction rates, extraction efficiencies, and potential leaching mechanisms of REEs and cations of interest from ion-adsorbed underclays. The initial leaching stage follows a slow intraparticle diffusion mechanism followed by a second stage controlled by a mixed diffusion regime. The leaching profiles of Ca and P were similar to those of REEs, suggesting that REEs are most likely derived from mineral surfaces such as hydroxyapatite or crandallite rather than predominately from underclays. Fitting to a modified diffusion control model found diffusion-controlled leaching to be the primary mechanism whereas non-diffusive mechanisms made up about 22% of the extracted REEs. Gangue cations associated with underclays had less non-diffusive leaching than REE species, indicating that their leaching kinetics may be dominated by diffusion from within the material or potentially from product layer formation. Fitting to Boyd plots further indicated that REEs were leached following intraparticle diffusion control. These results have important implications for the development of more efficient and sustainable methods for extracting REEs or critical minerals from alternative feedstocks.

Prem, Priscilla↗
DOE OSTI2023

Development of a Multi Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is designing and constructing a solvent extraction test bed, Beartooth, as part of a nuclear fuel cycle stewardship initiative. The test bed will provide facilities and equipment for testing novel extraction processes and give early career scientists opportunities to gain skills in performing separations chemistry. The test bed is being uniquely designed to enable machine learning capabilities for the characterization of chemical process operations. One of the intentions of incorporating machine learning methods is to provide process operators with enhanced situational awareness for the optimization of separations techniques and possible detection of a diversion event. As a result, the objective of this Laboratory Directed Research and Development project is to develop a multi-sensor data collections system and implement machine learning techniques on signals from an existing centrifugal contactor cascade to identify equipment usage and process events. This presentation will provide an overview of sensors used and experiments conducted to date.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
DOE OSTI2025

Solvated Electrons Have Multiple Personalities in Molten Salts

P-Crosscut: Solvated Electrons Have Multiple Personalities in Molten Salts [EFRC – MSEE] Alejandro Ramos-Ballesteros;1 Hung H. Nguyen;2 Kazuhiro Iwamatsu;3 Santanu Roy;4 Vyacheslav Bryantsev;4 Michael E. Woods;1 Ruchi Gakhar;1 Phillip Halstenberg;4 Bobby Layne;5 Jay A. LaVerne;6 Claudio J. Margulis:2* and James F. Wishart5* 1Idaho National Laboratory; 2The University of Iowa; 3Hunter College; 4Oak Ridge National Laboratory; 5Brookhaven National Laboratory; 6University of Notre Dame Abstract: The solvated (eS–) is a powerful reducing agent and one of the primary products of molten salt radiolysis. In these extreme high-temperature environments, the eS– can initiate cascades of redox processes that significantly alter the physical and chemical properties of a molten salt, posing challenges for molten salt reactor (MSR) performance and longevity. Consequently, mastering the fundamental behavior of the eS– could enable the design of specific molten salt mixtures with tailored Lewis acidities for controlling the speciation and chemical reactivity of the eS–, thereby mitigating its overall impact on MSR technologies. Here, we use time-resolved electron pulse radiolysis techniques for determining chemical kinetics and transient absorption spectra, combined with ab initio molecular dynamics simulations to explore the influence of multivalent metal cations on the fundamental speciation and distribution of eS– coordination environments.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Tailoring resonant magnetic perturbation to optimize fast-ion confinement during ELM control in KSTAR

Abstract 3D resonant magnetic perturbation (RMP) is one promising way to control edge localized modes that can cause excessive material erosion of tokamak first walls. However, RMP can lead to undesired degradation of plasma confinement, including fast-particle losses, which can impact the performance and safety of the reactor. This work investigates the optimization of the poloidal spectrum of the 3D field to optimize fast ion confinement during edge localized mode (ELM) suppression. In the initial step, the validity of the modeling framework is tested against experimental data. Simulations successfully replicate an increase in poloidal limiter temperature with different poloidal spectra. Then, the simulation shows improvement of fast ion confinement with a reduction of core resonant response, while edge resonant magnetic fields are maintained above the threshold to sustain the ELM suppression. Reduction of the core resonant fields keeps the Kolmogorov–Arnold–Moser surface and reduces the fast particle losses due to the stochastic magnetic field lines. The results highlight the potential of edge localization of the resonant fields to enhance the performance of fusion reactors, but further investigation is needed to improve the validation of this approach.

3D field↗
DOE OSTI2023

Generating Synthetic Time Series Photovoltaic Data with Real-World Physical Challenges and Noise for Use in Algorithm Test and Validation

The PV Fleet Data Initiative and other projects seek the develop algorithms for automated analysis of PV time series data for extraction of statistical information and other parameters of the data such as degradation rates, soiling loss information, tracker performance, clipping or curtailment, system availability and other valuable information. While there is a vast body of PV data available for application of said extraction algorithms it is difficult to validate these algorithms because the true parameters to be extracted are not known. There has been a wide use of synthetic data in the literature for algorithm validation but this synthetic data is typically very bounded by the problem or topic at hand. The PV Fleet Data Initiative project has demonstrated that real time series PV data almost always includes a host of data quality and physical problems that, in reality, any automated PV abstraction algorithm must handle appropriately. For this reason, this work describes the development of a complex synthetic PV times series data set that includes data quality and physical problems that have been experienced in real world PV data. The various quality and physical problems are documented in the synthetic data so that users can test the validity of various PV extraction algorithms as well as develop new algorithms to solve problems this data set can support.

14 SOLAR ENERGY↗
DOE OSTI2023

Joint velocity and density reconstruction of the Universe with nonlinear differentiable forward modeling

Reconstructing the initial conditions of the Universe from late-time observations has the potential to optimally extract cosmological information. Due to the high dimensionality of the parameter space, a differentiable forward model is needed for convergence, and recent advances have made it possible to perform reconstruction with nonlinear models based on galaxy (or halo) positions. In addition to positions, future surveys will provide measurements of galaxies' peculiar velocities through the kinematic Sunyaev-Zel'dovich effect (kSZ), type Ia supernovae, the fundamental plane relation, and the Tully-Fisher relation. Here we develop the formalism for including halo velocities, in addition to halo positions, to enhance the reconstruction of the initial conditions. We show that using velocity information can significantly improve the reconstruction accuracy compared to using only the halo density field. Here we study this improvement as a function of shot noise, velocity measurement noise, and angle to the line of sight. We also show how halo velocity data can be used to improve the reconstruction of the final nonlinear matter overdensity and velocity fields. We have built our pipeline into the differentiable Particle-Mesh FlowPM package, paving the way to perform field-level cosmological inference with joint velocity and density reconstruction. This is especially useful given the increased ability to measure peculiar velocities in the near future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Optimized purification methods for metallic contaminant removal from directly recycled Li-ion battery cathodes

Metallic contaminants pose a significant challenge to the viability of directly recycling Li-ion batteries. To date, few strategies exist to selectively remove metallic impurities from mixtures of shredded end-of-life material (black mass; BM) without concurrently damaging the structure and electrochemical performance of the target active material. We herein present tailored methods to selectively ionize two major contaminants—Al and Cu—while retaining a representative cathode (LiNi 0.33 Mn 0.33 Co 0.33 O 2 ; NMC-111) intact. This BM purification process is conducted at moderate temperatures in a KOH-based solution matrix. We rationally evaluate approaches to increase both the kinetic corrosion rate and the thermodynamic solubility of Al 0 and Cu 0 , and evaluate the impact of these treatment conditions on the structure, chemistry, and electrochemical performance of NMC. Specifically, we explore the impacts of chloride-based salts, a strong chelating agent, elevated temperature, and sonication on the rate and extent of contaminant corrosion, while concurrently evaluating the effects on NMC. The reported BM purification process is then demonstrated on samples of “simulated BM” containing a practically relevant 1 wt% concentration of Al or Cu. Increasing the kinetic energy of the purifying solution matrix through elevated temperature and sonication accelerates the corrosion of metallic Al and Cu, such that ∼100% corrosion of 75 μm Al and Cu particles is achieved within 2.5 hr. Further, we determine that effective mass transport of ionized species critically impacts the efficacy of Cu corrosion, and that saturated Cl – hinders rather than accelerates Cu corrosion by increasing solution viscosity and introducing competitive pathways for Cu surface passivation. The purification conditions do not induce bulk structural damage to NMC, and electrochemical capacity is maintained in half-cell format. Testing in full cells suggests that a limited quantity of residual surface species are present after treatment, which initially disrupt electrochemical behavior at the graphite anode but are subsequently consumed. Process demonstration on simulated BM suggests that contaminated samples—which prior to treatment show catastrophic electrochemical performance—can be recovered to pristine electrochemical capacity. The reported BM purification method offers a compelling and commercially viable solution to address contamination, particularly in the “fine” fraction of BM where contaminant sizes are on the same order of magnitude as NMC and where traditional separation approaches are unfeasible. Thus, this optimized BM purification technique offers a pathway towards viable direct recycling of BM feedstocks that would otherwise be unusable.

25 ENERGY STORAGE↗
DOE OSTI2026

AWSD Reactive Burn Model for the HMX‐Based High Explosive LX‐04

An Arrhenius–Wescott–Stewart–Davis (AWSD) reactive burn model is applied to describe shock initiation and detonation properties of the HMX-based high explosive LX-04. The parameters in the model are calibrated to data from multiple sources. The thermodynamic equations of state used in the model are calibrated to a combination of thermochemical calculations for HMX and LX-04 as well as experimentally-measured cylinder expansion results for LX-04. The kinetic parameters are calibrated to velocity data from gas gun experiments performed using EDC-32—a high explosive with the same chemical composition as LX-04 but different structural properties, and scaled rate stick data for PBX 9012. The AWSD model is shown to accurately describe the shock initiation and propagation of LX-04. Very good agreement is observed between the available experimental data and the AWSD model output. The presented results constitute an accurate LX-04 reactive burn model for use in engineering-scale models and simulations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

MECHANICAL PERFORMANCE EVALUATION OF THE PRINTED CIRCUIT HEAT EXCHANGER CORE EXPERIMENTS UNDER TENSION AND PRESSURE LOADING

The printed circuit heat exchanger (PCHE) has small channels with high surface area, making them an efficient solution for next-generation nuclear plants (NGNPs). These PCHEs are fabricated through a diffusion bonding process. This fabrication step changes the microstructure of wrought metal plates. The current ASME design code does not support the PCHE design for NGNPs due to a lack of test data. Hence, there has been initiative towards elevated temperature mechanical property characterization of the diffusion bonded material. One of the most common channel shapes is a semicircular channel with sharp corners. These corners act as a stress riser at the diffusion bonding interface. Evaluating elevated temperature mechanical performance of diffusion bonded material in the presence of stress risers is an essential step towards the ASME code development of PCHE design. This study selected two specimen geometries: the first is a PCHE bar specimen for tensile loading with three rows and three columns of channels, and the second is a lab-scaled PCHE with six rows and eight columns of channels. A set of elevated temperature monotonic and cyclic tests were conducted on the PCHE bar specimen to evaluate the mechanical performance under axial tensile loadings to study the failure mechanism. The lab-scaled PCHE specimens were tested under overpressure loads at room temperature, and pressure creep and pressure creep-fatigue loadings to mimic the realistic loading conditions observed in typical NGNPs. The X-ray scans of channeled specimens show interesting observations. The test results and observations are presented in the paper.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance and economic viability assessment of a novel CO 2 adsorbent for manufacturing and integration with coal power plants

Here, this study assesses the performance and economic feasibility of a novel CO 2 adsorbent for post-combustion capture in DOE/NETL’s 650 MWnet SubC PC power plant (case B11B). Bench-scale tests showed an initial adsorption capacity of 16.3 wt%, which decreased to 12.1 wt% after 41 adsorption–desorption cycles due to induced particle aggregation by over-humidification. With a conservative adsorption capacity of 8.8 wt% and 695 adsorption–desorption cycles, an adsorbent replenishment rate of 10 tonnes/h is necessary to capture 90% of CO 2 . The breakeven sale price of the adsorbent produced at this rate is $\$$1,293/tonne, which is 40 to 80 times lower than prices for K 2 CO 3 adsorbents reported in the literature (e.g., K 2 CO 3 /TiO 2 , K 2 CO 3 /ZrO 2 ) while providing better capture performances. Sensitivity analysis reveals that increasing the plant production rate from 10 to 40 tonnes/h reduces the sale price by 8%. The study also compares the CO 2 capture cost to Cansolv, an integrated solvent-based technology. The novel adsorbent requires 2.4 GJ/tonne of CO 2 for regeneration, lower than Cansolv’s 2.7 GJ/tonne. With conservative performance estimates, the capture cost is $\$$54/tonne of CO 2 , slightly higher than Cansolv’s $\$$45/tonne. To achieve lower or comparable capture costs to Cansolv, the adsorbent should meet one of the following conditions at a commercial scale: minimum 950 cycles, 16 wt% capture capacity, 50% of the adsorbent recovery, or a reduced cost to $\$$646/tonne by upscaling the manufactury to 75 tonnes/h.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Experimental measurement of energy release from an initiating layer in an insensitive explosive

When subjected to a shock of insufficient strength to trigger prompt reaction, heterogenous condensed phase explosives can form regions where significant amounts of the explosive remain unreacted for times much greater than the reaction time of the detonating explosive. This phenomena is observed for the explosive PBX 9502 (95 wt% TATB) both for planar and oblique input shocks. Here, we build on previous results by performing cylinder expansion (CYLEX) tests where the explosive charge is comprised of a faster core of PBX 9501 (95 wt% HMX) inside a slower annulus of PBX 9502. The detonation in the faster PBX 9501 drives an oblique shock into the adjacent PBX 9502, and an annular transverse initiating layer (IL) results. In the test geometry, the IL travels steadily down the length of the test after a short run distance. At radial positions beyond the IL, an annular region of detonating PBX 9502 is observed. Using standard CYLEX test diagnostics, we infer the total energy release of this experiment. By making the assumptions that (1) the combined energy release is comprised of contributions from detonating PBX 9501, detonating PBX 9502, and the IL in the PBX 9502 and (2) mass-specific energy release for the detonating explosives is approximately the same as typically observed for each explosive, the IL energy release and reaction efficiency can be computed. Results are compared to prior results for a similar geometry, and indicate that while shock deadened PBX 9502 does not detonate promptly, it does eventually release a significant portion of its chemical potential energy over longer timescales on the order of 10 µs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

November 2019 Initial Deployment of LiDAR in the H-Canyon Exhaust Tunnel

The H-Canyon Exhaust Tunnel (CAEX) structure is periodically inspected under the Structural Integrity Program using camera equipped crawlers or poles to remotely perform visual inspections. To explore the use of enhanced inspection methods a “Proof of Concept” using the Light Detection and Ranging (LiDAR) technology was performed over a 2-day period in November 2019. The purpose of the “Proof of Concept” deployment was to confirm whether a commercially available LiDAR unit could successfully operate and remotely transmit data from the tunnel CAEX environment and whether the data would provide quantitative information to establish baseline measurements. The LiDAR performance requirement was a measurement accuracy of ± 0.25-inches over a 30-foot distance. This report documents the work activities leading to the deployment, the deployment and processing of data, lessons learned from the deployment and the post data processing methods that will be applied to future deployments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗