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DOE OSTI2024

Sub-Diurnal Methane Variations on Mars Driven by Barometric Pumping and Planetary Boundary Layer Evolution

In recent years, the Tunable Laser Spectrometer within the Sample Analysis at Mars (TLS-SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub-diurnally, which is difficult to reconcile with an as-yet-unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically induced transport of methane from an underground source to the surface, modulated by temperature-dependent adsorption. The subsurface fractured-rock seepage model is coupled to a simplified 1-D atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub-diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for a MSL Curiosity sampling campaign. Our analysis suggests that there is a lower limit to the subsurface fracture density that can produce the observed methane patterns, below which the atmospheric methane variations would be out of phase with the observations. The best-performing model scenarios indicate a significant, short-lived methane pulse just prior to sunrise, the detection of which by TLS-SAM would be a potential indicator of the contribution of barometric pumping to Mars' atmospheric methane variations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft

A systems analysis was performed with experimental jet noise data, engine/aircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASAs N+2 goals for noise and performance. Model scale data from offset jets was used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called programmed lapse rate was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable range performance; one is a standard mixed-flow turbofan with a single-stage fan, and the other is a three-stream variable-cycle engine with a multi-stage fan. The engine with a single-stage fan has a lower specific thrust and is 8 to 10 EPNdB quieter for takeoff. Offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced and the bypass-to-core area ratio increases. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10 reduction in thrust just after takeoff rotation, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10 reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with additional reduction in takeoff thrust using programmed lapse rate, but studies are needed to investigate the practical limits for safety and takeoff regulations.

Turbofans↗

NASA NTRS2019

Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft

A systems analysis was performed with experimental jet noise data, engineaircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASAs N+2 goals for noise and performance. Model scale data from offset jets was used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called programmed lapse rate was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable range performance; one is a standard mixed-flow turbofan with a single-stage fan, and the other is a three-stream variable-cycle engine with a multi-stage fan. The engine with a single-stage fan has a lower specific thrust and is 8 to 10 EPNdB quieter for takeoff. Offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced and the bypass-to-core area ratio increases. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10 reduction in thrust just after takeoff rotation, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10 reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with additional reduction in takeoff thrust using programmed lapse rate, but studies are needed to investigate the practical limits for safety and takeoff regulations.

Jet Noise↗

NASA NTRS2016

Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft

A systems analysis was performed with experimental jet noise data, engine/aircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASA's N+2 goals for noise and performance. Model scale data from offset jets were used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called "programmed lapse rate" was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable mission range performance; one is a conventional mixed-flow turbofan and the other is a three-stream variable-cycle engine. Separate flow offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10% reduction in thrust just after clearing the runway, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10% reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with engine oversizing and derated takeoff, but more detailed mission studies are needed to investigate the range impacts as well as the practical limits for safety and takeoff regulations.

Turbofans↗

DOE OSTI2025

Securing Federated Learning Against Active Reconstruction Attacks

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Low Order Wavefront Sensing and Control for WFIRST Coronagraph

To maintain the required WFIRST Coronagraph starlight suppression performance in a realistic space environment, a low order wavefront sensing and control (LOWFS/C) subsystem is necessary. The LOWFS/C uses the rejected stellar light from coronagraph to sense and suppress the telescope pointing drift and jitter as well as the low order wavefront errors due to changes in thermal loading on the telescope and the rest of the observatory. In this paper we will present an overview of the low order wavefront sensing and control subsystem for the WFIRST Coronagraph and describe the WFIRST Coronagraph LOWFS function, its design, and modeled performance. We will present experimental results on a dedicated LOWFS/C testbed that show that the LOWFS/C subsystem not only can sense pointing errors better than 0.2 mas but has also experimentally demonstrated closed loop pointing error suppression with residuals better than 0.4 mas rms per axis for the vast majority of observatory reaction wheel speeds.

wavefront sensing and control↗

NASA NTRS2016

Low order wavefront sensing and control for WFIRST coronagraph

Wilson, Dan↗

DOE OSTI2023

ET-AL: Entropy-targeted active learning for bias mitigation in materials data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.

36 MATERIALS SCIENCE↗

NASA NTRS1991

PERTS: A Prototyping Environment for Real-Time Systems

We discuss an ongoing project to build a Prototyping Environment for Real-Time Systems, called PERTS. PERTS is a unique prototyping environment in that it has (1) tools and performance models for the analysis and evaluation of real-time prototype systems, (2) building blocks for flexible real-time programs and the support system software, (3) basic building blocks of distributed and intelligent real time applications, and (4) an execution environment. PERTS will make the recent and future theoretical advances in real-time system design and engineering readily usable to practitioners. In particular, it will provide an environment for the use and evaluation of new design approaches, for experimentation with alternative system building blocks and for the analysis and performance profiling of prototype real-time systems.

Liu, Jane W. S.↗

DOE OSTI2025

Enhancing heat pump water heater performance with embedded phase change materials thermal energy storage: First hour rating improvement and demand response operation

The increasing global emphasis on energy efficiency and sustainability has put heat pump water heaters (HPWHs) in the spotlight as an energy-efficient alternative to traditional water heating systems. However, their widespread adoption is limited by challenges such as insufficient First Hour Rating (FHR), suboptimal control mechanisms, and limited flexibility for demand response operations. Here, to address these limitations, this study proposes an innovative HPWH system integrated with embedded phase change material (PCM)-based thermal energy storage (TES). The research introduces a novel design and control strategy that leverages optimized PCM integration to enhance thermal storage capacity, improve hot water delivery during peak demand, and increase load-shifting potential. A combination of system modeling, performance simulation, and demand response control strategy evaluation was employed to quantify the benefits of PCM integration. Results demonstrate that the proposed PCM-TES HPWH system significantly enhances FHR, with an optimal 7.0 lb. of PCM increasing FHR by over 26 %—from 62 to 78 gal—for a standard 50-gal HPWH. Additionally, under advanced demand response operation using a preheat strategy, the system reduces the percentage of control temperature out-of-band time from 65 % (conventional HPWH) to just 11.6 %, enabling a more stable and efficient hot water supply. This research contributes a novel PCM-embedded HPWH design and control framework that addresses both performance and grid-interactivity challenges. The findings offer a viable pathway to enhancing the operational efficiency, flexibility, and grid responsiveness of residential water heating systems.

Demand response control↗

DOE OSTI2024

Insight into premixed diethoxymethane flames: Laminar burning velocities, temperatures, and emissions behaviour

Diethoxymethane ((CH 3 CH 2 O) 2 CH 2 , DEM) is a promising carbon-neutral fuel. DEM is a diether or acetal with a molecular structure similar to oxymethylene ethers (CH 3 O–(CH 2 O)n–CH 3 , OME n ). Thus, DEM can be expected to have a similar combustion behavior to OMEs, reducing harmful emissions such as NO x and particulate matter (PM) in internal combustion engines. From both experimental and kinetic modeling, fundamental studies on DEM are scarce in the literature. More studies are required to gain a detailed insight into the oxidation kinetics of DEM. Laminar burning velocity (LBV) is a critical property that allows a detailed assessment of the potential application of DEM in combustion devices. Unfortunately, the literature on the LBV of DEM is limited. Therefore, in this study we have investigated the LBV of DEM using two reactors for the first time, namely a heat flux burner and a combustion chamber. The experimental data is reported for equivalence ratio between 0.7 and 1.7, initial temperatures of 368–423 K, and initial pressure of 1–5 bar. In addition, we developed a detailed kinetic model extending our recent work of Shrestha et al. (Combust. Flame. 246 (2022) 112,426) to characterize the combustion behavior of DEM utilizing the new experimental data from this work and the literature data. Our model performs remarkably well in capturing the newly measured LBV experimental data over various experimental conditions. We found that DEM and dimethoxy methane (DMM) have similar values of LBVs (within ±1.5 cm/s) for a given condition, which indicates that intermediate chemistry governs the flame chemistry. Despite DEM being a larger molecule that is expected to have slightly lower LBVs than DMM, its effect on the measured values of LBVs is negligible. Finally, we experimentally measured NO x formation in DEM flame for the first time. The stochiometric flame has the highest NO x formation. The proposed model predicted the equivalence ratio dependence of NO x nicely. However, it overestimates the NO x formation for stoichiometric DEM/air mixtures by ~30 %. The model suggests that the thermal NO formation route is favored at lean and stochiometric conditions. In contrast, the prompt NO formation route is enhanced for rich mixtures.

10 SYNTHETIC FUELS↗

NASA NTRS1990

Relative merits of reactive and forward-look detection for wind-shear encounters during landing approach for various microburst escape strategies

The goal was to quantify the benefits of airborne forward-look windshear detection and to develop and test a candidate set of strategies for recovery from inadvertent microburst encounters during the landing approach, given the utilization of both reactive-only and forward-look windshear detection. Candidate strategies were developed and evaluated using a non-piloted simulation consisting of a simple point-mass performance model of a transport-category airplane flying through an analytical microburst model. The results indicate that the factor which most strongly effects a microburst recovery is the time at which the recovery is initiated. Forward-look alerts given 10 seconds prior to microburst entry permitted recoveries to be made with negligible altitude loss. The results also show that no single microburst scenario can be used to evaluate the relative merits of various recovery strategies. The type of alert used to initiate the recovery (reactive or forward-look) and the altitude of the microburst encounter had an effect on the type of recovery strategy that performed best. These factors may have serious implications for the design and certification of windshear systems.

Hinton, David A.↗

NASA NTRS2020

Variability of Lidar-Derived Particle Properties Over West Africa Due to Changes in Absorption: Towards an Understanding

Measurements performed in western Africa (Senegal) during the SHADOW field campaign are analyzed to show that spectral dependence of the imaginary part of the complex refractive index (CRI) of dust can be revealed by lidar-measured particle parameters. Observations in April 2015 provide good opportunity for such study, because, due to high optical depth of the dust, exceeding 0.5, the extinction coefficient could be derived from lidar measurements with high accuracy and the contribution of other aerosol types, such as biomass burning, was negligible. For instance, in the second half of April 2015, AERONET observations demonstrated a temporal decrease in the imaginary part of the CRI at 440 nm from approximately 0.0045 to 0.0025. This decrease is in line with a change in the relationship between the lidar ratios (the extinction-to-backscattering ratio) at 355 and 532 nm (S(355) and S(532)). For instance in the first half of April, S(355)∕S(532) is as high as 1.5 and the backscatter Angstrom exponent, A(β), is as low as −0.75, while after 15 April S(355)/S(532)=1.0 and A(β) is close to zero. The aerosol depolarization ratio δ(532) for the whole of April exceeded 30 % in the height range considered, implying that no other aerosol, except dust, occurred. The performed modeling confirmed that the observed S(355)∕S(532) and Aβ values match the spectrally dependent imaginary part of the refractive index as can be expected for mineral dust containing iron oxides. The second phase of the SHADOW campaign was focused on evaluation of the lidar ratio of smoke and estimates of its dependence on relative humidity (RH). For five studied smoke episodes the lidar ratio increases from 44±5 to 66±7 sr at 532 nm and from 62±6 to 80±8 sr at 355 nm, when RH varied from 25 % to 85 %. Performed numerical simulations demonstrate that observed ratio S(355)∕S(532), exceeding 1.0 in the smoke plumes, can indicate an increase in the imaginary part of the smoke particles in the ultraviolet (UV) range.

Lidar-Derived Particle Properties↗

DOE OSTI2023

An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers

The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the Nuclear-physics and Multi-Messenger Astrophysics framework NMMA. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions, and to classify electromagnetic observations and perform model selection. Here, we show an extension of the NMMA code as a first attempt of analyzing the gravitational-wave signal, the kilonova, and the gamma-ray burst afterglow simultaneously. Incorporating all available information, we estimate the radius of a 1.4M ⊙ neutron star to be $R$ $=$ $11.98^{+0.35}_{-0.04}$ km.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

14 SOLAR ENERGY↗

DOE OSTI2024

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. In conclusion, the crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

42 ENGINEERING↗

DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗