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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging Sequential Doping of Semiconducting Polymers to Enable Functionally Graded Materials for Organic Thermoelectrics

With the ability to modulate electronic properties through molecular doping coupled with ease in processability, semiconducting polymers are at the forefront in enabling organic thermoelectric devices for thermal energy management. In contrast to uniform thermoelectric material properties, an alternative route focuses on functionally graded materials (FGMs) where one spatially controls and optimizes transport properties across the length of a thermoelectric material. While primarily studied in the context of inorganic materials, the concept of FGMs for organic thermoelectrics has not been explored. Herein, we introduce how molecular doping of semiconducting polymers enables spatial compositional control of thin-film FGMs. Specifically, we utilize sequential vapor doping of poly[2,5-bis(3-tetradecylthiophen-2-yl) thieno [3,2-b]thiophene] (PBTTT) with the small molecule acceptor 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) to fabricate the simplest form of FGMs – double- segmented thin films. The two thin-film segments are of equal length (7.5 mm), but each set to different doping levels. Our study focuses on understanding the thermoelectric properties (Seebeck coefficient, α, and electronic conductivity, σ) and structural properties (through X-ray scattering, UV-vis-NIR spectroscopy, and Raman spectroscopy) within and across the two segments. We observe the presence of a small diffuse interfacial region of 0.5 – 1 mm between the two segments where the doping level and transport properties vary continuously. Despite the diffuse interface, the measured effective Seebeck coefficient (αeff) across the two segments is simply the average of α within each segment. Importantly, this experimental result is consistent with reported mathematical models describing αeff in graded thermoelectric materials. Finally, our results demonstrate the facile fabrication and characterization of functionally graded organic thermoelectric materials, providing guidelines for further development on more complex FGMs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗
DOE OSTI2023

Symmetric Random Butterfly Transform (SRBT) Based Preconditioner

Summary of work using Symmetric Random Butterfly Transformation (SRBT) in conjunction with Incomplete LDL T factorization as a preconditioner for FGMRES solver as a way of solving linear systems arising from interior point methods applied to power system problems. These linear systems have proven difficult to parallelize and this represents a possible route forward.

interior point optimization↗
DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats (Integration of Additive Manufacturing and Reinforced Polymer Composite)

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components. However, fabricating these metal polymer composite structures through traditional manufacturing processes limits their mechanical properties due to limited design freedom, lack of control over fiber orientation in composite parts, and poor interfacial bonding between the constituent materials. It is essential to develop a novel manufacturing process to enable high throughput production of lightweight automotive seats using metal and polymer composites. As such it is important to design the automotive seat suitable for manufacturing via this process and perform mechanical characterization on various subcomponents of the seat to ensure that the design and performance requirements provided by the auto manufacturer are met. The aim of this project is to develop a novel manufacturing technique to produce lightweight automotive seat by combining AM with conventional manufacturing processes. The car seat back panel will be designed via topology optimization and numerical simulations to minimize the overall weight while ensuring it meets all the performance requirements. The optimization of the seat back structure will be based on computational stress analysis to maximize the stiffness and minimize the weight. Materials currently used by Ford Motor Company will be adopted for a few subcomponents while the in-house composite materials will be used for the rest of the seat back. The composite and metallic materials will be tested to determine their mechanical properties as these are necessary for simulations. A novel manufacturing process will be developed to integrate AM metal inserts with discontinuous reinforced composite through large scale additive manufacturing and compression overmolding processes. The developed manufacturing technique will be used to fabricated various subcomponents suitable for the seat back design and mechanically tested to determine their properties. The manufacturing of the lightweight seat back design through this process involves integrated AM metal inserts with the composite structure for recliner connection. The manufacturing of the entire seat back which is lightweight through the novel manufacturing process will be discussed. The performance of the designed seat back will be investigated through numerical simulations and shown to meet all the requirements provided by the auto manufacturer. The final goal of developing a novel manufacturing process for lightweight automotive seats is met through design optimization of seat back, manufacturing of subcomponents, mechanical characterization, and validation through numerical simulations. The routes to achieve the final goal of the project and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. The project resulted in the development of a novel manufacturing process to integrate metal inserts with tailored polymer composite preforms through overmolding. Leveraging this proven manufacturing process, a lightweight seat back was designed through topology optimization and numerical simulations. The designed seat back uses AM metal inserts and compression overmolding of tailored polymer composite preforms obtained via large scale additive manufacturing. The metal polymer composite structures fabricated through this process exhibited enhancement in stiffness and improved ductility upon testing. Overall, the project provided an alternative design and manufacturing technique for automotive seat back that enables weight saving while meeting the safety and performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Performance Study of CXL Memory Topology

This paper presents a comprehensive evaluation of the performance impact of various Compute Express Link (CXL) memory topologies, with a particular emphasis on CXL switches, in the context of High- Performance Computing (HPC) and Large Language Model (LLM) inference workloads. Our study unveils significant performance variations across different topologies, demonstrating that certain configurations yield superior performance for specific workloads. These findings underscore the critical importance of tailored topol- ogy selection in optimizing system performance. Additionally, we address the inherent challenges associated with integrating CXL switches, including overhead considerations and routing complex- ities. Our research highlights the necessity for thorough evalua- tion methodologies to fully leverage CXL technology’s potential in contemporary computing environments. These insights provide valuable guidance for system architects and data center operators in designing and optimizing CXL-based infrastructures for diverse workload requirements.

CXL, memory, Artificial Intelligence (AI), HPC↗
DOE OSTI2023

N-dopants optimize the utilization of spontaneously formed photocharges in organic solar cells

Non-fullerene acceptors (NFAs) not only bring rapid efficiency progress to organic solar cells (OSCs) but have also generated scientific interest in re-evaluating the photocharge generation route via spontaneous or heterojunction-assisted charge separation. In this work, we propose the addition of n-dopants as a strategy to optimize the utilization of spontaneously formed photocharges in NFAs and construct ultralow donor-content dilute bulk heterojunction (BHJ) devices to validate the dopant's roles. N-dopants enhance the transport length of spontaneously formed photocharges over twofold and alleviate the bimolecular recombination with balanced carrier mobility during charge extraction. This improves the photocurrent of ultralow donor-content dilute BHJ devices by over 2.4 times. Moreover, these benefits are successfully conducted to the BHJ devices, and enhancement of conversion efficiencies is observed in both the 100 nm and 400 nm thick OSCs. Our results provide a feasible way to optimize the intrinsic photocharges in NFAs and renew the possibility of efficient single-component OSCs as inorganic photovoltaic techniques.

14 SOLAR ENERGY↗
DOE OSTI2026

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗
DOE OSTI2024

Local Markers for Crystalline Topology

Over the last few years, crystalline topology has been used in photonic crystals to realize edge- and corner-localized states that enhance light-matter interactions for potential device applications. However, the band-theoretic approaches currently used to classify bulk topological crystalline phases cannot predict the existence, localization, or spectral isolation of any resulting boundary-localized modes. While interfaces between materials in different crystalline phases must have topological states at some energy, these states need not appear within the band gap, and thus may not be useful for applications. Here, we derive a class of local markers for identifying material topology due to crystalline symmetries, as well as a corresponding measure of topological protection. As our real-space-based approach is inherently local, it immediately reveals the existence and robustness of topological boundary-localized states, yielding a predictive framework for designing topological crystalline heterostructures. In conclusion, beyond enabling the optimization of device geometries, we anticipate that our framework will also provide a route forward to deriving local markers for other classes of topology that are reliant upon spatial symmetries.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Using Mobile Charging Drones to Mitigate Battery Disruptions of Electric Vehicles on Highways

Our research explores innovative solutions to address the challenge of battery disruptions in electric vehicles (EVs) on highways. We propose a centralized fleet ownership model where a company manages a fleet of Mobile Charging Drones (MCDs) guided by a k-VRP (Vehicle Routing Problem) framework. This model is designed to tackle a multi-objective optimization issue with three primary goals: reduction of the overall operating costs, decrease in the cumulative waiting time, and minimization of the combined operating costs and waiting times. This approach extends beyond the usual VRP constraints, encompassing specific limitations for both MCDs and disrupted EVs (DEVs). Additionally, our study delves into the concept of decentralized fleet ownership through the lens of crowdsourcing. Preliminary numerical analyses indicate that the capital cost of MCDs is a significant factor on the charging service, and the system performance is sensitive to DEV owner's value of time (VOT) when VOTs are relatively low.

battery disruption↗
DOE OSTI2025

SCORE (Synthesis of COnsists as Rolling Energy microgrids) (Final Technical Report)

The SCORE toolset is an open source, web-based application to assess the impact of new powering technologies on railroad performance, specifically technologies capable of both putting power into the rail (motive force) and taking power from the rail (regeneration). SCORE’s primary goal is generate trade studies to analyze different powering and train make-up options to minimize energy usage and greenhouse gas generation. At the core of generating these trade spaces is calculating the optimal powering policy for the train given the makeup of the train, the route, and time constraints. This paper presents details on the algorithms used in SCORE to compute this powering policies that is fast and accurate, discusses its implementation in an Energy-Longitudinal Train Dynamics (E-LTD) model, compares it to naïve approaches, and demonstrates its use across a variety of train/route pairs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2021

Fermi-Löwdin-orbital self-interaction correction using the optimized-effective-potential method within the Krieger-Li-Iafrate approximation

Perdew-Zunger self-interaction correction (PZ-SIC) offers a route to remove self interaction errors on an orbital-by-orbital basis. A recent formulation of PZ-SIC by Pederson, Ruzsinszky and Perdew proposes restricting the unitary transformation to localized orbitals called Fermi-Löwdin orbitals. This formulation, called the FLOSIC method, simplifies PZ-SIC calculations and was implemented self-consistently using a Jacobi-like (FLOSIC-Jacobi) iteration scheme. In this work we implement the FLOSIC approach using the Krieger-Li-Iafrate (KLI) approximation to the optimized effective potential (OEP). We compare the results of present FLOSIC-KLI approach with FLOSIC-Jacobi scheme for atomic energies, atomization energies, ionization energies, barrier heights, polarizability of chains of hydrogen molecules etc. to validate the FLOSIC-KLI approach. The FLOSICKLI approach, which is within the realm of Kohn-Sham theory, predicts smaller energy gaps between frontier orbitals due to the lowering of eigenvalues of the lowest unoccupied orbitals. Results show that atomic energies, atomization energies, ionization energy as an absolute of highest occupied orbital eigenvalue, and polarizability of chains of hydrogen molecules between the two methods agree within 2%. Lastly the FLOSIC-KLI approach is used to determine the vertical ionization energies of water clusters.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Systemwide Planning with a Branch-and-Price Algorithm for Pavement-Marking Assessment Data Collection via the Mobile Retroreflectivity Unit Routing Model

The visibility of pavement markings is one of the most critical factors for traffic safety, and a periodical assessment plan is crucial for maintaining this function. Traditional assessment methods, such as visual windshield surveys or manual testing using handheld devices, are unsafe, time-consuming, and labor-intensive. In recent years, transportation agencies have begun to adopt the use of mobile retroreflectivity units (MRUs) for condition assessment of pavement markings. MRUs, different from other manual methods, can be utilized to collect large-scale retroreflectivity data in an efficient manner. However, no relevant research has yet proposed a mathematical optimization model for arranging the evaluation schedule and paths of MRUs. This study aims to propose a MRU routing model, and an efficient solution methodology. A branch-and-price algorithm, including column generation and branch-and-bound, was implemented. Computational experiments have been conducted based on actual tasks from the Florida MRU program for validation. In conclusion, results show that the proposed solution methodology with a set partitioning model in this study not only finds the optimal solution for problems with tasks less than 60, but also effectively narrows the solution gap to be within 1.0% for problems with tasks less than 131.

42 ENGINEERING↗
DOE OSTI2020

Production of Radiohalogens: Bromine and Astatine for Imaging and Therapy

Objective: This collaborative proposal between the University of Alabama at Birmingham (UAB), University of Wisconsin, Madison (UWisc) and the University of Pennsylvania (Penn) aims to research the efficient production of high purity, clinical grade radiohalogens for preclinical and translational applications by optimizing the cyclotron targetry and separation chemistry of 76,77Br and 211At for use in imaging and therapy. Proposed Research and Methods: During the course of this project, faculty and trainees at our three institutions will use the complementary infrastructure available to examine the 76Se(p,n)76Br, 77Se(p,n)77Br, 78Se(p,2n)77Br, natAs(α,2n)77Br ,80Kr(p,α)77Br and 209Bi(α, 2n)211At reactions as routes to the production of these valuable radiohalogens for imaging and therapy. Working together we aim to optimize both solid and gas targetry, including reclamation of expensive enriched material. We will develop high yielding recovery processes based on dry distillation and ion exchange techniques. We will also create a comprehensive set of quality control procedures to ensure we are producing our radioisotopes in the highest radiochemical and chemical purity possible. By bringing three institutions together to perform research and development we provide the unique opportunity to build an everlasting cooperative culture in the field of nuclear medicine research by enabling academic centers to delve into the fields of radiohalogen chemistry. In addition, each institution involved holds different skills and expertise and a collaborative grant will facilitate the cross-pollination of ideas, techniques, and build strong bonds that unite under the DOE mission. Additionally, our work does not stop at the completion of this proposal, rather this proposal allows us to gain the momentum needed to get our research and developmental efforts off the ground thereby creating a network of three institutions dedicated to the advancement of isotope production for medical research.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Transition metal carbides (TMCs) have attracted significant attention because of their applications toward a wide range of catalytic transformations. However, the practicality of their synthesis is still limited because of the harsh conditions in which most TMCs are prepared. Recently, a solution-phase synthesis of phase-pure a-MoC1-x nanoparticles was presented. While this synthetic route yielded nanoparticles with exceptional catalytic performance, the reaction parameter space was not explored, and catalyst throughput was not optimized for scale-up. Continuous flow platforms coupled with statistical design of experiments (DoE) can provide a powerful method for understanding the reaction parameter space for optimizations. Here, we demonstrate the use of statistical DoE in tandem with response surface methodology for a parametric screening analysis to optimize the throughput of a MoC1-x nanoparticle synthesis utilizing a millifluidic flow reactor. A full factorial design was implemented to evaluate four input variables (reaction temperature, flow rate, solvent fraction of oleylamine, and precursor concentration) that carry statistically significant effects on three responses (throughput, residence time, and isolated yield). A Doehlert matrix was implemented to investigate each significant variable at a higher number of levels to optimize throughput. Our results give a nonintuitive set of experimental conditions that resulted in an optimized throughput of 2.2 g h-1. This translates to a 50-fold increase in throughput compared to the previously reported batch method. The catalytic performance of the MoC1-x nanoparticles produced under optimized throughput was demonstrated in the CO2 hydrogenation reaction. This DoE screening analysis and throughput optimization of MoC1-x synthesis open the door to an increased feasibility for scale-up.

design of experiments↗