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DOE OSTI2025

Data Agnostic Feature-Target Analysis & Ranking Machine Learning Pipeline (DAFTAR-ML) v0.1.0

DAFTAR-ML is a specialized machine-learning pipeline that identifies relevant features based on their relationship to a target variable. Many ML pipelines focus solely on prediction, and feature ranking is often absent or lacks robust statistical methods. DAFTAR-ML performs its tasks with this outcome in mind. Model training is robust, using nested cross-validation and hyperparameter tuning. Instead of relying on native feature-importance scores, it employs SHAP (SHapley Additive exPlanations) to quantify feature importance. The pipeline also produces comprehensive results, including publication-quality visualizations.

Melie, Tina [Lawrence Berkeley National Laboratory↗

DOE OSTI2020

Online Modelling and Optimization of Nonlinear Integrable Systems

Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Laser Intensity Profile Effects on the Solidification Microstructure of Ti-6Al-4V and Ti-5553

Traditional machining of Ti-6Al-4V and Ti-5Al-5Mo-5V-3Cr (Ti-5553) is challenging due to high chemical reactivity, adiabatic shear band formation, and material cost. Titanium is attractive because of its high tensile strength, low density, corrosion resistance, and ability to withstand extreme temperatures. Selective Laser Melting (SLM) is capable of manufacturing geometrically complicated parts at a lower expense. Exceptional tolerances can be obtained with properly tuned processing parameters. Alterations on scan parameters permit microstructure and melt pool control. Single track laser melting experiments were performed on Ti-6Al-4V and Ti- 5553 using elliptical intensity profiles. Results show that maintaining constant energy density but increasing power results in a melt pool failing to penetrate the substrate for Ti-5553. Increasing laser energy density increases melt pool width and can increase powder bed denudation. Comparing Ti64 and Ti-5553 microstructures contributes to a better understanding of rapid solidification in titanium.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Observation of a Synchro-Betatron Instability in Fermilab Booster

In preparation for PIP-II, there has been interest in running the Fermilab Booster at a higher current more indicative of the PIP-II era operation. In July 2023, an experiment was performed to study collective instabilities over the transition crossing at the Fermilab Booster. Over the transition crossing, the synchrotron tune becomes small and synchro-betatron instabilities become possible. During the experiment, an intensity threshold was observed, above which a dipole instability with losses concentrated in the tail of the bunch. These losses are consistent with the Convective Instability

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗

NASA NTRS1984

Flight test configuration for verifying inertial sensor redundancy management techniques

The Redundant Strapdown Inertial Measurement Unit presently tested in flight configuration consists of a semioctahedral array of four dynamically tuned, two-degree-of-freedom (TDOF) gyros and four TDOF accelerometers which can provide dual, fail-operational performance for integrated avionics systems. Attention is given to the multilevel algorithm used for the detection and isolation of three ranges of sensor failure in an integrated avionics context. A technique for the generation of accelerometer and gyro error thresholds which is sensitive to dynamic sensor errors and separation effects is presented, together with simulation results. Emphasis is placed on the ensuring of highly reliable data for flight control/navigation functions, while minimizing false or missed alarms.

Bryant, W. H.↗

NASA NTRS1989

Self-tuning Generalized Predictive Control applied to terrain following flight

Generalized Predictive Control (GPC) describes an algorithm for the control of dynamic systems in which a control input is generated which minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. Self-tuning GPC refers to an implementation of the GPC algorithm in which the parameters of the internal model(s) are estimated on-line and the predictive control law tuned to the parameters so identified. The self-tuning GPC algorithm is applied to a problem of rotorcraft longitudinal/vertical terrain-following flight. The ability of the algorithm to tune to the initial vehicle parameters and to successfully adapt to a stability augmentation failure is demonstrated. Flight path performance is compared to a conventional, classically designed flight path control system.

Hess, R. A.↗

NASA NTRS1992

Robust stabilization, robust performance, and disturbance attenuation for uncertain linear systems

This paper presents a linear quadratic regulator approach to the robust stabilization, robust performance, and disturbance attenuation of uncertain linear systems. The state-feedback designed systems provide both the robust stability with optimal performance and the disturbance attenuation with H-infinity-norm bounds. The proposed approach can be applied to matched and/or mismatched uncertain linear systems. For a matched uncertain linear system, it is shown that the disturbance attenuation robust-stabilizing controllers with or without optimal performance always exist and can be easily determined without searching; whereas, for a mismatched uncertain linear system, the introduced tuning parameters greatly enhance the flexibility of finding the disturbance-attenuation robust-stabilizing controllers.

Wang, Yeih J.↗

NASA NTRS1992

A 100 Mbps resonant cavity phase modulator for coherent optical communications

A resonant cavity electro-optic phase modulator has been designed and implemented to operate at a data rate of 100 Mbps. The modulator consists of an electro-optic crystal located in a highly resonant cavity. The cavity is electro-optically tuned on and off resonance, and the phase dispersion near the cavity resonance provides the output phase modulation. The performance of the modulator was measured by first heterodyne detecting the signal to an intermediate frequency and then measuring the spectral characteristics using an RF spectrum analyzer. The measured phase shift is shown to be in good agreement with the theoretical predictions.

Chen, Chien-Chung↗

NASA NTRS1996

Turbulent flame propagation in partially premixed flames

Turbulent premixed flame propagation is essential in many practical devices. In the past, fundamental and modeling studies of propagating flames have generally focused on turbulent flame propagation in mixtures of homogeneous composition, i.e. a mixture where the fuel-oxidizer mass ratio, or equivalence ratio, is uniform. This situation corresponds to the ideal case of perfect premixing between fuel and oxidizer. In practical situations, however, deviations from this ideal case occur frequently. In stratified reciprocating engines, fuel injection and large-scale flow motions are fine-tuned to create a mean gradient of equivalence ratio in the combustion chamber which provides additional control on combustion performance. In aircraft engines, combustion occurs with fuel and secondary air injected at various locations resulting in a nonuniform equivalence ratio. In both examples, mean values of the equivalence ratio can exhibit strong spatial and temporal variations. These variations in mixture composition are particularly significant in engines that use direct fuel injection into the combustion chamber. In this case, the liquid fuel does not always completely vaporize and mix before combustion occurs, resulting in persistent rich and lean pockets into which the turbulent flame propagates. From a practical point of view, there are several basic and important issues regarding partially premixed combustion that need to be resolved. Two such issues are how reactant composition inhomogeneities affect the laminar and turbulent flame speeds, and how the burnt gas temperature varies as a function of these inhomogeneities. Knowledge of the flame speed is critical in optimizing combustion performance, and the minimization of pollutant emissions relies heavily on the temperature in the burnt gases. Another application of partially premixed combustion is found in the field of active control of turbulent combustion. One possible technique of active control consists of pulsating the fuel flow rate and thereby modulating the equivalence ratio (Bloxsidge et al. 1987). Models of partially premixed combustion would be extremely useful in addressing all these questions related to practical systems. Unfortunately, the lack of a fundamental understanding regarding partially premixed combustion has resulted in an absence of models which accurately capture the complex nature of these flames. Previous work on partially premixed combustion has focused primarily on laminar triple flames. Triple flames correspond to an extreme case where fuel and oxidizer are initially totally separated (Veynante et al. 1994 and Ruetsch et al. 1995). These flames have a nontrivial propagation speed and are believed to be a key element in the stabilization process of jet diffusion flames. Different theories have also been proposed in the literature to describe a turbulent flame propagating in a mixture with variable equivalence ratio (Muller et al. 1994), but few validations are available. The objective of the present study is to provide basic information on the effects of partial premixing in turbulent combustion. In the following, we use direct numerical simulations to study laminar and turbulent flame propagation with variable equivalence ratio.

Poinsot, T.↗

NASA NTRS2000

Coherent Doppler Laser Radar: Technology Development and Applications

NASA's Marshall Space Flight Center has been investigating, developing, and applying coherent Doppler laser radar technology for over 30 years. These efforts have included the first wind measurement in 1967, the first airborne flights in 1972, the first airborne wind field mapping in 1981, and the first measurement of hurricane eyewall winds in 1998. A parallel effort at MSFC since 1982 has been the study, modeling and technology development for a space-based global wind measurement system. These endeavors to date have resulted in compact, robust, eyesafe lidars at 2 micron wavelength based on solid-state laser technology; in a factor of 6 volume reduction in near diffraction limited, space-qualifiable telescopes; in sophisticated airborne scanners with full platform motion subtraction; in local oscillator lasers capable of rapid tuning of 25 GHz for removal of relative laser radar to target velocities over a 25 km/s range; in performance prediction theory and simulations that have been validated experimentally; and in extensive field campaign experience. We have also begun efforts to dramatically improve the fundamental photon efficiency of the laser radar, to demonstrate advanced lower mass laser radar telescopes and scanners; to develop laser and laser radar system alignment maintenance technologies; and to greatly improve the electrical efficiency, cooling technique, and robustness of the pulsed laser. This coherent Doppler laser radar technology is suitable for high resolution, high accuracy wind mapping; for aerosol and cloud measurement; for Differential Absorption Lidar (DIAL) measurements of atmospheric and trace gases; for hard target range and velocity measurement; and for hard target vibration spectra measurement. It is also suitable for a number of aircraft operations applications such as clear air turbulence (CAT) detection; dangerous wind shear (microburst) detection; airspeed, angle of attack, and sideslip measurement; and fuel savings through headwind minimization. In addition to the airborne and space platforms, a coherent Doppler laser radar system in an unmanned aerial vehicle (UAV) could provide battlefield weather and target identification.

Kavaya, Michael J.↗

DOE OSTI2024

Observation of a Synchro-betatron Instability in Fermilab Booster

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Tuning the Mechanical Properties of a Polymer Semiconductor by Modulating Hydrogen Bonding Interactions

Conjugation breakers (CBs) with different H-bonding chemistry and linker flexibility are designed and incorporated into a Diketopyrrolopyrrole (DPP)-based conjugated polymer backbone. The effects of H-bonding interactions on polymer semiconductor morphology, mechanical properties and electrical performance are systematically investigated. We observe that CBs with H-bonding self-association constant >0.7 or denser packing tendency are able to induce higher polymer chain aggregation and crystallinity in as-casted thin film, resulting in higher modulus and crack on-set strain. Additionally, the rDoC (relative degree of crystallinity) of the stretched thin film with the highest crack on-set strain only suffers a small decrease, suggesting the main energy dissipation mechanism is the breakage of H-bonding interactions. By contrast, other less stretchable polymer films dissipate strain energy through the breakage of crystalline domains, indicated by drastic decrease in rDoC. Furthermore, we evaluate their electrical performance under mechanical strain in fully stretchable field-effect transistors. The polymer with the highest crack on-set strain has the least degradation in mobility as a function of strain. Overall, these observations suggest that we can aptly tune the mechanical properties in polymer semiconductors by modulating intermolecular interactions, such as H-bonding chemistry and linker flexibility. Such understanding provides molecular design guidelines for future stretchable semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2025

Tuning effect of vanadium substitution on the structural and electronic properties of potassium hollandite surfaces

Metal oxide surfaces possess unique properties that are crucial for a wide variety of applications. Herein, density functional theory calculations are performed to study surfaces of potassium hollandite, KMn 8 O 16 , a promising cathode material for electrochemical energy storage, and the vanadium-substituted analog KMn 7 VO 16 . The results show that there is a clear increase in the stability of KMn 8 O 16 with (001) < (110) < (100) or (010), apt to adopt an elongated rod-like morphology. The vanadium (V)-substitution lowers the crystal symmetry and prefers to occupy the surface sites, resulting in electron redistribution and selective tuning of surface energy depending on the surface structures. In particular, the higher stability of substituted V 4+ compared with Mn 4+ ions leads to stabilization of the (001) surface due to the direct interaction of reduced Mn δ+ ions on the surface, while such tuning effect decreases with the increase in surface stability, (110) > (100) and (010). As a result, the KMnO 16 rod is shortened upon V-substitution as observed experimentally, effectively facilitating the ion transport during discharge. The V substituents also introduce stabilization to the defect surfaces resulting from Mn 2+ dissolution during cycling, thereby hindering further structural decay. In conclusion, our study demonstrates the potential tuning effect of V-substitution to promote the ion transport and mitigate the capacity degradation of α-MnO 2 -based materials.

25 ENERGY STORAGE↗

NASA NTRSDate not supplied

Reconfigurable Metasurface Optics at NASA LaRC Towards Space Image Sensing

Reconfigurable optical metasurfaces are rapidly emerging as a major frontier in photonics research, development, and applications. They promise compact, lightweight, and energy-efficient reconfigurable optical systems with unprecedented performance and functions that can be dynamically defined on-demand. Space applications represent an emerging area in which these characteristics are highly prized. The ability to dynamically tune optical functions through selective modulation of electromagnetic waves is crucial to the advancement of a variety of sensing applications, from imaging spectrometers to light detection and ranging (LiDAR). This presentation introduces a reconfigurable metasurface optic project led by a research team at NASA Langley Research Center (NASA LaRC) since 2019. It covers advances in phase change material-based reconfigurable optics, performance data on reliability enhancement of photonic devices, image sensing system architectures, and mission concepts enabled through these advances.

metasurface↗

DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗

DOE OSTI2024

Nanoscale Probing of Electrical Memory Effects in van der Waals Layered PdSe 2

Tunable electronic materials that can be switched between different impedance states are fundamental to the hardware elements for neuromorphic computing architectures. This “brain-like” computing paradigm uses highly paralleled and colocated data processing, leading to greatly improved energy efficiency and performance compared to traditional architectures in which data have to be frequently transferred between processor and memory. In this work, we use scanning microwave impedance microscopy for nanoscale electrical and electronic characterization of two-dimensional layered semiconductor PdSe 2 to probe neuromorphic properties. The local resolution of tens of nanometers reveals significant differences in electronic behavior between and within PdSe 2 nanosheets (NSs). In particular, we detected both n-type and p-type behaviors, although previous reports only point to ambipolar n-type dominating characteristics. Nanoscale capacitance–voltage curves and subsequent calculation of characteristic maps revealed a hysteretic behavior originating from the creation and erasure of Se vacancies as well as the switching of defect charge states. In addition, stacks consisting of two NSs show enhanced resistive and capacitive switching, which is attributed to trapped charge carriers at the interfaces between the stacked NSs. Stacking n- and p-type NSs results in a combined behavior that allows one to tune electrical characteristics. In conclusion, as local inhomogeneities of electrical and electronic behavior can have a significant impact on the overall device performance, the demonstrated nanoscale characterization and analysis will be applicable to a wide range of semiconducting materials.

2D material↗