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DOE OSTI2025

Combined TDLAS and chemiluminescence imaging in a flat flame burner operated with NH3/H2 blends

Ammonia is viewed as a viable hydrogen carrier due to favorable storage and transport characteristics. While it can be re-converted to hydrogen at point-of-use via thermal catalytic cracking, direct utilization in combustion systems can result in reduced costs and improved efficiency. A major barrier to this approach is the low flammability and potential for high nitrogen oxide emissions, driven by fuel-bound nitrogen and complex kinetic pathways. While a number of kinetic mechanisms currently exist for simulating ammonia combustion, a major need continues to be direct information about species profiles in easy-to-model systems capable of isolating chemical kinetics from multi-dimensional fluid dynamic effects. This paper reports on recent species measurements made in a flat flame burner using a combined tunable-diode-laser-absorption-spectroscopy (TDLAS) and chemiluminescence imaging approach. Three flame conditions were included representative of NH3/H2 blends, partially cracked NH3 (inc. N2), and 100% NH3 with enhanced air (30% oxygen). Two NIR distributed feedback (DFB) laser diodes were used to determine H2O concentration and temperature (via ratio thermometry) at various radial and axial positions, after which an inverse Abel transform was used to infer centerline values. Companion images were collected using a Princeton Instrument PI-MAX intensified camera equipped with a 105mm UV lens and multiple filter sets targeting OH*, NH*, and NH2* emission. Results were compared to companion Cantera burner-stabilized flame simulations using various kinetic mechanisms. A recent mechanism including excited species chemistry was also included, to investigate whether excited and ground state profiles exhibit significant differences.

ammonia combustion↗

NASA NTRS1993

Rough surface scattering from moving ocean surfaces as an indicator of scattering mechanisms

Ever since Crombie used the Doppler spectrum of RF signals scattered from the ocean surface to provide conclusive evidence for Bragg resonant scattering, scattering from the ocean surface has served as a testing ground for the identification of governing scattering mechanisms in rough surface scattering. The moving ocean surface has the advantage for rough surface scattering that, being a dispersive medium, features with different spatial scales the tendency to travel at different speeds. Thus, by looking at the temporal signature of the scattered field, one can try to isolate the scattering mechanism. This feature has been exploited by various authors. To give a recent example, we cite the work of Plant, who examined the Doppler spectrum of the return signal to infer the presence of two-scale scattering in scattering at medium incidence angles. The problem with restricting oneself to the examination of the Doppler spectrum, as has been done traditionally, is that one restricts the phenomena under investigation to temporally homogeneous phenomena: transient phenomena, which are localized in time, are not localized in frequency space. There is increasing evidence that this type of phenomena, due to specular scattering or breaking waves, may also play an important role in determining the scattering mechanism in ocean-like surfaces. To overcome this problem, we introduce the use of the Wavelet Transform to study the frequency-temporal signatures of the scattered field from moving ocean waves. We calculate this signature using various analytic scattering theories and show that the Wavelet Transform provides a useful tool for separating the different scattering mechanisms operating in scattering in ocean-like surfaces. Next we simulate realistic nonlinear moving ocean waves and calculate the temporal scattered field signature by using the method of moments and the stop-start approximation: the surface is assumed stationary during a scattering event, but moves between incident EM pulses. We compute and compare the Doppler frequency spectrum and the Wavelet Transform of the resulting signal for various types of surfaces and show how different scattering mechanisms may be isolated by this technique. Finally, we compare these numerical results against the analytical predictions in order to obtain the domain of applicability of various scattering theories for moving ocean surfaces which may include wave breaking and specular glints.

Rodriguez, Ernesto↗

DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗

NASA NTRS2004

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Boccippio, Dennis↗

NASA NTRS2006

Equations for Scoring Rules When Data Are Missing

A document presents equations for scoring rules in a diagnostic and/or prognostic artificial-intelligence software system of the rule-based inference-engine type. The equations define a set of metrics that characterize the evaluation of a rule when data required for the antecedence clause(s) of the rule are missing. The metrics include a primary measure denoted the rule completeness metric (RCM) plus a number of subsidiary measures that contribute to the RCM. The RCM is derived from an analysis of a rule with respect to its truth and a measure of the completeness of its input data. The derivation is such that the truth value of an antecedent is independent of the measure of its completeness. The RCM can be used to compare the degree of completeness of two or more rules with respect to a given set of data. Hence, the RCM can be used as a guide to choosing among rules during the rule-selection phase of operation of the artificial-intelligence system..

James, Mark↗

DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗

NASA NTRS1994

Plains tectonism on Venus: Inferences from canali longitudinal profiles

Canali-type channels on Venus show inverted profile segments; that is, large portions of the channels trend uphill. The original gradients for these channels were not horizontal, but must have progressively trended downhill. Therefore, undulation patterns imposed on the profiles have to be the result of tectonism occurring since channel formation. This implies that some of the uppermost geological units of the plains, which are coincident with canali, experienced significant postemplacement tectonic deformation. The pattern of deformation is hierarchical. Within observational limits, at least two scales of deformation are indicated. The longer scale deformation (thousands of kilometers) corresponds to large-scale basins; the shorter scale deformation (hundreds of kilometers) corresponds to ridge belts or to small-scale domes/basins. The latter features, at scales up to a few hundred kilometers, extensively deform some plain areas. Although not appearing in the topographic profiles because of resolution limitations, deformation scales of tens of kilometers or less, which mostly reflect wrinkle ridges, also overlap the longer deformation scales. The channels probably formed relatively quickly in comparison to the time scale of deformation. Canali formation is closely related to the genesis of plains, and canali profile deformation reflects tectonic processes operating at multiple scales. The lower limit rates of large-scale tectonic warping are comparable to epeirogenetic deformation rates for Earth's intraplate continental interiors.

Komatsu, Goro↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗

DOE OSTI2026

Status and plans for measurements of Neutrino-Argon interactions at ICARUS

The ICARUS experiment, utilizing Liquid Argon Time Projection Chamber (LAr TPC) technology, has been installed at Fermilab (USA), following its initial operation in Italy and subsequent refurbishment at CERN. ICARUS has successfully been taking physics data at Fermilab since June 2022. While the experiment’s primary objective is to function as the far detector of the Short Baseline Neutrino program (SBN), searching for hints of physics beyond three-flavour PMNS neutrino oscillations, ICARUS also offers other diverse physics capabilities, including searches beyond the standard model and measurements of cross-sections. In addition to being exposed to the common Booster Neutrino (BNB) beamline of the SBN experiment, ICARUS receives neutrinos from the Main Injector (NuMI) beam. Due to the off-axis angle between NuMI and ICARUS, coupled with contributions from both pion and kaon decays to neutrino fluxes, interactions of NuMI neutrinos within ICARUS can be detected over a range of several GeV in energy. Measurements of these interactions present unique opportunities to infer neutrino interaction cross sections on an argon nuclear target within an energy range that overlaps both the SBN oscillation search and a significant portion of the DUNE spectrum. This presentation will summarise the current status of ICARUS’ neutrino cross-section measurements.

Roy, Promita [Fermilab] (ORCID:0000000164958937)↗

NASA NTRS2016

Spatial and Temporal Trends in Precambrian Nitrogen Cycling: A Mesoproterozoic Offshore Nitrate Minimum

Fixed nitrogen is an essential nutrient for eukaryotes. As N2 fixation and assimilation of nitrate are catalyzed by metalloenzymes, it has been hypothesized that in Mesoproterozoic oceans nitrate was limited in offshore environments by low trace metal concentrations and high rates of denitrification in anoxic and episodically euxinic deep water masses, restricting eukaryotes to near-shore environments and limiting their evolutionary innovation. To date this hypothesis has only been tested in the Belt Supergroup (∼1.4 Ga), with results that support an onshore-offshore nitrate gradient as a potential control on eukaryote ecology. Here we present bulk nitrogen and organic carbon isotopic data from non-isochronous cross-basinal facies across the Bangemall (∼1.5 Ga) and the Roper (∼1.4–1.5 Ga) basins to better understand the extent and variability of onshore-offshore nitrogen isotope gradients in the Mesoproterozoic. Both basins show an average ∼1-2‰ enrichment in δ-15N(sub bulk) from deep to shallow facies, with a maximum range from ~1‰ offshore to +7.5‰ onshore. Unlike the Belt basin, the Bangemall and Roper basins show some offshore δ-15N(sub bulk) values that are enriched beyond the isotopic range associated with biological N2 fixation alone. This suggests a mixture of aerobic and anaerobic metabolisms offshore. In shallow waters, where δ-15N(sub bulk) enrichment peaks, an aerobic nitrogen cycle was evidently operating. Even though isotopic signatures of aerobic nitrogen cycling are seen in all parts of the Bangemall and Roper basins, our data are consistent with a lateral gradient in nitrate availability within the photic zone, with higher concentrations in near-shore environments than offshore. The variability in δ-15N(sub bulk) values in each depositional environment and the consistently low δ-15N(sub bulk) values from Mesoproterozoic units compared to the Paleoproterozoic and Neoproterozoic suggest that nitrate concentrations in the global ocean were likely low. This trend is now seen in all three Mesoproterozoic basins so far examined, and contrasts with the Paleoproterozoic and Neoproterozoic where nearly all δ-15N(sub bulk) data plot above the N2 fixation window. Thus, we propose that the Mesoproterozoic ocean was characterized by a nitrate minimum, with the lowest concentrations in offshore environments. This inference is consistent with a Mesoproterozoic O2 decline following a temporary Paleoproterozoic O2 peak, and it further supports the idea that nitrate limitation offshore may have contributed to the restriction of photosynthetic eukaryotes to near-shore environments, delaying their rise to ecological dominance until the Neoproterozoic Era.

Mesoproterozoic↗

NASA NTRS2009

Handbook for Designing MMOD Protection

Spacecraft are subject to micro-meteoroid and orbital debris (MMOD) impact damage which have the potential to degrade performance, shorten the mission, or result in catastrophic loss of the vehicle. Specific MMOD protection requirements are established by NASA for each spacecraft early in the program/project life, to ensure the spacecraft meets desired safety and mission success goals. Both the design and operations influences spacecraft survivability in the MMOD environment, and NASA considers both in meeting MMOD protection requirements. The purpose of this handbook is to provide spacecraft designers and operations personnel with knowledge gained by NASA in implementing effective MMOD protection for the International Space Station, Space Shuttle, and various science spacecraft. It has been drawn from a number of previous publications [10-14], as well as new work. This handbook documents design and operational methods to reduce MMOD risk. In addition, this handbook describes tools and equations needed to design proper MMOD protection. It is a living report, in that it will be updated and re-released periodically in future with additional information. Providing effective and efficient MMOD protection is essential for ensuring safe and successful operations of spacecraft and satellites. A variety of shields protect crew modules, external pressurized vessels and critical equipment from MMOD on the International Space Station (ISS). Certain Space Shuttle Orbiter vehicle systems are hardened from MMOD impact, and operational rules are established to reduce the risk from MMOD (i.e., flight attitudes are selected and late inspection of sensitive thermal protection surfaces are conducted to reduce MMOD impacts). Science spacecraft include specific provisions to meet MMOD protection requirements in their design (for example, Stardust & GLAST). Commercial satellites such as Iridium and Bigelow Aerospace Genesis spacecraft incorporate MMOD protection. The development of low-weight, effective MMOD protection has enabled these spacecraft missions to be performed successfully. This handbook describes these shielding techniques. For future exploration activities to the Moon and Mars, implementing high-performance MMOD shielding will be necessary to meet protection requirements with minimum mass penalty. A current area of technology development in MMOD shielding is the incorporation of sensors to detect and locate MMOD impact damage. Depending on the type of sensor the signals from the sensor can be processed to infer the location of the impact and the extent of damage. The objective of the sensors is to locate critical damage that would endanger the spacecraft or crew immediately or during reentry (such as an air leak from crew module or critical damage to thermal protection system of reentry vehicles). The information from the sensors can then be used with repair kits, patch kits, hatch closure or other appropriate remedial techniques to reduce MMOD risk.

Arnold, Jim↗

NASA NTRS2012

Model-Based Method for Sensor Validation

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Vatan, Farrokh↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

Saik Anam Siam↗

NASA NTRS2022

Mars 2020 Lander Vision System Flight Performance 1

The Mars 2020 Entry Descent and Landing (EDL) system delivered the Perseverance rover to the surface of Mars on February 18th, 2021. A large fraction of the Jezero Crater landing site was covered with landing hazards including cliffs, inescapable dune fields and rocks. These hazards were identified or inferred using orbital imagery before launch so that they could be avoided using Terrain Relative Navigation (TRN) which was composed of two parts: the Lander Vision System (LVS) and Safe Target Selection (STS). During EDL, the LVS successfully estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with Inertial Measurement Unit (IMU) data. This position estimate was used by STS to identify the safest target for landing that was also reachable given fuel and other constraints. The EDL system then used the powered descent phase to retarget to this location and land safely. The overall error between the targeted location and actual landing location was 5m which was an order of magnitude less than the 60m touchdown error requirement. This paper will describe the final tests of the LVS before launch, the checkout of the LVS during operations and the LVS performance during EDL.

Zheng, Jason↗

DOE OSTI2025

Risk Assessment in a Chemical Laboratory Following an Explosive Incident Involving a Novel Diazonium Compound: Retrospective Analysis and Lessons Learned

Diazonium compounds are synthetically useful in the production of dyes and textiles, however they are highly explosive under dry conditions. Explosion prevention becomes more difficult when new diazonium compounds are synthesized, because while some syntheses include a counterion to increase their stability, this is not always a reliable method to prevent an explosive incident. Due to the uncertainty surrounding the explosiveness of different diazonium compounds, it is important to understand how to safely clean up after an incident and how to determine when it is safe to return a laboratory to typical operational use, particularly when the incident involves a novel compound where a standard does not exist for instrument calibration. Here, an explosive event is discussed involving the synthesis of 4-bromo-benzenediazonium-2-carboxylate. Following the explosive incident and 3-step cleanup, which involved a precautionary neutralization step, samples were collected from the fume hood where the incident occurred. Because the incident involved an unstable, novel compound that is not commercially available and was deemed unsafe to resynthesize for instrument calibration, we assessed the risk of further explosion by analyzing for the stable decomposition products. Mass spectrometry analysis confirmed that the residue in the fume hood contained 5-bromosalicylic acid, a decomposition product of 4-bromo-benzenediazonium-2-carboxylate. Samples were taken from multiple points in the fume hood and analyzed to estimate the spatial distribution of the decomposition product. Based on this analysis, we inferred that the primary decomposition product was far more abundant than residual energetic, indicating the energetic had been consumed or neutralized to a trace quantity where the risk of further explosion was low. Furthermore, the steps presented here─specifically, initial neutralization and then analyzing the spatial distribution of expected decomposition products to assess risk when a novel explosive material is detonated in a confined space─were our approach to assess further risk following an explosion due to a novel diazonium compound without the need for any further handling or resynthesis of the energetic. Here, we present our approach and critically analyze these steps by discussing retrospective lessons learned and alternative analytical approaches.

Computer simulations↗

DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗

NASA NTRS2012

Observations During GRIP from HIRAD: Images of C-Band Brightness Temperatures and Ocean Surface Wind Speed and Rain Rate

HIRAD (Hurricane Imaging Radiometer) flew on the WB-57 during NASA s GRIP (Genesis and Rapid Intensification Processes) campaign in August - September of 2010. HIRAD is a new C-band radiometer using a synthetic thinned array radiometer (STAR) technology to obtain cross-track resolution of approximately 3 degrees, out to approximately 60 degrees to each side of nadir. By obtaining measurements of emissions at 4, 5, 6, and 6.6 GHz, observations of ocean surface wind speed and rain rate can be inferred. This technique has been used for many years by precursor instruments, including the Stepped Frequency Microwave Radiometer (SFMR), which has been flying on the NOAA and USAF hurricane reconnaissance aircraft for several years. The advantage of HIRAD over SFMR is that HIRAD can observe a +/- 60-degree swath, rather than a single footprint at nadir angle. Results from the flights during the GRIP campaign will be shown, including images of brightness temperatures, wind speed, and rain rate. To the extent possible, comparisons will be made with observations from other instruments on the GRIP campaign, for which HIRAD observations are either directly comparable or are complementary. Features such as storm eye and eyewall, location of vortex wind and rain maxima, and indications of dynamical features such as the merging of a weaker outer wind/rain maximum with the main vortex may be seen in the data. Potential impacts on operational ocean surface wind analyses and on numerical weather forecasts will also be discussed.

Miller, Timothy L.↗