Results for “Advanced Optimization” — Page 95

NASA NTRS1991

Multi-element airfoil optimization for maximum lift at high Reynolds numbers

An experimental study has been performed to assess the maximum lift capability of a supercritical multielement airfoil representative of an advanced transport aircraft wing. The airfoil model was designed with a leading-edge slat and single or two-segment trailing-edge flaps. Optimization work was performed at various slat/flap deflections as well as gap/overhang positions. Landing configurations and the attainment of maximum lift coefficients of 4.5 with single-element flaps and 5.0 with two-segment flaps was emphasized. Test results showed a relatively linear variation of the optimum gap/overhang positioning of the slat versus slat deflection, considerable differences in optimum rigging between single and double segment flaps, and large Reynolds number effects on multielement airfoil optimization.

Valarezo, Walter O.↗

DOE OSTI2026

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

A Pulsar-Inspired Timing Framework for Power System: Optimization and Performance Evaluation

Due to their excellent stability, neutron pulsar stars are considered promising candidate timing sources for power system applications. However, the complexity of pulsar signals necessitates advanced processing algorithms to provide accurate timing references. This paper presents the foundational framework for pulsar signal processing, serving as the basis for further optimization. To enhance the timing accuracy and computation efficiency in pulsar period searches, three algorithms are proposed as the initial optimization step: wavelet de-noising, fast folding, and cross-correlation for profile evaluation. Wavelet de-noising improves signal-to-noise ratio (SNR) by 36%–70%. Fast folding reduces computation time from hundreds of seconds to mere milliseconds. Cross-correlation works better than traditional SNR-based methods by effectively identifying the optimal period. The performance of the proposed algorithms is evaluated using observation data from telescopes. Together, these algorithms significantly improve pulsar timing performance, reducing the error of the Pulse Per Second (PPS) signal from hundreds to tens of microseconds.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗

DOE OSTI2016

Highly efficient gene transfer using a retroviral vector into murine T cells for preclinical chimeric antigen receptor-expressing T cell therapy

Adoptive immunotherapy using chimeric antigen receptor-expressing T (CAR-T) cells has attracted attention as an efficacious strategy for cancer treatment. To prove the efficacy and safety of CAR-T cell therapy, the elucidation of immunological mechanisms underlying it in mice is required. Although a retroviral vector (Rv) is mainly used for the introduction of CAR to murine T cells, gene transduction efficiency is generally less than 50%. The low transduction efficiency causes poor precision in the functional analysis of CAR-T cells. We attempted to improve the Rv gene transduction protocol to more efficiently generate functional CAR-T cells by optimizing the period of pre-cultivation and antibody stimulation. In the improved protocol, gene transduction efficiency to murine T cells was more than 90%. In addition, almost all of the prepared murine T cells expressed CAR after puromycin selection. These CAR-T cells had antigen-specific cytotoxic activity and secreted multiple cytokines by antigen stimulation. We believe that our optimized gene transduction protocol for murine T cells contributes to the advancement of T cell biology and development of immunotherapy using genetically engineered T cells. - Highlights: • We established highly efficient gene transduction protocols for murine T cells. • CD8{sup +} CAR-T cells had antigen-specific cytotoxic activity. • CD4{sup +} CAR-T cells secreted multiple cytokines by antigen stimulation. • This finding can contribute to the development of T-cell biology and immunotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Electrocatalytic CO 2 Reduction to Fuels: Progress and Opportunities

The electrochemical reduction of CO 2 continues to be an appealing option for storing renewable energy in a chemical form. In this review, we assess progress in designing catalysts that convert CO 2 to high energy density products. We explain how reaction data can be reported to reflect the intrinsic properties of the catalyst. This analysis shows that limited advances have been made in improving the performance of Cu. We suggest that opportunities remain using bimetallic catalysts that are resistant to dealloying. While aqueous systems are instrumental to developing our understanding of this chemistry, gas-fed systems that operate at high current densities must be developed. Although obstacles remain for practical application of CO 2 reduction, advances in fundamental understanding made over the years give reason for optimism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Unveiling the lithium-ion transport mechanism in Li{sub 2}ZrCl{sub 6} Solid-State Electrolyte {ital via} deep learning-accelerated molecular dynamics simulations.

Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolytes for next-generation all-solid-state batteries. The unique crystal structure of LZCs plays a crucial role in facilitating lithium-ion mobility, which further affects the electrochemical performance. To understand the underlying mechanism governing ion transport, we employed deep learning-accelerated molecular dynamics simulation on Li2ZrCl6 (trigonal alpha- and monoclinic beta-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered alpha-LZC exhibits the highest ionic conductivity, while beta-LZC demonstrates significantly lower conductivity, closely aligning with experimental findings. The study confirms that across all phases, lithium migration proceeds via the site-to-site hopping mechanism, where variations in site residence times critically impact the overall ionic conductivity. In alpha-LZCs, lithium ions prefer to anisotropically diffuse across interlayers as the result of a lower energy barrier, driven primarily by collective diffusion. In contrast, lithium ions in beta-LZC primarily isotropically diffuse within the intralayer, hindered by higher energy barriers and determined by individual diffusion. The variation in ZrCl6 2- octahedral unit softening, induced by the specific layered arrangement of zirconium atoms, emerges as a critical determinant of the energy barriers across the LZC phases. These atomic-scale insights into the transport processes provide valuable guidance for the rational design and optimization of LZCs-based electrolytes, accelerating their practical application in advanced energy storage technologies.

Guo, Hanzeng↗

DOE OSTI2018

Effects of geometry and composition of soft polymer films embedded with nanoparticles on rates for optothermal heat dissipation

Embedding soft matter with nanoparticles (NPs) can provide electromagnetic tunability at sub-micron scales for growing number of uses in healthcare, sustainable energy, and chemical processing. But use of NP-embedded soft material in temperature-sensitive applications has been constrained by difficulty in validating prediction of rates for energy dissipation across thermally insulating to conducting behavior. This work improved embedment of monodisperse NP to stably decrease inter-NP spacings in polydimethylsiloxane (PDMS) to nano-scales. Lumped-parameter and finite element analyses were refined to apportion effects of structure and composition of NP-embedded soft polymer to rates for conductive, convective, and radiative heat dissipation. These advances allowed rational selection of PDMS size and NP composition to optimize measured rates of internal (conductive) and external (convective and radiative) heat dissipation. Stably reducing distance between monodisperse NP to nano-scale intervals increased overall heat dissipation rate by up to 29%. Refined fabrication of NP-embedded polymer enabled tunability of dynamic thermal response, the ratio of internal to external dissipation rate, by a factor of 3.1 to achieve a value of 0.091, the largest reported to date. Heat dissipation rates simulated a priori were consistent with 130 μm resolution thermal images across 2- to 15-fold changes in NP-PDMS geometry and composition. Nusselt number was observed to increase with the fourth root of Rayleigh number across thermally insulative and conductive regimes, further validating the approach. These developments support model-informed design of soft media embedded with nano-scale spaced NP to optimize heat dissipation rates for evolving temperature-sensitive diagnostic and therapeutic modalities as well as emerging uses in flexible bioelectronics, cell and tissue culture, and solar-thermal heating.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Interplay between experiments and calculations for organometallic clusters and caged clusters

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al{sub 12}X, behaving as a “superatom”.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between “choose-maximum” (choose a base pair giving the maximum β for each step) and “choose-minimum” (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Cryogen-free variable temperature scanning SQUID microscope

Scanning Superconducting QUantum Interference Device (SQUID) microscopy is a powerful tool for imaging local magnetic properties of materials and devices, but it requires a low-vibration cryogenic environment, traditionally achieved by thermal contact with a bath of liquid helium or the mixing chamber of a “wet” dilution refrigerator. We mount a SQUID microscope on the 3 K plate of a Bluefors cryocooler and characterize its vibration spectrum by measuring SQUID noise in a region of sharp flux gradient. By implementing passive vibration isolation, we reduce relative sensor-sample vibrations to 20 nm in-plane and 15 nm out-of-plane. A variable-temperature sample stage that is thermally isolated from the SQUID sensor enables measurement at sample temperatures from 2.8 K to 110 K. We demonstrate these advances by imaging inhomogeneous diamagnetic susceptibility and vortex pinning in optimally-doped YBCO above 90 K.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Progress toward picosecond on-chip magnetic memory

Here, we offer a perspective on the prospects of ultrafast spintronics and opto-magnetism as a pathway to high-performance, energy-efficient, and non-volatile embedded memory in digital integrated circuit applications. Conventional spintronic devices, such as spin-transfer-torque magnetic-resistive random-access memory (STT-MRAM) and spin–orbit torque MRAM, are promising due to their non-volatility, energy-efficiency, and high endurance. STT-MRAMs are now entering into the commercial market; however, they are limited in write speed to the nanosecond timescale. Improvement in the write speed of spintronic devices can significantly increase their usefulness as viable alternatives to the existing CMOS-based devices. In this article, we discuss recent studies that advance the field of ultrafast spintronics and opto-magnetism. An optimized ferromagnet–ferrimagnet exchange-coupled magnetic stack, which can serve as the free layer of a magnetic tunnel junction (MTJ), can be optically switched in as fast as ~3 ps. Integration of ultrafast magnetic switching of a similar stack into an MTJ device has enabled electrical readout of the switched state using a relatively larger tunneling magnetoresistance ratio. Purely electronic ultrafast spin–orbit torque induced switching of a ferromagnet has been demonstrated using ~6 ps long charge current pulses. We conclude our Perspective by discussing some of the challenges that remain to be addressed to accelerate ultrafast spintronics technologies toward practical implementation in high-performance digital information processing systems.

42 ENGINEERING↗

DOE OSTI2021

High-performance ECRH at W7-X: experience and perspectives

The second operation phase of W7-X (OP1.2) showed the potential of exclusively electron cyclotron resonance heating (ECRH)-sustained plasma operations in stellarators. Employing multi-pass ECRH scenario in the second harmonic O-mode (O2-ECRH), stationary densities of up to 1.4 × 10 20 m -3 could be achieved. This scenario also made stationary divertor detachment possible, which is a reactor-relevant scenario for power and particle exhaust. At high densities and with sufficiently high density gradients for an improved ion confinement, the coupling between the electrons and ions was strong enough to bring the ion temperature to values above 3 keV and to the neoclassical limit for some magnetic configurations, thus enabling to test the W7-X neoclassical optimization. The planned enhancement of the ECRH performance will enable to advance towards reactor-relevant beta values and to investigate their stability and confinement of fast particles, which is a priority goal of W7-X.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

High-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-of-the-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

Luo, Huizhang (ORCID:0000000323920267)↗

DOE OSTI2014

SU-E-J-58: Dosimetric Verification of Metal Artifact Effects: Comparison of Dose Distributions Affected by Patient Teeth and Implants

Purpose: Implant-supported dentures seem particularly appropriate for the predicament of becoming edentulous and cancer patients are no exceptions. As the number of people having dental implants increased in different ages, critical dosimetric verification of metal artifact effects are required for the more accurate head and neck radiation therapy. The purpose of this study is to verify the theoretical analysis of the metal(streak and dark) artifact, and to evaluate dosimetric effect which cause by dental implants in CT images of patients with the patient teeth and implants inserted humanoid phantom. Methods: The phantom comprises cylinder which is shaped to simulate the anatomical structures of a human head and neck. Through applying various clinical cases, made phantom which is closely allied to human. Developed phantom can verify two classes: (i)closed mouth (ii)opened mouth. RapidArc plans of 4 cases were created in the Eclipse planning system. Total dose of 2000 cGy in 10 fractions is prescribed to the whole planning target volume (PTV) using 6MV photon beams. Acuros XB (AXB) advanced dose calculation algorithm, Analytical Anisotropic Algorithm (AAA) and progressive resolution optimizer were used in dose optimization and calculation. Results: In closed and opened mouth phantom, because dark artifacts formed extensively around the metal implants, dose variation was relatively higher than that of streak artifacts. As the PTV was delineated on the dark regions or large streak artifact regions, maximum 7.8% dose error and average 3.2% difference was observed. The averaged minimum dose to the PTV predicted by AAA was about 5.6% higher and OARs doses are also 5.2% higher compared to AXB. Conclusion: The results of this study showed that AXB dose calculation involving high-density materials is more accurate than AAA calculation, and AXB was superior to AAA in dose predictions beyond dark artifact/air cavity portion when compared against the measurements.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Retrofit Energy Analysis and Central Thermal modeling (REACT) v1.0

REACT is a website designed to simplify the analysis and decision-making process for retrofitting existing central plant heating and cooling systems with advanced heat pump technologies. The tool evaluates the technical and economic viability of replacing traditional boilers with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. Retrofitting traditional boiler and chiller systems with water-to-water or air-to-water heat pumps can significantly reduce energy consumption and lower lifecycle costs. The REACT provides: User-Friendly Tools: A user friendly web interface for quick, intuitive analysis accessible to non-experts. Advanced Modeling: A Python-powered engine for detailed parametric studies, optimization, and research applications. Comprehensive Analysis: Lifecycle cost evaluation, energy consumption modeling, and environmental impact assessment. Visual Insights: A variety of plots to visualize system performance and design trade-offs. The engine for the website (REACT) bases on several Python libraries, and the website will be hosted on an ETA server.

Kim, Donghun [Lawrence Berkeley National Laborator↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗