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At least 685 records · Page 38

Helical Fins for Concentrated Solar Receivers: Design Optimization and Entropy Analysis

Concentrated solar power (CSP) with thermal energy storage (TES) has the potential to achieve grid parity. This can be realized by operating CSP systems at temperatures above 700 °C with high-efficiency sCO 2 power cycles. However, operating CSP systems at such temperatures poses several challenges, among which the design of solar receivers to accommodate increased thermal loads is critical. To this end, this work explores and optimizes various swirl-inducing internal fin designs for solar receiver tubes. These fin designs not only improve the thermal performance of receiver tubes but also levelize temperature unevenness caused by non-uniform thermal loading. In this work, the geometric parameters of the fin designs are optimized to maximize the Nusselt number with a constraint on the friction factor. This optimization, however, is computationally intensive, requiring hundreds of simulation calls to computational fluid dynamics (CFD) models. Here, to circumvent this problem, surrogate models are used to approximate the simulation outputs needed during the optimization. In addition, this study also examines the fin designs from an entropy generation perspective. To this end, the entropy contributions from thermal and viscous effects are quantitatively compared while varying the operational Reynolds number.

14 SOLAR ENERGY↗

Measure This, Not That: Pareto Optimal Trade-Offs between Model-Based Information Content and Measurements Cost

The slides present a novel convex optimization formulation to compute the best set of measurements for multi-response dynamical systems with asynchronous time steps that maximize the Fisher information content subject to budget constraints. The trace (A-optimality) or determinant (D-optimality) of the Fisher Information Matrix (FIM) quantifies the information content. The framework supports arbitrary (positive semi-definite) variance and covariances between every pair of responses and their time steps.

Wang, Jialu↗

Combined Experimental and Computational Study of Molybdenum and Niobium for Nuclear Sensor Application

Due to their novel electromagnetic and thermal properties, molybdenum (Mo) and niobium (Nb) become optimal temperature sensor materials for nuclear energy applications. We leveraged voltage recorded during a heat ramp to tune a computational method to predict the Seebeck electromotive force (EMF) of Mo and Nb. Using a combined Density Functional Theory (DFT) and Boltzmann Transport Equations (BTE) method the voltage was predicted but did not include the effects of temperature on atomic structure. Combining Ab Initio Molecular Dynamics (AIMD) and BTE included temperature effects on structure optimization and yielded voltages in a good agreement with experiment. Lanthanum (La) and Phosphorus (P) additives in Mo and Nb, respectively, could increase the EMF compared to those of the pure metals. The presence of oxygen (O) in Mo increases the EMF while O in Nb slightly reduces the EMF. Furthermore, our studies suggested that heat treatment-induced structural changes that lead to a reduction in voltage occur not only at the mesoscale as previously understood but also at the atomic scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Multiphysics Modeling of Hydrokinetic Turbine Energy Conversion System

Hydrokinetic turbines (HKTs) hold great promise as a renewable energy source, but high maintenance costs and limited energy output hinder their widespread adoption. The lack of comprehensive research on HKT drivetrain designs creates a knowledge gap in enhancing generation efficiency and cost reduction. A model of the HKT system with a focus on electric drivetrains and power converters is required to address this knowledge gap. This paper first introduces a MATLAB-averaged model integrating electrical-mechanical-thermal domains and aging behaviors within multi-time frames. Then a PLECS model, which incorporates maximum power point tracking and dq reference framed control for AC-DC-AC power converters, is enriched by a dynamic thermal model to predict fast transients accurately. These models optimize the design at the component level, resulting in improved integrated system performance. Furthermore, the validation of the models is carried out through hardware experiments for the averaged model and hardware-in-the-loop testing for the dynamic model.

ADVANCED PROPULSION SYSTEMS,HYDRO ENERGY↗

The Importance of Nonequilibrium to Equilibrium Transition Pathways for the Efficiency and Stability of Organic Solar Cells

Controlled morphology of solution-processed thin films have realized impressive achievements for non-fullerene acceptor (NFA)–based organic solar cells (OSCs). Given the large set of donor–acceptor pairs, employing various processing conditions to realize optimal morphology for high efficiency and stable OSCs is a strenuous task. Therefore, comprehensive correlations between processing conditions and morphology evolution pathways have to be developed for efficient performance and stability of devices. Within the framework of the blend system, crystallization transitions of NFA molecules are tracked utilizing the first heating scan of differential scanning calorimeter (DSC) measurement correlating with respective morphology evolution of blend films. Real-time dynamics measurements and morphology characterizations are combined to provide optimal morphology transition pathways as NFA molecules are shown to be released from the mixed phase to form balanced ordered packing with variant processing conditions. Polymer:NFA films are fabricated using blade coating incorporating solvent additive or thermal annealing as processing conditions as a correlation is formulated between performance and stability of solar cells with morphology transition pathways. In conclusion, this work demonstrates the significance of processing condition-controlled transition pathways for the realization of optimal morphology leading to superior OSC devices.

14 SOLAR ENERGY↗

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗

Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

42 ENGINEERING↗

Development of inducible promoters for regulating gene expression in Clostridium tyrobutyricum for biobutanol production

Abstract Clostridium tyrobutyricumis an anaerobe known for its ability to produce short‐chain fatty acids, alcohols, and esters. We aimed to develop inducible promoters for fine‐tuning gene expression inC. tyrobutyricum. Synthetic inducible promoters were created by employing anEscherichia coli lacoperator to regulate the thiolase promoter (PCathl) fromClostridium acetobutylicum, with the best one (LacI‐Pto4s) showing a 5.86‐fold dynamic range with isopropyl β‐d‐thiogalactoside (IPTG) induction. A LT‐Pt7 system with a dynamic range of 11.6‐fold was then created by combining LacI‐Pto4s with a T7 expression system composing of RNA polymerase (T7RNAP) and Pt7lacpromoter. Furthermore, two inducible expression systems BgaR‐PbgaLA and BgaR‐PbgaLB with a dynamic range of ~40‐fold were developed by optimizing a lactose‐inducible expression system fromClostridium perfringenswith modified 5′ untranslated region (5′ UTR) and ribosome‐binding site (RBS). BgaR‐PbgaLB was then used to regulate the expressions of a bifunctional aldehyde/alcohol dehydrogenase encoded byadhE2 and butyryl‐CoA/acetate Co‐A transferase encoded bycat1 inC. tyrobutyricumwild type and Δcat1::adhE2, respectively, demonstrating its efficient inducible gene regulation. The regulatedcat1 expression also confirmed that the Cat1‐catalyzed reaction was responsible for acetate assimilation inC. tyrobutyricum. The inducible promoters offer new tools for tuning gene expression inC. tyrobutyricumfor industrial applications.

Biotechnology & Applied Microbiology↗

Gramicidin and chlorhexidine encapsulated in bicontinuous microemulsions: Antimicrobial activity performance and their impact on self‐assembly

The utility of bicontinuous microemulsions (BMEs) as carriers of the antimicrobial peptide (AMP) gramicidin D and antiseptic chlorhexidine was investigated for possible topical delivery to chronic wounds. The two water-insoluble solutes dissolved in pre-formed one-phase BMEs of Water/Polysorbate 80/Limonene/Ethanol/Glycerol and Water/Aerosol-OT (AOT)/Polysorbate 85/Isopropyl Myristate and an AOT/Polysorbate 85 Winsor-III system, achieving gramicidin and chlorhexidine concentrations of 1.0 wt% and 0.5% individually and 0.5% and 0.3% in mixtures at 22°C, respectively. Small-angle neutron scattering measurements demonstrated that both solutes decreased surfactant interfacial activity and increased interfacial fluidity for the Polysorbate 80 system. For the AOT/Polysorbate 85 systems, ellipsoidal aggregates consisting of gramicidin and likely adsorbed surfactant and oil formed, while chlorhexidine enhanced the surface activity of surfactants. According to bioassays performed on artificial skin, the incorporation of melittin, gramicidin, and chlorhexidine in general enhanced the bioactivity of Polysorbate 80 BMEs for 24 h treatment against relevant antibiotic-resistant bacteria found on skin relative to controls. Yet, BME treatments were less effective than aqueous melittin control, in contrast to well diffusion bioassays performed previously. The results reflect the strong impact of AMPs and antiseptics on BME structure and dynamics and the complexity of formulating BMEs for optimal antimicrobial activity.

antimicrobial peptides↗

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

Oxygen enrichment combustion to reduce fossil energy consumption and emissions in hot rolling steel production

The reheating furnace operation in the hot mill is natural gas- and electricity-intensive. Oxygen enrichment combustion for reheating furnaces has been proposed to curb and replace natural gas use. In this study, heat transfer in steel slabs in the combustion environment of a push-type reheating furnace was simulated using a computational fluid dynamics (CFD) model. Two oxygen enrichment methods that optimized for performance were selected— a medium oxygen enrichment (MOE) case and an oxy-fuel (OF) case. A life cycle analysis (LCA) characterized the energy and emission profiles of an integrated iron and steel manufacturing process using the two oxygen enrichment cases for the hot mill. These conditions were evaluated for energy use and carbon intensity and compared with a baseline case. Results show that with oxygen enrichment, natural gas consumption can decrease by 19.6%–26.8%, total energy consumption (natural gas and electricity) can decrease by 15.1%–20.7% in the hot mill. Emissions of greenhouse gases can decrease by 11.1%–15.2% in the two optimized cases with 14%–27% reductions in regulated criteria pollutants (nitrogen oxides, carbon monoxide, particulate matter, volatile organic compounds, black carbon, organic carbon, and volatile organic carbons). There is a tradeoff between reducing natural gas consumption and increasing electricity demand from a life cycle perspective. Although the OF case resulted in higher energy- and emissions-related benefits, the MOE case showed the more desirable heat flux uniformity, which is key to maintaining product quality. The analysis suggests that oxygen enrichment in the reheating furnace process can have a significant impact on hot mill environmental performance and become a contributing factor in transitioning to low-carbon steel manufacturing.

36 MATERIALS SCIENCE↗

In situ high-temperature Raman spectroscopy for online EAF slag analysis

Real-time monitoring of slag chemistry is critical for optimizing Electric Arc Furnace (EAF) steelmaking operations, where dynamic variations in slag composition directly influence slag foaming, refractory degradation, and thermal efficiency. Conventional techniques such as X-ray fluorescence (XRF), Fourier-transform infrared (FTIR), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are commonly used to analyze slag composition, but their offline nature and equipment constraints limit their applicability for online monitoring in harsh industrial environments. To address this challenge, we present an in situ, high-temperature analytical approach that integrates Raman spectroscopy with a custom designed fiber-optic probe for real-time slag characterization at 1550 °C. The system enables non destructive spectral acquisition from molten slags, providing molecular-level insights into silicate polymerization and iron oxidation states. Eight synthetic slag samples were evaluated, and key Raman features—such as Q n silicate units and FeO₄/FeO₆ coordination environments—were identified and quantitatively correlated with slag basicity and Fe₂O₃ content. The results demonstrate agreement between Raman spectral ratios and bulk slag chemistry, validating the method’s capability to track compositional and structural changes under molten temperature. This work establishes the feasibility of deploying fiber-optic Raman probe for online EAF slag monitoring and highlights their potential to support closed-loop control strategies, thereby enhancing process stability, refractory protection, and steel quality in industrial steelmaking applications.

47 OTHER INSTRUMENTATION↗

A greedy Galerkin method to efficiently select sensors for linear dynamical systems

A key challenge in inverse problems is the selection of sensors to gather the most effective data. In this paper, we consider the problem of inferring the initial condition to a linear dynamical system and develop an efficient control-theoretical approach for greedily selecting sensors. Our method employs a Galerkin projection to reduce the size of the inverse problem, resulting in a computationally efficient algorithm for sensor selection. As a byproduct of our algorithm, we obtain a preconditioner for the inverse problem that enables the rapid recovery of the initial condition. Here, we analyze the theoretical performance of our greedy sensor selection algorithm as well as the performance of the associated preconditioner. Finally, we verify our theoretical results on various inverse problems involving partial differential equations.

97 MATHEMATICS AND COMPUTING↗

Molecular Origin of Wettability Alteration of Subsurface Porous Media upon Gas Pressure Variations

Upon extraction/injection of a large quantity of gas from/into a subsurface system in shale gas production or carbon sequestration, the gas pressure varies remarkably, which may significantly change the wettability of porous media involved. Mechanistic understanding of such changes is critical for designing and optimizing a related subsurface engineering process. Using molecular dynamics simulations, in this work we have calculated the contact angle of a water droplet on various solid surfaces (kerogen, pyrophyllite, calcite, gibbsite, and montmorillonite) as a function of CO 2 or CH 4 gas pressure up to 200 atm at a temperature of 300 K. The calculation reveals a complex behavior of surface wettability alteration by gas pressure variation depending on surface chemistry and structure, and molecular interactions of fluid molecules with surfaces. As the CO 2 gas pressure increases, a partially hydrophilic kerogen surface becomes highly hydrophobic, while a calcite surface becomes more hydrophilic. Considering kerogen and calcite being the major components of a shale formation, we postulate that the wettability alteration of a solid surface induced by a gas pressure change may play an important role in fluid flows in shale gas production and geological carbon sequestration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dual-Bed Radioiodine Capture from Complex Gas Streams with Zeolites: Regeneration and Reuse of Primary Sorbent Beds for Sustainable Waste Management

Dual-sorbent systems are proposed for radioiodine management with a regenerated primary bed for multiple cycles of use in complex conditions and a secondary bed for disposal with higher waste loadings. Sorbent approaches for the effective capture of gaseous radioiodine (isotopes 129 I and 131 I) produced from a range of nuclear processes have been studied for over half a century. (1−5) Whether or not a sorbent (e.g., molecular sieve) is required to physically screen/trap or chemically bind a radionuclide of interest through chemisorption, the complexity of the gas stream has a large impact on the performance (e.g., loading capacity, selectivity) and active life of a sorbent bed. (3) Silver mordenite (AgZ), the U.S. Department of Energy baseline sorbent for radioiodine capture from nuclear processes, performs well within acidic conditions and at elevated temperatures (6) and can be consolidated into a chemically durable waste form for long-term disposal. (7,8) However, new sorbents are being sought because optimal capture performance of AgZ significantly decreases in dynamic oxidizing environments with competing species, and it is expensive and it contains Ag (a toxic metal). (9) Until a new sorbent is found to replace AgZ, the regeneration and reuse of AgZ is an attractive alternative to a single-use primary sorbent bed. In this regard, a primary sorbent could be designed for enhanced capture in complex gas streams and the ability to be regenerated for reuse. Here, a secondary sorbent could then be tailored for maximum iodine loading in the gas stream and chemical durability within a disposal facility.

chemisorption↗

Impact of processing humidity on ionomer film structure and performance in hydroxide exchange membrane electrolyzers

Hydroxide exchange membrane electrolyzers (HEMELs) enable hydrogen production using low-cost, earth-abundant materials. Improving electrode fabrication is integral to enhancing device performance, and ionomer-responsible for transporting hydroxide and mechanically supporting the catalyst-is a major component. Here, we use experiments and computation to study the effects of relative humidity (RH) during the drying process of poly(aryl piperidinium) ionomer films on HEMEL electrodes. Broadly, the drying environments determine the physical structure and electrochemical traits of the ionomer network. High RH drying yields a highly porous network with excessive water uptake, structural defects, washout, and 64% reduction in hydroxide conductivity. Extremely low RH drying produces an overly compact pore network that hinders hydroxide mobility. In contrast, moderately low RH drying (9% RH) creates an ionomer film with well-balanced traits: excellent mechanical stability and connectivity needed for catalyst retention and hydroxide transport, which improves HEMEL performance by 40% at 1.8 V compared to suboptimal RHs. This research advances HEMEL manufacturing by providing a simple, scalable, and low-cost approach to optimize electrode ionomer films.

36 MATERIALS SCIENCE↗

Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study

The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.

Pan, Melrose [ORNL] (ORCID:000000031627448X)↗

In situ TEM study of crystallization and chemical changes in an oxidized uncapped Ge 2 Sb 2 Te 5 film

Ge 2 Sb 2 Te 5 (GST-225) has been the most used active material in nonvolatile phase-change memory devices. Understanding the kinetics and dynamics involved in crystallization is critical for the optimization of materials and devices. A GST-225 thin film of 20 nm thickness was prepared by sputtering directly onto a Protochip and left uncapped and exposed to atmosphere for approximately 1 year. Here, early stages of crystallization and growth of the film have been studied inside the TEM from room temperature to 140 °C. The morphological and structural transformations have been studied by a C s -corrected environmental TEM, and images have been recorded using a high-speed low electron dose camera (Gatan K3 IS). The amorphous to crystalline transformation has been observed at ~35 °C. From the large field, high-resolution images obtained using the Gatan K3 IS camera early crystallization can be detected and nucleation rates and growth velocities can be obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗