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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗
DOE OSTI2023

A compact dication source for Ba 2+ tagging and heavy metal ion sensor development

Here, we present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cadmium samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean Ba 2+ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb 2+ and Cd 2+ also demonstrated for this purpose.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Multiplicity dependence of charged-particle production in pp, p–Pb, Xe–Xe and Pb–Pb collisions at the LHC

Multiplicity (N ch ) distributions and transverse momentum (p T ) spectra of inclusive primary charged particles in the kinematic range of |η| < 0.8 and 0.15 GeV/c < p T < 10 GeV/c are reported for pp, p–Pb, Xe–Xe and Pb–Pb collisions at centre-of-mass energies per nucleon pair ranging from $\sqrt{s_{NN}}$ = 2.76 TeV up to 13 TeV. A sequential two-dimensional unfolding procedure is used to extract the correlation between the transverse momentum of primary charged particles and the charged-particle multiplicity of the corresponding collision. This correlation sharply characterises important features of the final state of a collision and, therefore, can be used as a stringent test of theoretical models. The multiplicity distributions as well as the mean and standard deviation derived from the p T spectra are compared to state-of-the-art model predictions. Providing these fundamental observables of bulk particle production consistently across a wide range of collision energies and system sizes can serve as an important input for tuning Monte Carlo event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

static-subid: Deterministic subordinate UID/GID assignment for unprivileged containers

static-subid calculates predictable subordinate UID and GID ranges for Linux users based on their UID, ensuring consistent ID mappings across multiple systems. Unlike shadow-utils' sequential allocation (which depends on creation order), static-subid uses a deterministic formula that guarantees the same user UID always receives the same subordinate ID range. Subordinate IDs enable user namespaces for unprivileged container runtimes (Podman, Docker rootless mode, LXC) by mapping container UIDs/GIDs to host subordinate IDs without requiring root privileges.

Riehecky, Pat [Fermi National Accelerator Laborato↗
DOE OSTI2025

Search for heavy neutral resonances decaying to tau lepton pairs in proton-proton collisions at $\sqrt{s}$=13 TeV

A search for heavy neutral gauge bosons (𝑍′) decaying into a pair of tau leptons is performed in proton-proton collisions at $\sqrt{s}$ =13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 fb −1 . The observations are found to be in agreement with the expectation from standard model processes. Limits at 95% confidence level are set on the product of the 𝑍′ production cross section and its branching fraction to tau lepton pairs for a range of 𝑍′ boson masses. For a narrow resonance in the sequential standard model scenario, a 𝑍′ boson with a mass below 3.5 TeV is excluded. This is the most stringent limit to date from this type of search.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

Engineering↗
DOE OSTI2024

Self-Cleaning Highly Porous TiO 2 Coating Designed by Swelling-Assisted Sequential Infiltration Synthesis (SIS) of a Block Copolymer Template

Photocatalytic self-cleaning coatings with a high surface area are important for a wide range of applications, including optical coatings, solar panels, mirrors, etc. Here, we designed a highly porous TiO 2 coating with photoinduced self-cleaning characteristics and very high hydrophilicity. This was achieved using the swelling-assisted sequential infiltration synthesis (SIS) of a block copolymer (BCP) template, which was followed by polymer removal via oxidative thermal annealing. The quartz crystal microbalance (QCM) was employed to optimize the infiltration process by estimating the mass of material infiltrated into the polymer template as a function of the number of SIS cycles. This adopted swelling-assisted SIS approach resulted in a smooth uniform TiO 2 film with an interconnected network of pores. The synthesized film exhibited good crystallinity in the anatase phase. The resulting nanoporous TiO 2 coatings were tested for their functional characteristics. Exposure to UV irradiation for 1 h induced an improvement in the hydrophilicity of coatings with wetting angle reducing to unmeasurable values upon contact with water droplets. Furthermore, their self-cleaning characteristics were tested by measuring the photocatalytic degradation of methylene blue (MB). The synthesized porous TiO 2 nanostructures displayed promising photocatalytic activity, demonstrating the degradation of approximately 92% of MB after 180 min under ultraviolet (UV) light irradiation. Thus, the level of performance was comparable to the photoactivity of commercial anatase TiO 2 nanoparticles of the same quantity. Our results highlight a new robust approach for designing hydrophilic self-cleaning coatings with controlled porosity and composition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Vapor Infiltration Synthesis of Indium Sulfide Magic Size Cluster

The energetically favorable formation of atomically precise clusters, known as magic size clusters, in the solution phase enables a precision nanoscale synthesis with exquisite uniformity. Here, we report the synthesis of magic size clusters via vapor infiltration of atomic layer deposition precursors directly in a polymer thin film. Sequential infiltration of trimethylindium vapor and hydrogen sulfide gas into poly(methyl methacrylate) leads to the formation of clusters with uniform properties consistent with a magic size cluster-In 6 S 6 (CH 3 ) 6 . While an increase in cluster size might be expected with additional sequential infiltration cycles of the reactive In and S precursors, uniform properties consistent with magic size clusters form in multiple polymers under a range of processing conditions. Ultraviolet-visible absorption spectra of In 6 S 6 (CH 3 ) 6 are largely independent of the number of sequential infiltration cycles and exhibit air stability, both of which are attributed to an energetically favorable synthetic pathway that is evaluated with density functional theory.

atomic layer deposition↗
DOE OSTI2023

Anthropogenic uranium signatures in turtles, tortoises, and sea turtles from nuclear sites

Chelonians (turtles, tortoises, and sea turtles) grow scute keratin in sequential layers over time. Once formed, scute keratin acts as an inert reservoir of environmental information. For chelonians inhabiting areas with legacy or modern nuclear activities, their scute has the potential to act as a time-stamped record of radionuclide contamination in the environment. Here, we measure bulk (i.e., homogenized scute) and sequential samples of chelonian scute from the Republic of the Marshall Islands and throughout the United States of America, including at the Barry M. Goldwater Air Force Range, southwestern Utah, the Savannah River Site, and the Oak Ridge Reservation. We identify legacy uranium ( 235 U and 236 U) contamination in bulk and sequential chelonian scute that matches known nuclear histories at these locations during the 20th century. Our results confirm that chelonians bioaccumulate uranium radionuclides and do so sequentially over time. This technique provides both a time series approach for reconstructing nuclear histories from significant past and present contexts throughout the world and the ability to use chelonians for long-term environmental monitoring programs (e.g., sea turtles at Enewetok and Bikini Atolls in the Republic of the Marshall Islands and in Japan near the Fukushima Daiichi reactors).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Autodifferentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Here, our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.

autodifferentiation↗
DOE OSTI2020

Polycaprolactone: A Promising Addition to the Sequential Infiltration Synthesis Polymer Family Identified through In Situ Infrared Spectroscopy

Infiltration of inorganic oxides inside polymers using sequential infiltration synthesis (SIS) is an effective method for creating materials for a broad range of applications. The reactions between various polymer functional groups and organometallic/inorganic precursors are unique, which makes it essential to understand the specific interactions for a range of precursors and polymers to enable predictive process design and to extend the utility of SIS to applications. In this paper, in situ Fourier transform infrared spectroscopy (FTIR) measurements have been performed during Al 2 O 3 and TiO 2 SIS in three different homopolymers: poly(methyl methacrylate) (PMMA), poly(epsilon-caprolactone) (PCL), and poly(2-vinylpyridine) (P2VP). From the FTIR intensity changes after precursor exposure and during the subsequent purge times, it is demonstrated quantitatively that the interaction dynamics and the stability of the intermediate complexes of these polymers with the metal precursors are substantially different. A key finding from this comparative study is that PCL interacts far more strongly with metal precursors, even though its carbonyl (C=O) and ester (C-O-R) functional groups are similar to those of the more weakly interacting PMMA. This behavior suggests that additional factors beyond the identity of the functional groups dictate how polymers interact with metal compounds in SIS. PCL, which has not previously been reported in SIS processes, may be an attractive polymeric template for implementing SIS with improved uniformity and cost-effectiveness.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Opening-mode fracturing and cementation during hydrocarbon generation in shale: An example from the Barnett Shale, Delaware Basin, West Texas

Relative timing of fracturing is a key input for predictive fracture models, but timing information for fractures is commonly difficult to obtain. In this study, we used crosscutting relations and fluid inclusion assemblage temperatures from fracture cements from a few well-documented sampled fractures, combined with a one-dimensional burial history model, to establish timing for three generations of opening-mode fractures in a Barnett Shale core from the southern part of the Delaware Basin, Pecos County, West Texas. A burial history model is presented for the cored well and matched to measured vitrinite reflectance in samples from the core, and bottomhole temperature in the well. Here, the earliest fractures (group 1) likely formed due to early fluid-expulsion events (ca. 300 Ma) and were folded during host-rock compaction. Later group 2 fractures are sealed with fibrous barite containing primary, liquid hydrocarbon inclusions (mean homogenization temperature [T h ] = –9°C) and aqueous fluid inclusions (mean T h = 108.1°C). Group 2 fractures likely formed in response to fluid overpressure associated with cracking of type II kerogen to oil. Group 3 vertical fractures are up to 2 m in height with kinematic apertures ranging from less than 0.05 to 1.4 mm, partly open, and strike dominantly 010°–020°. Sequentially trapped aqueous fluid inclusions in fracture-spanning quartz cement bridges (mean T h = 110°C in crack-seal texture and 128°C in post-crack-seal fracture cement) record fracture opening under increasing temperature, inferred to reflect increasing burial, with continued overpressuring during the Triassic to Late Cretaceous. Some group 3 fractures may have continued to fill during Cenozoic uplift.

02 PETROLEUM↗
DOE OSTI2021

srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to single-objective, where the regularization with a regularized ridge term is also introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the convergence and diversity of the Pareto frontier by the acquisition function for exploitation and exploration. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

42 ENGINEERING↗
DOE OSTI2024

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Amini, Mohammad Reza↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗
DOE OSTI2024

Sequential infiltration of two-photon polymerized 3D photonic crystals for mid-IR spectroscopic applications

Photonic crystals (PhCs) are spatially organized structures with lattice parameters equivalent to the operational wavelength of light. PhCs have been subject to extensive research efforts in the last two decades and are known for controlling light propagation with applications in sensing and time-delayed communication due to the slow-light phenomenon. Despite their exceptional properties, PhCs are difficult to fabricate using planar micromachining techniques due to their periodic structures. Techniques like two-photon stereolithography have been discussed for PhC fabrication in the literature, but the inherent disadvantage of poor refractive index (RI) contrast results in limited application. In this work, we present sequential infiltration synthesis performed on two-photon stereolithographically printed 3D PhCs for infiltration with zinc oxide to increase the RI of 3D PhCs. Further, finite element analysis was performed over a range of RI contrast values to study the change in photonic bandgap (PBG) with RI contrast. The transmission spectra were recorded on 3D PhCs before and after infiltration to demonstrate the change experimentally. An increase in the PBG width and absorbance is seen postinfiltration due to enhanced RI. This work presents the first, to our knowledge, sequentially infiltrated enhanced 3D PhC fabricated with two-photon stereolithography.

3D printing↗
DOE OSTI2023

Quantifying conditional probabilities of fish-turbine encounters and impacts

Tidal turbines are one source of marine renewable energy but development of tidal power is hampered by uncertainties in fish-turbine interaction impacts. Current knowledge gaps exist in efforts to quantify risks, as empirical data and modeling studies have characterized components of fish approach and interaction with turbines, but a comprehensive model that quantifies conditional occurrence probabilities of fish approaching and then interacting with a turbine in sequential steps is lacking. We combined empirical acoustic density measurements of Pacific herring ( Clupea pallasii ) and when data limited, published probabilities in an impact probability model that includes approach, entrainment, interactions, and avoidance of fish with axial or cross-flow tidal turbines. Interaction impacts include fish collisions with stationary turbine components, blade strikes by rotating blades, and/or a collision followed by a blade strike. Impact probabilities for collision followed by a blade strike were lowest with estimates ranging from 0.0000242 to 0.0678, and highest for blade strike ranging from 0.000261 to 0.40. Maximum probabilities occurred for a cross-flow turbine at night with no active or passive avoidance. Estimates were lowest when probabilities were conditional on sequential events, and when active and passive avoidance was included for an axial-flow turbine during the day. As expected, conditional probabilities were typically lower than analogous independent events and literature values. Estimating impact probabilities for Pacific herring in Admiralty Inlet, Washington, United States for two device types illustrates utilization of existing data and simultaneously identifies data gaps needed to fully calculate empirical-based probabilities for any site-species combination.

collision risk↗