Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Route optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Co-Continuous Phase Nanofiber Composites

We report on the development of a co-continuous phase composite with a polyacrylonitrile (PAN nanofiber network integrated into an acrylonitrile-butadiene-styrene (ABS) matrix that offers improved mechanical strength. Homogeneous dispersion of nanomaterials in a matrix can be challenging and typically involves mechanical mixing or chemical modification. This work overcomes this obstacle by taking an inverse approach in that a nanoscale network is synthesized then infiltrated with polymer instead of attempting to disperse the nanomaterial in the polymer. This not only enables excellent homogeneity but also allows the nanoscale phase to be continuous throughout the composite. Here, PAN is electrospun to form a random network of nanofibers having diameters of roughly 200 nm, which is then infiltrated with ABS using solution processing. By optimizing the heat treatments and ABS concentrations, the tensile strength and elastic modulus were improved by 208% and 313%, respectively, as compared to the bare PAN nanofibers. Since there has been significant previous research efforts on electrospinning polymers, many different nanofiber and polymer systems can be made into composites using the methodology established in this work. Therefore, this research offers a new route to create a co-continuous composite with the aim to replace some existing fiber reinforced composites.

Bowland, Christopher↗
DOE OSTI2017

Fabrication and Properties of Advanced Ceramic Technetium Waste Forms - 17311

Technetium-99 (Tc, t{sub 1/2} = 2.13 x 10{sup 5} years) is a challenge from a nuclear waste perspective and is one of the most abundant, long-lived radioisotopes found in used nuclear fuel (UNF). Within the Hanford Tank Waste Treatment and Immobilization Plant, technetium volatilises at typical glass melting temperature, is captured in the off-gas treatment system and recycled back into the feed to eventually increase Tc-loadings of the glass. In this research we are providing a technical solution for continuously depleting the Tc-content of the glass melter feed by precipitating Tc from the off-gas effluents and effectively immobilizing {sup 99}Tc as durable ceramic waste form. We have developed a wet-chemical coprecipitation-based synthesis route to fabricate gram-size Tc-based pyrochlores with the model composition Sm{sub 2}(Tc{sub 0.5-x}Ru{sub x}Ti{sub 0.5}){sub 2}O{sub 7}. These ceramic Tc waste forms exhibit better technetium retention and corrosion resistance in ASTM C1220-10 based testing compared with technetium-containing LAWE4-type borosilicate glass, combined with optimal waste loading. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Development of an autonomous solvent extraction system to isolate astatine-211 from dissolved cyclotron bombarded bismuth targets

Abstract Cyclotron-produced astatine-211 ( 211 At) shows tremendous promise in targeted alpha therapy (TAT) applications due to its attractive half-life and its 100% α-emission from nearly simultaneous branched alpha decay. Astatine-211 is produced by alpha beam bombardment of naturally monoisotopic bismuth metal ( 209 Bi) via the ( α, 2n ) reaction. In order to isolate the small mass of 211 At (specific activity = 76 GBq·µg −1 ) from several grams of acid-dissolved Bi metal, a manual milliliter-scale solvent extraction process using diisopropyl ether (DIPE) is routinely performed at the University of Washington. As this process is complex and time consuming, we have developed a fluidic workstation that can perform the method autonomously. The workstation employs two pumps to concurrently deliver the aqueous and organic phases to a mixing tee and in-line phase mixer. The mixed phases are routed to a phase settling reservoir, where they gravity settle. Finally, each respective phase is withdrawn into its respective pump. However, development of a phase boundary sensor, placed in tandem with the phase settling reservoir, was necessary to communicate to the system when withdrawal of the denser aqueous phase was complete (i.e., the intersection of the two phases was located). The development and optimization of the autonomous solvent extraction system is described, and the 211 At yields from several ~1.1 GBq-level 211 At processing runs are reported.

211At↗
DOE OSTI2016

BiVO{sub 4} photoanodes for water splitting with high injection efficiency, deposited by reactive magnetron co-sputtering

Photoactive bismuth vanadate (BiVO{sub 4}) thin films were deposited by reactive co-magnetron sputtering from metallic Bi and V targets. The effects of the V-to-Bi ratio, molybdenum doping and post-annealing on the crystallographic and photoelectrochemical (PEC) properties of the BiVO{sub 4} films were investigated. Phase-pure monoclinic BiVO{sub 4} films, which are more photoactive than the tetragonal BiVO{sub 4} phase, were obtained under slightly vanadium-rich conditions. After annealing of the Mo-doped BiVO{sub 4} films, the photocurrent increased 2.6 times compared to undoped films. After optimization of the BiVO{sub 4} film thickness, the photocurrent densities (without a catalyst or a blocking layer or a hole scavenger) exceeded 1.2 mA/cm{sup 2} at a potential of 1.23 V{sub RHE} under solar AM1.5 irradiation. The surprisingly high injection efficiency of holes into the electrolyte is attributed to the highly porous film morphology. This co-magnetron sputtering preparation route for photoactive BiVO{sub 4} films opens new possibilities for the fabrication of large-scale devices for water splitting.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

ODU Optimization of Nb3Sn supttering on SRF structures

High efficiency Superconducting radio-frequency (SRF) cavities are the key technology to cost-effective Continuous Wave (CW) SRF accelerators. Recent advances in Nb3Sn coatings on the inner surface of SRF cavities showed the potential for this material to significantly cut the capital and operating cost of SRF accelerators. The best results are presently achieved with Nb3Sn films coated by using the so-called vapor diffusion technique. Several alternative approaches have been and are being investigated such as electrochemical deposition, bronze routes, and sputtering. Among these techniques, magnetron sputtering is a versatile technique, which has been demonstrated to deposit Nb3Sn films on SRF cavities. The technique requires further development to compete with the results demonstrated on the cavities coated with the vapor diffusion techniques. The team to develop the novel technique includes a graduate student from Old Dominion University (ODU) and other experts. ODU Prof. Elsayed-Ali directs the graduate student to coat samples and cavities in the new coating system and analyze samples with different surface science techniques. Dr. Grigory Eremeev from Fermilab assists with 2.6 GHz cavities for coating, cavity characterization at cryogenic temperature, and result analysis. The research is funded by the Office of High Energy Physics at the US Department of Energy at both Fermilab and ODU

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multiband Imaging Data

Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ E ≤ 1''. Visual inspection is then performed for sources with CNN scores of P lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches.

High-redshift galaxies↗
DOE OSTI2026

Optimizing Transportation Networks for E-Waste Reverse Logistics: A Multi-Modal Cost Allocation and Pricing Strategy

The exponential growth of electronic waste (e-waste) poses critical challenges for sustainable reverse logistics and transportation network optimization. This study develops a dual-channel transportation framework for e-waste logistics that integrates dynamic freight pricing, cost allocation mechanisms, and game-theoretic coordination. The model captures interactions between centralized hubs and distributed processing networks, accounting for freight rate elasticity, volume allocation, and capacity constraints. Using Stackelberg game theory and cost-sharing strategies, the framework optimizes transportation efficiency and profit distribution across logistics channels. Numerical simulations show that the dual-channel structure increases centralized hub profit by 226.8% compared to baseline single-channel operations, while boosting total transported volume by 1.2% and nearly doubling freight collector profit under cost-sharing. Scenario analyses across regional infrastructures reveal that network density, policy incentives, and logistics costs shape routing efficiency and profit allocation. These findings suggest that coordinated strategies combining dynamic pricing, targeted infrastructure investment, and strategic cost allocation are needed to design efficient, resilient, and regionally adaptable e-waste transportation systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Molecular engineering of dispersed nickel phthalocyanines on carbon nanotubes for selective CO 2 reduction

Electrochemical reduction of CO 2 is a promising route for sustainable production of fuels. A grand challenge is developing low-cost and efficient electrocatalysts that can enable rapid conversion with high product selectivity. Here we design a series of nickel phthalocyanine molecules supported on carbon nanotubes as molecularly dispersed electrocatalysts (MDEs), achieving CO 2 reduction performances that are superior to aggregated molecular catalysts in terms of stability, activity and selectivity. The optimized MDE with methoxy group functionalization solves the stability issue of the original nickel phthalocyanine catalyst and catalyses the conversion of CO 2 to CO with >99.5% selectivity at high current densities of up to -300 mA cm -2 in a gas diffusion electrode device with stable operation at -150 mA cm -2 for 40 h. The well-defined active sites of MDEs also facilitate the in-depth mechanistic understandings from in situ/operando X-ray absorption spectroscopy and theoretical calculations on structural factors that affect electrocatalytic performance.

42 ENGINEERING↗
DOE OSTI2022

Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale

Many industrial chemicals that are produced from fossil resources could be manufactured more sustainably through fermentation. In this work, we describe the development of a carbon-negative fermentation route to producing the industrially important chemicals acetone and isopropanol from abundant, low-cost waste gas feedstocks, such as industrial emissions and syngas. Using a combinatorial pathway library approach, we first mined a historical industrial strain collection for superior enzymes that we used to engineer the autotrophic acetogen Clostridium autoethanogenum. Next, we used omics analysis, kinetic modeling and cell-free prototyping to optimize flux. Finally, we scaled-up our optimized strains for continuous production at rates of up to ~3 g/L/h and ~90% selectivity. Life cycle analysis confirmed a negative carbon footprint for the products. Unlike traditional production processes, which result in release of greenhouse gases, our process fixes carbon. These results show that engineered acetogens enable sustainable, high-efficiency, high-selectivity chemicals production. We expect that our approach can be readily adapted to a wide range of commodity chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Exact Fock-State Preparation with $n^{1/4}$ Circuit Depth

Efficient, deterministic, and high-fidelity preparation of large Fock states is essential for scaling bosonic quantum technologies and exploring quantum phenomena at large excitation energies. We introduce a deterministic one-parameter (D1p) protocol that maps Fock-state preparation in an infinite-dimensional Hilbert space onto two-dimensional amplitude amplification. Starting from a coherent state with $|α|\simeq\sqrt{n}$, the initial target-state population scales as $n^{-1/2}$, yielding an iteration count and circuit depth of $\mathcal{O}(n^{1/4})$. Phase matching guarantees unit fidelity in the ideal model; remarkably, preparing $|{10^6}\rangle$ requires only 39 iterations. The protocol uses only displacements and number-selective phase operations, requires no numerical optimization, and further extends to state transfer, general superpositions, finite-dimensional systems, and multipartite entangled states. In the large-amplitude regime, its multi-target form prepares $L$-legged cat states with an iteration count determined only by $L$; cats with up to ten legs require only two iterations, independent of the coherent-state amplitude. This framework provides a broadly applicable route to highly excited bosonic states on platforms supporting these elementary controls.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2019

The Poisson–Boltzmann model for implicit solvation of electrolyte solutions: Quantum chemical implementation and assessment via Sechenov coefficients

Here, we present the theory and implementation of a Poisson-Boltzmann implicit solvation model for electrolyte solutions. This model can be combined with arbitrary electronic structure methods that provide an accurate charge density of the solute. A hierarchy of approximations for this model includes a linear approximation for weak electrostatic potentials, finite size of the mobile electrolyte ions, and a Stern-layer correction. Recasting the Poisson-Boltzmann equations into Euler-Lagrange equations then significantly simplifies the derivation of the free energy of solvation for these approximate models. The parameters of the model are either fit directly to experimental observables-e.g., the finite ion size-or optimized for agreement with experimental results. Experimental data for this optimization are available in the form of Sechenov coefficients that describe the linear dependence of the salting-out effect of solutes with respect to the electrolyte concentration. In the final part, we rationalize the qualitative disagreement of the finite ion size modification to the Poisson-Boltzmann model with experimental observations by taking into account the electrolyte concentration dependence of the Stern layer. A route toward a revised model that captures the experimental observations while including the finite ion size effects is then outlined. This implementation paves the way for the study of electrochemical and electrocatalytic processes of molecules and cluster models with accurate electronic structure methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Cu-Cr Literature Review

Cu-Cr alloys are part of a class of face-centered cubic (FCC)-body-centered cubic (BCC) composites that includes similar alloys, such as Cu-Nb and Cu-Ta. When heavily deformed, these FCC-BCC materials create 'in situ' composites with a characteristic structure-nanoscale BCC filaments in a ductile FCC matrix. The strength of these composites is vastly greater than predicted by the rule of mixtures, and has been shown to be inversely proportional to the filament spacing. Lower raw materials costs suggest that Cu-Cr alloys may offer more economical solution to high-strength, high-conductivity wire than either their Nb or Ta counterparts. However, Cr is also more brittle and soluble in Cu than Nb or Ta. These qualities necessitate thermal treatments to remove solute atoms from the Cu matrix, improve conductivity, and maintain the ductility of the Cr filaments. Through the use of different thermomechanical processing routes or the addition of select dopants, alloys with strength in excess of 1 GPa at 70% IACS have been achieved. To date, previous research on Cu-Cr alloys has focused on a relatively small number of alloy compositions and processing methods while the effects of dopants and ageing treatments have only been studied independently. Consequently, there remains considerable opportunity for the development and optimization of these alloys as a leading high-strength, high-conductivity material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.

Chitty-Venkata, Krishna Teja↗
DOE OSTI2025

Disordered Rocksalts as High‐Energy and Earth‐Abundant Li‐Ion Cathodes

To address the growing demand for energy and support the shift toward transportation electrification and intermittent renewable energy, there is an urgent need for low‐cost, energy‐dense electrical storage. Research on Li‐ion electrode materials has predominantly focused on ordered materials with well‐defined lithium diffusion channels, limiting cathode design to resource‐constrained Ni‐ and Co‐based oxides and lower‐energy polyanion compounds. Recently, disordered rocksalts with lithium excess (DRX) have demonstrated high capacity and energy density when lithium excess and/or local ordering allow statistical percolation of lithium sites through the structure. This cation disorder can be induced by high temperature synthesis or mechanochemical synthesis methods for a broad range of compositions. DRX oxides and oxyfluorides containing Earth‐abundant transition metals have been prepared using various synthesis routes, including solid‐state, molten‐salt, and sol‐gel reactions. This review outlines DRX design principles and explains the effect of synthesis conditions on cation disorder and short‐range cation ordering (SRO), which determines the cycling stability and rate capability. In addition, strategies to enhance Li transport and capacity retention with Mn‐rich DRX possessing partial spinel‐like ordering are discussed. Finally, the review considers the optimization of carbon and electrolyte in DRX materials and addresses key challenges and opportunities for commercializing DRX cathodes.

Li-ion batteries↗
DOE OSTI2021

CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers—both of which are superior to previous approaches in precision and recall in three different test scenarios—and the first signedness classification algorithm, which exhibits a $>$ 97% F-score. Altogether, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average $\ell ^1$ error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods’ assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.

42 ENGINEERING↗
DOE OSTI2017

Radioactive Waste Management in a Treatability and Optimisation Perspective - 17527

In order to meet recycling criteria or to fulfil Waste Acceptance Criteria (WAC) for disposal there are many different management concepts for radioactive waste that can be applied. Which concept or set of concepts to use to optimize waste management depends on several factors, among them, the regulatory framework for recycling, the availability of a disposal facility and the acceptance criteria for disposal. The required pre-treatment and other pre-disposal activities should be carefully analyzed for each of the disposal options, as they are significant contributors to the total cost for managing the waste. Waste Acceptance Criteria are living documents that should be updated when regulatory framework changes as well as when new treatment methods and new packaging and barrier systems develop. Each waste treatment facility, has its own specific limitations and therefore its own WAC/CFA (conditions for acceptance), which depends both on the plant design and national legislation. Many waste owners, countries or licensees, have their preferred, and in some cases well established, waste routes for certain waste streams. In some countries, the full chain from waste treatment through to disposal of radioactive waste is in place, whilst other countries still are in the process of establishing their repository program. The worst case scenario for any licensee or waste owner is waste which is not possible to qualify for disposal. This can be due to poor or degraded waste containers, unspecified content in waste packages, or wastes that are not suitable for disposal in their current form. Such deficiencies result in expensive actions related to waste retrieval, new characterisation and/or costly investigations. The EDF Group has a fleet of waste treatment facilities and has, for decades, provided waste treatment services to domestic and international customers. This paper will discuss important waste owner considerations and EDF experiences related to the optimization of the waste management process. This will be from both a treatment and an end-state perspective. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Enabling full-scale grain boundary mitigation in polycrystalline perovskite solids

There exists a considerable density of interaggregate grain boundaries (GBs) and intra-aggregate GBs in polycrystalline perovskites. Mitigation of intra-aggregate GBs is equally notable to that of interaggregate GBs as intra-aggregate GBs can also cause detrimental effects on the photovoltaic performances of perovskite solar cells (PSCs). Here, we demonstrate full-scale GB mitigation ranging from nanoscale intra-aggregate to submicron-scale interaggregate GBs, by modulating the crystallization kinetics using a judiciously designed brominated arylamine trimer. The optimized GB-mitigated perovskite films exhibit reduced nonradiative recombination, and their corresponding mesostructured PSCs show substantially enhanced device efficiency and long-term stability under illumination, humidity, or heat stress. The versatility of our strategy is also verified upon applying it to different categories of PSCs. Our discovery not only specifies a rarely addressed perspective concerning fundamental studies of perovskites at nanoscale but also opens a route to obtain high-quality solution-processed polycrystalline perovskites for high-performance optoelectronic devices.

36 MATERIALS SCIENCE↗