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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2014

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Evaluation of nitrogen-rich macrocyclic ligands for the chelation of therapeutic bismuth radioisotopes

The use of α-emitting isotopes for radionuclide therapy is a promising treatment strategy for small micro-metastatic disease. The radioisotope ²¹³Bi is a nuclide that has found substantial use for targeted α-therapy (TAT). The relatively unexplored aqueous chemistry of Bi³⁺, however, hinders the development of bifunctional chelating agents that can successfully deliver these Bi radioisotopes to the tumor cells. Here, a novel series of nitrogen-rich macrocyclic ligands is explored for their potential use as Bi-selective chelating agents. The ligands, 1,4,7,10-tetrakis(pyridin-2-ylmethyl)-1,4,7,10-tetraazacyclododecane (L py ), 1,4,7,10-tetrakis(3-pyridazylmethyl)-1,4,7,10-tetraazacyclododecane (L pyd ), 1,4,7,10-tetrakis(4-pyrimidylmethyl)-1,4,7,10-tetraazacyclododecane (L pyr ), and 1,4,7,10-tetrakis(2-pyrazinylmethyl)-1,4,7,10-tetraazacyclododecane (L pz ), were prepared by a previously reported method and investigated here for their abilities to bind Bi radioisotopes. The commercially available and commonly used ligands 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and N-[ (R) -2-amino-3-( p -isothiocyanato-phenyl)propyl]- trans -(S,S)- cyclohexane-1,2-diamine- N,N,N',N",N" -pentaacetic acid (CHX-A''-DTPA) were also explored for comparative purposes. Radio-thin-layer chromatography (TLC) was used to measure the binding kinetics and stabilities of the complexes formed. The long-lived isotope, ²⁰⁷Bi (t 1/2 = 32 years), was used for these studies. Density functional theory (DFT) calculations were also employed to probe the ligand interactions with Bi³⁺ and the generator parent ion Ac³⁺.In contrast to DOTA and CHX-A''-DTPA, these nitrogen-rich macrocycles selectively chelate Bi³⁺ in the presence of the parent isotope Ac³⁺. Among the four tested, L py was found to exhibit optimal Bi³⁺-binding kinetics and complex stability. L py complexes Bi³⁺ more rapidly than DOTA, yet the resulting complexes are of similar stability. DFT calculations corroborate the experimentally observed selectivity of these ligands for Bi³⁺ over Ac³⁺. Taken together, these data implicate L py as a valuable chelating agent for the delivery of ²¹³Bi. Its selectivity for Bi³⁺ and rapid and stable labeling properties warrant further investigation and biological studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Efficient algorithm for locating and sizing series compensation devices in large power transmission grids: I. Model implementation

We explore optimization methods for planning the placement, sizing and operations of Flexible Alternating Current Transmission System (FACTS) devices installed to relieve transmission grid congestion. We limit our selection of FACTS devices to Series Compensation (SC) devices that can be represented by modification of the inductance of transmission lines. Our master optimization problem minimizes the l 1 norm of the inductance modification subject to the usual line thermal-limit constraints. We develop heuristics that reduce this non-convex optimization to a succession of Linear Programs (LP) which are accelerated further using cutting plane methods. The algorithm solves an instance of the MatPower Polish Grid model (3299 lines and 2746 nodes) in 40 seconds per iteration on a standard laptop—a speed up that allows the sizing and placement of a family of SC devices to correct a large set of anticipated congestions. We observe that our algorithm finds feasible solutions that are always sparse, i.e., SC devices are placed on only a few lines. In a companion manuscript, we demonstrate our approach on realistically-sized networks that suffer congestion from a range of causes including generator retirement. In this manuscript, we focus on the development of our approach, investigate its structure on a small test system subject to congestion from uniform load growth, and demonstrate computational efficiency on a realistically-sized network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Evaluating structure selection in the hydrothermal growth of FeS 2 pyrite and marcasite

While the ab initio prediction of the properties of solids and their optimization towards new proposed materials is becoming established, little predictive theory exists as to which metastable materials can be made and how, impeding their experimental realization. Here we propose a quasi-thermodynamic framework for predicting the hydrothermal synthetic accessibility of metastable materials and apply this model to understanding the phase selection between the pyrite and marcasite polymorphs of FeS 2 . We demonstrate that phase selection in this system can be explained by the surface stability of the two phases as a function of ambient pH within nano-size regimes relevant to nucleation. This result suggests that a first-principles understanding of nano-size phase stability in realistic synthesis environments can serve to explain or predict the synthetic accessibility of structural polymorphs, providing a guideline to experimental synthesis via efficient computational materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Influence of Local Distortions on Proton Mobility in Acceptor Doped Perovskites

Optimizing proton conduction in solids remains the most promising solution for achieving intermediate temperature (~750–1000 K) solid oxide fuel cell devices, and enabling selective membranes for H 2 separation. Proton conduction, a thermally activated process, exhibits its highest rates in yttrium (Y) acceptor doped BaZrO 3 at an optimal doping level of 20% Y. The presence of extended defects such as grain boundaries has typically generated a wide variability in reported conductivity values. This has hindered a fundamental mechanistic understanding of how (acceptor) doping levels correlate with the activation energy of protons to produce an optimal doping level for fast proton transport. While isolated dopants have been suggested as the primary source of proton trapping, our results indicate that it is the local dopant-density that matters. Here in this work, we show that increasing the local dopant density promotes localized lattice distortions in the presence of point defects such as oxygen-vacancies or proton interstitials. An increasing distortion amplitude traps the point defects more strongly in the form of polarons, forming defect-clusters at higher concentrations. This leads to a monotonic increase in the activation energy (and hence a decrease in proton mobility) as observed in our measurements. The optimum doping level can now be explained as a competition between increasing proton concentration with doping levels and increasing activation energy due to defect-clusters formed by defect-polarons. Based on our findings, we demonstrate how to improve proton conductivity in doped BaZrO 3 , by inhibiting this dopant-lattice polaronic interaction. Finally, this approach should be generally applicable for ionic conduction in perovskite oxides such as oxygen-ion conduction in solid-oxide fuel cells and alkali-ion conduction in solid-state batteries where carriers might get trapped as defect-polarons.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Advanced Control Architecture and Sensor Information Development for Process Automation, Optimization, and Imaging of Chemical Looping Systems

The Syngas Chemical Looping (SCL) process developed at The Ohio State University (OSU) uses an iron-based oxygen carrier to convert syngas into high-purity hydrogen while simultaneously capturing carbon dioxide that is readily sequestrable. A 25 kWth sub-pilot unit was successfully demonstrated multiple times on the OSU Clean Coal Research Facility, and a 250 kWth – 3 MWth pilot demonstration unit has been designed and constructed at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama. The focus of this project is to develop multiple technologies to automate the startup and shutdown of chemical looping system. Success of this project will propel the advanced carbon capture method toward commercialization, hence be disruptive to the fossil fuel and power industries. Advanced process automation control architecture and imaging and optimization sensor software were developed for OSU chemical looping process. A high-level controller (HLC) consisting of decision-making and controller-selection logic integrated with sliding mode controllers (SMCs) were developed to perform chemical looping process startup and shutdown. Babcock & Wilcox Power Generation Group’s FocalPoint Optimization System was implemented to optimize the process operating conditions. The process automation controller and optimization information were realized and tested on OSU’s existing sub-pilot chemical looping test unit under atmospheric condition. The implemented control architecture was able to perform the complete process procedure from startup, steady-state reaction operation to shutdown with minimal operator intervention. The controller was also implemented on the pressurized syngas chemical looping (SCL) pilot test unit constructed at the National Carbon Capture Center (NCCC), demonstrating its pressure control capability with a designed transient state trajectory. An electrical capacitance volume tomography (ECVT) sensor device and its software were developed to image the packed moving bed and slugging fluidized bed of oxygen carriers in chemical looping systems at elevated temperatures. The ECVT device constructed was capable of tracking the subtle voidage variance along the moving bed axis as a feature for directly measuring the oxygen carrier particles’ linear velocity. Also, the high temperature ECVT device was used to study the fluidization characteristics of Geldart Group D particles.

08 HYDROGEN↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2024

Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis

The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Low Global Warming Potential Refrigerants for Commercial Refrigeration Systems

Supermarket refrigeration systems account for approximately 50% of supermarket energy use, placing this class of equipment among the highest energy consumers in the commercial building domain. In addition, the commonly used refrigeration system in supermarket applications is the multiplex direct expansion (DX) system, which is prone to refrigerant leaks due to its long lengths of refrigerant piping. This leakage reduces the efficiency of the system and increases the impact of the system on the environment. The high Global Warming Potential (GWP) of the hydrofluorocarbon (HFC) refrigerants commonly used in these systems, coupled with the large refrigerant charge and the high refrigerant leakage rates leads to significant direct emissions of greenhouse gases into the atmosphere. Environmental concerns are driving regulations for the heating, ventilating, air-conditioning and refrigeration (HVAC&R) industry towards lower GWP alternatives to HFC refrigerants. Existing lower GWP refrigerant alternatives include hydrocarbons, such as propane (R-290) and isobutane (R-600a), as well as carbon dioxide (R-744), ammonia (R-717), and R-32. In addition, new lower GWP refrigerant alternatives are currently being developed by refrigerant manufacturers, including hydrofluoro-olefin (HFO) and unsaturated hydrochlorofluorocarbon (HCFO) refrigerants. The selection of an appropriate refrigerant for a given refrigeration application should be based on several factors, including the GWP of the refrigerant, the energy consumption of the refrigeration system over its operating lifetime, and leakage of refrigerant over the system lifetime. For example, focusing on energy efficiency alone may overlook the significant environmental impact of refrigerant leakage; while focusing on GWP alone might result in lower efficiency systems that result in higher indirect impact over the equipment lifetime. Thus, the objective of this Collaborative Research and Development Agreement (CRADA) between Honeywell and the Oak Ridge National Laboratory (ORNL) is to develop a Life Cycle Climate Performance (LCCP) modeling tool for optimally designing HVAC&R equipment with lower life cycle greenhouse gas emissions, and the selection of alternative working fluids that reduce the greenhouse gas emissions of HVAC&R equipment. In addition, an experimental evaluation program is used to measure the coefficient of performance (COP) and refrigerating capacity of various refrigerant candidates, which have differing GWP values, in commercial refrigeration equipment. Through a cooperative effort between industry and government, alternative working fluids will be chosen based on maximum reduction in greenhouse gases at minimal cost impact to the consumer. This project will ultimately result in advancing the goals of reducing greenhouse gas emissions through the use of low GWP working fluids and technologies for HVAC&R and appliance equipment, resulting in cost-competitive products and systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An experimental investigation on the characteristics of heat pipes with annular type composite wick structure

Here, an experimental investigation has been conducted on the hydraulic characteristics of annular type wick structures for heat pipes. An experimental facility which can measure porosity, permeability, and effective pore radius of the wick structures in a vacuum condition is established. Nine different types of multi-layered (6 layers in total) composite screen meshes are characterized. Based on the measurement results, a wick structure composed of one layer of 100×100 mesh, three layers of 400×400 mesh, and two layers of 60×60 mesh is determined to have the highest permeability to effective pore radius ratio $(K/r_{eff})$ and was selected as a targeted sample. The wick-to-wall gap effect on the hydraulic characteristics of annular type wick structure is also investigated by measuring the permeability and effective pore radius of the sample wick structure with varying gap widths. The result shows that the permeability increases as the gap increases from 0 mm to 1.2 mm. After a peak at 1.2 mm, the permeability decreases as the gap increases and converges to the value of the case measured without a wall structure. The effective pore radius becomes smaller as the gap increases, making a peak at a gap distance of 1.2 mm. This result implies that there is an optimal point in gap distance which is determined to be 1.2 mm for the selected composite mesh structure. A capillary limitation correlation with a multiplying factor which explains the enhanced performance of the wick due to the gap is suggested. An annular wick type heat pipe is constructed and tested. The capillary limitation of the heat pipe showed good agreement with the suggested correlation.

42 ENGINEERING↗
DOE OSTI2017

Miniaturized Air-to-Refrigerant Heat Exchangers

Air-to-refrigerant Heat eXchangers (HX) are an essential component of Heating, Ventilation, Air-Conditioning, and Refrigeration (HVAC&R) systems, serving as the main heat transfer component. The major limiting factor to HX performance is the large airside thermal resistance. Recent literature aims at improving heat transfer performance by utilizing enhancement methods such as fins and small tube diameters; this has lead to almost exhaustive research on the microchannel HX (MCHX). The objective of this project is to develop a miniaturized air-to-refrigerant HX with at least 20% reduction in volume, material volume, and approach temperature compared to current state-of-the-art multiport flat tube designs and also be capable of production within five years. Moreover, the proposed HX’s are expected to have good water drainage and should succeed in both evaporator and condenser applications. The project leveraged Parallel-Parametrized Computational Fluid Dynamics (PPCFD) and Approximation-Assisted Optimization (AAO) techniques to perform multi-scale analysis and shape optimization with the intent of developing novel HX designs whose thermal-hydraulic performance exceeds that of state-of-the-art MCHX. Nine heat exchanger geometries were initially chosen for detailed analysis, selected from 35+ geometries which were identified in previous work at the University of Maryland, College Park. The newly developed optimization framework was exercised for three design optimization problems: (DP I) 1.0kW radiator, (DP II) 10kW radiator and (DP III) 10kW two-phase HX. DP I consisted of the design and optimization of 1.0kW air-to-water HX’s which exceeded the project requirements of 20% volume/material reduction and 20% better performance. Two prototypes for the 1.0kW HX were prototyped, tested and validated using newly-designed airside and refrigerant side test facilities. DP II, a scaled version DP I for 10kW air-to-water HX applications, also yielded optimized HX designs which met project requirements. Attempts to prototype a 10kW have presented unique manufacturing challenges, especially regarding tube blockages and structural stability. DP III comprised optimizing two-phase HX’s for a 3.0Ton capacity in a heat pump / air-conditioning unit for cooling mode application using R410A as the working fluid. The HX’s theoretically address the project requirements. System-level analysis showed the HX’s achieved up to 15% improvement in COP while also reducing overall unit charge by 30-40%. The project methodology was capable of developing HX’s which can outperform current state-of-the-art MCHX by at least 20% reduction in volume, material volume, and approach temperature. Additionally, the capability for optimization using refrigerant charge as an objective function was developed. The five-year manufacturing feasibility of the proposed HX’s was shown to have a good outlook. Successful prototyping through both conventional manufacturing methods and next generation methods such as additive manufacturing was achieved.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A precisely substituted benzopyran targets androgen refractory prostate cancer cells through selective modulation of estrogen receptors

Dietary consumption of phytoestrogens like genistein has been linked with lower incidence of prostate cancer. The estradiol-like benzopyran core of genistein confers estrogen receptor-β (ER-β) selectivity that imparts weak anti-proliferative activity against prostate cancer cells. DL-2-[4-(2-piperidinoethoxy)phenyl]-3-phenyl-2H-1-benzopyran (BP), a SERM designed with benzopyran core, targeted androgen independent prostate cancer (PC-3) cells 14-times more potently than genistein, ~ 25% more efficiently than tamoxifen and 6.5-times more actively than ICI-182780, without forfeiting significant specificity in comparison to genistein. BP increased apoptosis (annexin-V and TUNEL labeling), arrested cell cycle, and significantly increased caspase-3 activity along with mRNA expressions of estrogen receptor (ER)-β and FasL (qPCR) in PC-3 cells. In classical ERE-luc reporter assay BP behaved as a potent ER-α antagonist and ER-β agonist. Accordingly, it decreased expression of ER-α target PS2 (P < 0.01) and increased expression of ER-β target TNF-α (P < 0.05) genes in PC-3. ER-β deficient PC-3 (siRNA-transfected) was resistant to apoptotic and anti-proliferative actions of SERMs, including stimulation of FasL expression by BP. BP significantly inhibited phosphorylation of Akt and ERK-1/2, JNK and p38 in PC-3 (immunoblotting), and thus adopted a multi-pathway mechanism to exert a more potent anti-proliferative activity against prostate cancer cells than natural and synthetic SERMs. Its precise ER-subtype specific activity presents a unique lead structure for further optimization. - Highlights: • BP with benzopyran core of genistein was identified for ER-β selective action. • BP was 14-times more potent than genistien in targeting prostate cancer cells. • It behaved as a potent ER-β agonist and ER-α antagonist in gene reporter assays. • BP's anti-proliferative action was inhibited significantly in ER-β deficient cells. • BP — a unique lead structure for further optimization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

How to Safely Build 100-plus Kilograms of Weapons-Grade Plutonium

The goal of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project was to reduce compensating errors by utilizing machine learning to both help determine which reactions contain compensating errors as well as optimizing an experiment which can be used to maximally reduce these errors. Compensating errors can adversely impact the predictive power of application simulations, and therefore it’s useful to further constrain nuclear data and reduce these errors. The EUCLID project included building two configurations at the National Criticality Experiments Research Center (NCERC). These two configurations had very different geometries (one was cube-like and one was slab-like). Previous works focus on selection of the target experiment(s), radiation transport capabilities developed in the project, the experiment optimization, and the performance of the experiments. This work will focus only on the safety aspects of performing this experiment, which utilized over 100 kg of weapons-grade plutonium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗