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DOE OSTI2025

Quantifying health benefits of sustainable aviation fuels: Modeling decreased ultrafine particle emissions and associated impacts on communities near the Seattle-Tacoma International Airport

Exposure to ultrafine particles (UFP, ≤100 nm) is an emerging health concern linked to premature mortality, with jet fuel combustion identified as a significant source of UFPs near airports. Sustainable aviation fuel (SAF) adoption has the potential to reduce aviation-related UFPs and may particularly benefit populations who reside nearby. However, assessing aviation-specific impacts on health remains challenging due to the lack of tools capable of addressing: fine-scale exposure evaluation, novel ambient pollutants, and groups with increased exposure or susceptibility. We develop and apply a method to estimate reductions in mortality associated with aviation-related UFP reductions at the Seattle-Tacoma (SEA-TAC) International Airport under SAF adoption scenarios, with a focus on near-airport communities. Using UFP exposure surfaces generated from AERMOD modeling, flight count data, and UFP measurements, we evaluated UFP reductions under various control scenarios. We estimated mortality reductions by combining this with population data, baseline mortality, and a hazard ratio of 1.012 (95 % confidence interval: 1.010, 1.015) per interquartile range increment of 2723 particles/cm 3 . Our analysis included 412 census tracts representing almost 1.5 million adults. Baseline aviation-related UFP exposures averaged 1145 (SD: 277) particles/cm 3 . The highest baseline concentrations and subsequent reductions under SAF scenarios were near SEA-TAC. Mortality case reductions averaged between 3.1 (95 % range: 2.5–3.7) for a 5 % UFP reduction to 31.0 (24.6–37.4) for a 50 % reduction, with corresponding mortality rate reductions of 0.2 (0.2–0.3) to 2.1 (1.7–2.5) cases per 100,000 people per year. Mortality rate reductions were larger among populations residing closer to SEA-TAC, including those that were Hispanic or Latino, below-poverty, and did not identify as White. Reducing aviation-related UFPs through SAF adoption could lead to lower mortality, particularly in near-airport communities. This reproducible approach can be adapted to other settings to evaluate health benefits from aviation-related UFP reductions.

Aviation-related air pollution↗

DOE OSTI2021

Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data { X i noisy , X i exact } via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where X i noisy and X i exact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. Here, we obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

First M87 Event Horizon Telescope Results. VII. Polarization of the Ring

In 2017 April, the Event Horizon Telescope (EHT) observed the near-horizon region around the supermassive black hole at the core of the M87 galaxy. These 1.3 mm wavelength observations revealed a compact asymmetric ring-like source morphology. This structure originates from synchrotron emission produced by relativistic plasma located in the immediate vicinity of the black hole. Here we present the corresponding linear-polarimetric EHT images of the center of M87. We find that only a part of the ring is significantly polarized. The resolved fractional linear polarization has a maximum located in the southwest part of the ring, where it rises to the level of ~15%. The polarization position angles are arranged in a nearly azimuthal pattern. We perform quantitative measurements of relevant polarimetric properties of the compact emission and find evidence for the temporal evolution of the polarized source structure over one week of EHT observations. The details of the polarimetric data reduction and calibration methodology are provided. We carry out the data analysis using multiple independent imaging and modeling techniques, each of which is validated against a suite of synthetic data sets. The gross polarimetric structure and its apparent evolution with time are insensitive to the method used to reconstruct the image. These polarimetric images carry information about the structure of the magnetic fields responsible for the synchrotron emission. Their physical interpretation is discussed in an accompanying publication.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Determination of the relativistic time delay from the Mariner 9 superior conjunction data

The purpose of the analyses performed is to investigate the Mariner 9 superior conjunction range and Doppler data in order to determine the values for the relativity and solar corona time-delay parameters. This approach is complementary to the long arc normal point approach. The two methods of analysis differ insofar as: (1) This analysis investigates a much shorter arc of data. (2) This procedure deals directly with the Mariner 9 Doppler and range data; therefore it avoids the intermediate step involved in constructing pseudo-data. It is complementary to the reduction of the normal point data because it relies upon the generation of an Earth-Mars ephemeris, which is determined from the analysis of normal points as well as past optical and radar data.

Esposito, P. B.↗

NASA NTRS1983

Shuttle booster separation aerodynamics

A wind tunnel test program of some complexity was used to define the aerodynamic forces exerted on the space shuttle solid rocket boosters and orbiter/external tank during staging. In these tests, problems associated with the use of up to three models in close proximity and with the need to simulate high pressure separation motor plumes were handled in a unique and effective manner. A new method was developed for efficiently organizing data which is a function of a large number of independent variables. Data derived from the test program drastically reduced previous estimates of aerodynamic uncertainties and allowed certification of the separation system at the design maximum staging dynamic pressure. Reduction of flight data has implicitly verified the staging aerodynamics data base and its associated uncertainties.

Mark K. Craig↗

NASA NTRS1985

A tomographic technique for aerodynamics at transonic speeds

Computer aided tomography (CAT) provides a means of noninvasively measuring the air density distribution around an aerodynamic model. This technique is global in that a large portion of the flow field can be measured. A test of the applicability of CAT to transonic velocities was studied. A hemispherical-nose cylinder afterbody model was tested at a Mach number of 0.8 with a new laser holographic interferometer at the 2- by 2-Foot Transonic Wind Tunnel. Holograms of the flow field were taken and were reconstructed into interferograms. The fringe distribution (a measure of the local densities) was digitized for subsequent data reduction. A computer program based on the Fourier-transform technique was developed to convert the fringe distribution into three-dimensional densities around the model. Theoretical aerodynamic densities were calculated for evaluating and assessing the accuracy of the data obtained from the tomographic method.

Lee, G.↗

NASA NTRS1985

A tomographic technique for aerodynamics at transonic speeds

Lee, G.↗

NASA NTRS2013

Open Rotor Aeroacoustic Installation Effects for Conventional and Unconventional Airframes

As extensive experimental campaign was performed to study the aeroacoustic installation effects of an open rotor with respect to both a conventional tube and wing type airframe and an unconventional hybrid wing body airframe. The open rotor rig had two counter rotating rows of blades each with eight blades of a design originally flight tested in the 1980s. The aeroacoustic installation effects measured in an aeroacoustic wind tunnel included those from flow effects due to inflow distortion or wake interaction and acoustic propagation effects such as shielding and reflection. The objective of the test campaign was to quantify the installation effects for a wide range of parameters and configurations derived from the two airframe types. For the conventional airframe, the open rotor was positioned in increments in front of and then over the main wing and then in positions representative of tail mounted aircraft with a conventional tail, a T-tail and a U-tail. The interaction of the wake of the open rotor as well as acoustic scattering results in an increase of about 10 dB when the rotor is positioned in front of the main wing. When positioned over the main wing a substantial amount of noise reduction is obtained and this is also observed for tail-mounted installations with a large U-tail. For the hybrid wing body airframe, the open rotor was positioned over the airframe along the centerline as well as off-center representing a twin engine location. A primary result was the documentation of the noise reduction from shielding as a function of the location of the open rotor upstream of the trailing edge of the hybrid wing body. The effects from vertical surfaces and elevon deflection were also measured. Acoustic lining was specially designed and inserted flush with the elevon and airframe surface, the result was an additional reduction in open rotor noise propagating to the far field microphones. Even with the older blade design used, the experiment provided quantification of the aeroacoustic installation effects for a wide range of open rotor and airframe configurations and can be used with data processing methods to evaluate the aeroacoustic installation effects for open rotors with modern blade designs.

Czech, Michael J.↗

NASA NTRSDate not supplied

Analysis of Bioburden Associated with Nonmetallic Spacecraft Materials

Planetary Protection (PP) requirements help to ensure the integrity of life-detection science conducted on solar system bodies, particularly those with liquid water. Such requirements are often met by a combination of cleaning and verification steps to reduce microbial contamination (i.e. bioburden) to an acceptable limit. There are only two cleaning methods approved by the National and Space Administration (NASA), vapor hydrogen peroxide (VHP) and heat microbial reduction (HMR), the latter of which is the most commonly employed. However, HMR can be harsh and produce its own concerns about material integrity, like aging or increasing hardness. When bioburden reduction methods cannot be employed, the project must rely on assumed NASA specification values of bioburden which can cause compliance issues with strict PP bioburden limitations. This work aimed to more accurately define the risk of contamination of a target body by providing empirical data associated with raw materials commonly used in solid rocket motors for lander missions. In this study, we pulverized nonmetallic materials using a cryogenic grinder, then analyzed the resulting substrate for microbial colony forming units (CFU). We found that many nonmetallic materials do not harbor large bioburden when compared with the NASA specification values, though a range existed depending on the material, treatment, and handling. The results from this work provide quantitative data to help reduce concerns of contamination, while also providing a foundation for follow up studies into additional sterilization methods and molecular identification of contaminating microbes.

Planetary Protection↗

DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗

DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗

DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗

NASA NTRS1993

Chemical kinetic studies of atmospheric reactions using tunable diode laser spectroscopy

IR absorption using tunable diode laser spectroscopy provides a sensitive and quantitative detection method for laboratory kinetic studies of atmospheric trace gases. Improvements in multipass cell design, real time signal processing, and computer controlled data acquisition and analysis have extended the applicability of the technique. We have developed several optical systems using off-axis resonator mirror designs which maximize path length while minimizing both the sample volume and the interference fringes inherent in conventional 'White' cells. Computerized signal processing using rapid scan (300 kHz), sweep integration with 100 percent duty cycle allows substantial noise reduction while retaining the advantages of using direct absorption for absolute absorbance measurements and simultaneous detection of multiple species. Peak heights and areas are determined by curve fitting using nonlinear least square methods. We have applied these techniques to measurements of: (1) heterogeneous uptake chemistry of atmospheric trace gases (HCl, H2O2, and N2O5) on aqueous and sulfuric acid droplets; (2) vapor pressure measurements of nitric acid and water over prototypical stratospheric aerosol (nitric acid trihydrate) surfaces; and (3) discharge flow tube kinetic studies of the HO2 radical using isotopic labeling for product channel and mechanistic analysis. Results from each of these areas demonstrate the versatility of TDL absorption spectroscopy for atmospheric chemistry applications.

Worsnop, Douglas R.↗

DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A Cross-Domain Optimization Framework of PMU and Communication Placement for Multidomain Resiliency and Cost Reduction

Phasor measurement units (PMUs) play a crucial role in real-time monitoring and control of power grids. They rely on a communication network to transfer measurement data to the phasor data concentrator (PDC) for further processing and analysis. In this paper, a resilient cross-domain PMU and communication link placement method for minimizing the overall installation cost of the wide-area measurement system (WAMS) is proposed. Here, the main idea is to break down the barrier between the power grid domain and the communication domain, and consider the impact of one when design the other. The PMU placement in the power grid domain takes into account the cost of communication links by generating multiple solutions with equally minimum PMU costs for communication link placement evaluation. On the other hand, the communication link placement problem reduces the cost by customizing the routing policies based on the different roles of PMUs in grid observability. The proposed WAMS design is capable of withstanding any single component failure in the power domain (PMU failure or power branch failure) or in the communication domain (communication link failure or PDC failure). Numerical study on the IEEE 57-bus system reveals that the developed cross-domain optimization framework can significantly reduce the overall installation cost of WAMS while attaining multi-domain resiliency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Signal Extraction and Simulations for n -> p^0 y y and n -> p+p-e+e- Decays at the Jefferson Lab Eta Factory

The Jefferson Lab eta Factory (JEF) began acquiring data in early 2025. The experiment aims to give insight into the connection between Dark Matter physics models and the Standard Model by investigating rare decay processes of n and n' mesons. Several other physics motivations are also a key factor in the experiment, such as probing C and/or P violation and aspects of chiral perturbation theory. For these purposes, the forward calorimeter of the GlueX experiment in Jefferson Lab was upgraded so that it provides greater positional and energy resolution. Understanding physics-motivated cuts and background removal methods is of great importance to achieving JEF goals. Several methods have been implemented to obtain invariant mass plots for the “golden” channel of interest ¿ ¿ p0¿¿, while channels such as ¿ ¿ p+p-e+e- open a promising window into CP-violating physics. This thesis work shows a sig nificant background reduction in rare decay channels of interest, asymmetry factors comparable to recent experimental measurements, an evaluation on which analysis cuts to use after data acquisition and the likelihood of probing specific rare ¿ decays. Despite background rejection from obstructing decay channels, much remains to do to extract the p0¿¿ final-state. The asymmetry between the pion and lepton planes looks promising for p+p-e+e-; simulations show that the asymmetry is consistent with zero (no instrumental asymmetry), and the next step should include generators that model the physics of the asymmetry. This thesis work may help in the effort of probing CP-violating physics or solving the mysteries between “beyond-Standard Model” and our current understanding of physics.

Oresic, Stjepan [Univ. of Regina, SK (Canada)]↗

DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗