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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,621 records · Page 90

Optimization of Hafnium dimensions for thermal epithermal experiments (TEX) with highly enriched uranium Jemima plates and polyethylene - 14517

The goal of the US Nuclear Criticality Safety Program's Thermal Epithermal experiments (TEX) is to design and conduct new critical experiments to address high priority nuclear data needs from the nuclear criticality safety and nuclear data communities, with special emphasis on intermediate energy (0.625 eV - 100 keV) assemblies. The second set of TEX experiments will use stacks of highly enriched uranium Jemima plates, hafnium (Hf) diluent plates, and polyethylene moderator plates to create assemblies that span a wide range of fission energy spectra, from thermal, through intermediate, to fast energies. As the hafnium plates need to be fabricated and represent a significant cost for this experiment, a parameter study examining experimental configurations with various Hf thicknesses was completed using KENO V.a to determine optimal dimensions. The Hf plate thicknesses were optimized based on three major design criteria: 1) ensuring thermal, intermediate, and fast critical configurations could be built, 2) creating assemblies with a height to diameter ratio of approximately one, and 3) maximizing sensitivity to hafnium isotope cross sections, as predicted by the SCALE/TSUNAMI package. Based on the calculations presented in this report, a hafnium thickness of 0.10 cm will result in compact critical assemblies using the existing Jemima plate inventory, while reducing Hf cost and ensuring adequate sensitivity of the system to hafnium cross sections. (authors)

42 ENGINEERING↗

Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

Patnaik, Surya N.↗

Jig-Shape Optimization of a Low-Boom Supersonic Aircraft

A simple approach for optimizing the jig-shape is proposed in this study. This simple approach is based on an unconstrained optimization problem and applied to a low-boom supersonic aircraft. In this study, the jig-shape optimization is performed using the two-step approach. First, starting design variables are computed using the least-squares surface fitting technique. Next, the jig-shape is further tuned using a numerical optimization procedure based on an in-house object-oriented optimization tool. During the numerical optimization procedure, a design jig-shape is determined by the baseline jig-shape and basis functions. A total of 12 symmetric mode shapes of the cruise-weight configuration, rigid pitch shape, rigid left and right stabilator rotation shapes, and a residual shape are selected as sixteen basis functions. After three optimization runs, the trim shape error distribution is improved, and the maximum trim shape error of 0.9844 inches of the starting configuration becomes 0.00367 inch by the end of the third optimization run.

numerical analysi↗

Coupling Amorphization and Compositional Optimization of Ternary Metal Phosphides toward High-Performance Electrocatalytic Hydrogen Production

Amorphous materials, with abundant active sites and unique electronic configurations, have the potential to outperform their crystalline counterparts in high-performance catalysis for clean energy. However, their synthesis and compositional optimization remain underexplored due to the strict conditions required for their formation. Here, in this study, we report the synthesis of ternary platinum-nickel-phosphorus (PtNiP) amorphous nanoparticles (ANPs) within milliseconds by flash Joule heating, which features ultrafast cooling that enables the vitrification of metal precursors. Through compositional optimization, the Gibbs free energy of hydrogen adsorption for Pt 4 Ni 4 P 1 ANPs is optimized at 0.02 eV, an almost ideal value, even surpassing that of the benchmark metallic platinum catalyst. As a result, the PtNiP ANPs exhibited superior activity in electrocatalytic hydrogen evolution in acid electrolyte (η 10 ∼ 14 mV, Tafel slope ∼ 18 mV dec –1 , and mass activity 5× higher than state-of-the-art Pt/C). Life-cycle assessment and technoeconomic analysis suggest that, compared to existing processes, our approach enables notable reductions in greenhouse gas emission, energy consumption, and production cost for practical electrolyzer catalyst manufacturing.

36 MATERIALS SCIENCE↗

Minimizing the Electromechanical Stresses in Poloidal Field Coils by Optimizing their Numbers and Locations using FREDA Framework

Poloidal field (PF) and central solenoid (CS) coils play a crucial role in sustaining the equilibrium and preserving the shape of highly confined tokamak plasmas. Ensuring that PF coil current and mechanical stress stay within superconducting and structural limitations is an important check in the design assessment. Minimizing the PF coil currents and mechanical stresses influences reliability, cost, and performance. A free-boundary MHD equilibrium code—FreeGS is employed within the fusion reactor design and assessment (FREDA) whole facility modeling (WFM) framework to construct the plasma equilibrium based on the configuration and currents in the PF coils. Here, we present the capability of the FreeGS code to minimize the currents, forces, and electromagnetic stresses on the PF coils by optimizing their number, sizes, structures, and locations while maintaining an MHD stable plasma configuration with a large confinement factor. The workflow is initialized with a configuration of plasma parameters and coils’ locations from the 0-D tokamak build systems code in the FREDA framework. Then, FreeGS is called to calculate the initial equilibrium at the minimum total current in PF coils. Thereafter, FreeGS’s internal optimizer minimizes the currents and hoop and central forces on the PF coils while maintaining the reference equilibrium. Finally, the input configuration is updated with the optimized parameters for equilibria over the ramp-up phase of a burning-plasma operation. FREDA’s whole facility optimization capability, which includes all magnetic field coil systems, blanket, vacuum vessel (VV), first wall, divertor, etc., is under development and out of the scope for this study.

Hassan, Ehab [ORNL] (ORCID:0000000181060301)↗

Analysis, Design and Optimization of Non-Cylindrical Fuselage for Blended-Wing-Body (BWB) Vehicle

Initial results of an investigation towards finding an efficient non-cylindrical fuselage configuration for a conceptual blended-wing-body flight vehicle were presented. A simplified 2-D beam column analysis and optimization was performed first. Then a set of detailed finite element models of deep sandwich panel and ribbed shell construction concepts were analyzed and optimized. Generally these concepts with flat surfaces were found to be structurally inefficient to withstand internal pressure and resultant compressive loads simultaneously. Alternatively, a set of multi-bubble fuselage configuration concepts were developed for balancing internal cabin pressure load efficiently, through membrane stress in inner-stiffened shell and inter-cabin walls. An outer-ribbed shell was designed to prevent buckling due to external resultant compressive loads. Initial results from finite element analysis appear to be promising. These concepts should be developed further to exploit their inherent structurally efficiency.

Mukhopadhyay, V.↗

Fundamental advantages of multijunction thermoradiative cells

Thermoradiative (TR) cells convert heat to work through emission of thermal radiation. Multijunction thermoradiative cells have received little research interest due to the apparent overlap with energy harvesting limits. Through detailed balance formalism, the present study models both single- and multi-diode TR devices, establishing their performance limits and identifying key factors that influence efficiency. For single-junction TR cells, we derive a relationship for the optimal bandgap and show that higher emitter temperatures increase efficiency. When the receiver is at absolute zero temperature, multijunction TR cells have little advantage over the single-junction cell in terms of the maximum output power density. However, for higher receiver temperatures, the multijunction configuration demonstrates significant improvements in both power and efficiency through the optimization of the chemical potential and bandgap of each diode in the complete ensemble. A 500 K emitter and 300 K receiver TR system can achieve a 21.25% increase in efficiency and a 10% improvement in the power density with multijunction architecture compared to a single junction through chemical potential optimization. These findings suggest that multijunction TR cells offer a promising approach to advancing low-grade heat recovery technologies for efficient heat-to-work conversion.

30 DIRECT ENERGY CONVERSION↗

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗

Systematic Propulsion Optimization Tools (SPOT)

This paper describes a computer program written by senior-level Mechanical Engineering students at the University of Alabama in Huntsville which is capable of optimizing user-defined delivery systems for carrying payloads into orbit. The custom propulsion system is designed by the user through the input of configuration, payload, and orbital parameters. The primary advantages of the software, called Systematic Propulsion Optimization Tools (SPOT), are a user-friendly interface and a modular FORTRAN 77 code designed for ease of modification. The optimization of variables in an orbital delivery system is of critical concern in the propulsion environment. The mass of the overall system must be minimized within the maximum stress, force, and pressure constraints. SPOT utilizes the Design Optimization Tools (DOT) program for the optimization techniques. The SPOT program is divided into a main program and five modules: aerodynamic losses, orbital parameters, liquid engines, solid engines, and nozzles. The program is designed to be upgraded easily and expanded to meet specific user needs. A user's manual and a programmer's manual are currently being developed to facilitate implementation and modification.

Bower, Mark↗

Design of imaginary transition gamma booster synchrotron for the Jefferson Lab EIC (JLEIC)

Baseline design of the JLEIC booster synchrotron is presented. Its aim is to inject and accumulate heavy ions and protons at 285 MeV, to accelerate them to about 7 GeV, and finally to extract the beam into the ion collider ring. The Figure-8 ring features two 2600 achromatic arcs configured with negative momentum compaction optics, designed to avoid transition crossing for all ion species during the course of acceleration. The lattice also features a specialized high dispersion injection insert optimized to facilitate the transverse phase-space painting in both planes for multi-turn ion injection. Furthermore, the lattice has been optimized to ease chromaticity correction with two families of sextupoles in each plane. The booster ring is configured with super-ferric, 3 Tesla bends. We are presently launching optimization of the booster synchrotron design to operate in the extreme space-charge dominated regime.

Bogacz, Alex↗

Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

Garcia, Sergio (ORCID:000000023998986X)↗

Status Report on the Water-Based NSTF RCCS Capability: Preparations and Design for the Transformation of the Natural Convection Shutdown Heat Removal Test Facility (NSTF) From Air to Water-based Cooling

The following report provides an overview of the current status of the water-based transformation, including details of the design, geometry, and fabrication of the test section and network piping, for the ½ scale water-based NSTF program at Argonne National Laboratory. In early FY2015, efforts towards a water-based design began in parallel with on-going air-based work, and began by identifying top level objectives of the transformation. Scaling studies and preparation tasks with computational modeling were carried out to guide the design decisions, minimize distortions, and ensure relevant data among scales. This entailed close collaboration with AREVA, whose water-based RCCS (included as part of their 625 MWt SC-HTGR) served as the primary design basis for incorporation into the NSTF. A literature review of previous works was made, with an emphasis placed on similar facilities, relevance to passive decay heat removal, and details pertaining to two-phase flow and measurement techniques. The study focused on input from key designers and a thermal analysis of existing cooling panels (e.g. at TAMU and UW-Madison) and two proposed AREVA cooling panels. A base case was used as a reference point for parametric studies of effects from varying tube spacing (pitch), tube diameter, fin thickness, and materials. Then, a structural analysis was performed to ensure safe material stresses during high temperature operation. Details of the pipe network geometry and guidelines for design of the water storage tank are finally presented, followed by engineering drawings in the appendix. The results of this study have identified a suitable configuration to support both the ANL/DOE project goals and the DOE vision to provide AREVA with data suitable for characterizing the RCCS of their full scale HTGR design. The dimensions were not selected for optimal RCCS performance, but instead to serve as a representative yet bounding configuration for future implementation into a full scale design. Scaling distortions are unavoidable; however, they can be accurately predicted based on earlier works of derived similarity solutions. Flexibility remained a primary design philosophy, and will allow the test facility to easily accommodate future alterations. The final design for the test section and network piping is as follows: riser tubes: 1.5” Schedule 160, 5.91” (150-mm) pitch, 304L stainless, heat transfer panels: 5/16” plates, 4.01” (102-mm) width, full penetration weld to risers, 304L stainless, split into 4 panels per riser separated by horizontal 1/8” gaps, test section: Eight (x8) riser tubes and nine (x9) heat transfer panels, fabricated into banks of two (x2) riser tubes and three (x3) fins, joined to form single section, and network geometry: 4.0” Schedule 40, 304L stainless. Details of the water storage tank will reflect competing effects such as thermal hydraulic phenomena during single and two-phase operation. These considerations include mixing during single-phase jet penetration, condensation of rising bubbles during two-phase discharge, bubble entrainment into the liquid outlet, and liquid carry-over into the steam outlet.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Euler procedure for three-dimensional transonic wall interference

Based on an optimization formulation, a procedure has been developed to evaluate Mach number and angle-of-attack corrections. The Euler equations are assumed to be the flow governing equations. To obtain efficient solutions for the optimization problem, the iterative solutions for the flow variables and the design parameters are simultaneously updated. In addition to the model lift and geometry, the procedure requires pressure measurements near the tunnel walls. The tunnel boundary conditions are based on the introduction of Reimann invariants for a one-dimensional flow normal to the boundary. Computations are performed to verify that the errors introduced by this approximate boundary-condition formulation are acceptably small. The correction scheme is applied to an aircraft configuration in an open jet. The results indicate that the optimization scheme is highly efficient with the rate of convergence of the flow solution nearly equal to the corresponding rate of a regular analysis problem.

Rizk, Magdi H.↗

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

36 MATERIALS SCIENCE↗

A geometric representation scheme suitable for shape optimization

A geometric representation scheme is outlined which utilizes the natural design variable concept. A base configuration with distinct topological features is created. This configuration is then deformed to define components with similar topology but different geometry. The values of the deforming loads are the geometric entities used in the shape representation. The representation can be used for all geometric design studies; it is demonstrated here for structural optimization. This technique can be used in parametric design studies, where the system response is defined as functions of geometric entities. It can also be used in shape optimization, where the geometric entities of an original design are modified to maximize performance and satisfy constraints. Two example problems are provided. A cantilever beam is elongated to meet new design specifications and then optimized to reduce volume and satisfy stress constraints. A similar optimization problem is presented for an automobile crankshaft section. The finite element method is used to perform the analyses.

Tortorelli, Daniel A.↗

Optimizing dynamic wireless charging for electric buses: A data-driven approach to infrastructure planning

The network configuration significantly impacts the performance of dynamic wireless charging (DWC) technology for electric buses. Here, this study presents a novel approach to planning charging infrastructure for public transit using data-driven nonconvex mixed-integer optimization. Integrating DWC and charging station technologies reveals a trade-off between enroute and stationary charging times. Our framework optimizes bus frequency settings and transmitter coil arrangements to minimize operational and infrastructure costs. A case study in Chattanooga, Tennessee, demonstrates the method's effectiveness in mitigating range anxiety and reducing charging expenses. This research implies that integrating DWC technology into public transit systems can enhance the feasibility and cost-effectiveness of electric bus operations, promoting sustainable urban mobility.

33 ADVANCED PROPULSION SYSTEMS↗

Experimental Evaluation of a Microchannel Condenser for R600a Domestic Refrigerators

Domestic refrigerators are among the most common household appliances, accounting for approximately 4% of total household energy consumption, which is higher than that of clothes dryers, lighting, or cooking appliances. With nearly 13 million new domestic refrigerators sold annually, even marginal improvements in their energy performance can yield significant reductions in overall energy demand and cost. This study focuses on enhancing the performance of domestic refrigerators using R600a as the refrigerant. Most domestic refrigerators currently employ wire-and-tube condensers, which are bulky and require a larger refrigerant charge. Given that R600a is classified as an A3 flammable refrigerant under ASHRAE standards, minimizing the system charge while maintaining or improving performance is critical. To address this, a compact and light weight single-pass serpentine microchannel heat exchanger (made of aluminum) was adopted as a condenser, replacing the conventional wire-and-tube configuration. The modified system was experimentally tested according to AHAM test standards, and charge optimization was performed. The results demonstrated that the refrigerator equipped with the microchannel condenser achieved a 5.3% reduction in energy consumption compared to the baseline configuration with wire-and-tube condenser.

Murugan, Muneeshwaran [ORNL] (ORCID:00000003340352↗

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware codesign of optimized neural networks on FPGAs. We present the design and implementation process for the keyword spotting, anomaly detection, and image classification benchmark tasks. The resulting hardware implementations are quantized, configurable, spatial dataflow architectures tailored for speed and efficiency and introduce new generic optimizations and common workflows developed as a part of this work. The full workflow is presented from quantization-aware training to FPGA implementation. The solutions are deployed on system-on-chip (Pynq-Z2) and pure FPGA (Arty A7-100T) platforms. The resulting submissions achieve latencies as low as 20 $\mu$s and energy consumption as low as 30 $\mu$J per inference. We demonstrate how emerging ML benchmarks on heterogeneous hardware platforms can catalyze collaboration and the development of new techniques and more accessible tools.

Borras, Hendrik↗