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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗
NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗
NASA NTRS1995

Runtime volume visualization for parallel CFD

This paper discusses some aspects of design of a data distributed, massively parallel volume rendering library for runtime visualization of parallel computational fluid dynamics simulations in a message-passing environment. Unlike the traditional scheme in which visualization is a postprocessing step, the rendering is done in place on each node processor. Computational scientists who run large-scale simulations on a massively parallel computer can thus perform interactive monitoring of their simulations. The current library provides an interface to handle volume data on rectilinear grids. The same design principles can be generalized to handle other types of grids. For demonstration, we run a parallel Navier-Stokes solver making use of this rendering library on the Intel Paragon XP/S. The interactive visual response achieved is found to be very useful. Performance studies show that the parallel rendering process is scalable with the size of the simulation as well as with the parallel computer.

Ma, Kwan-Liu↗
NASA NTRS1999

Applications of Parallel Processing in Configuration Analyses

The paper presents the recent progress made towards developing an efficient and user-friendly parallel environment for routine analysis of large CFD problems. The coarse-grain parallel version of the CFL3D Euler/Navier-Stokes analysis code, CFL3Dhp, has been ported onto most available parallel platforms. The CFL3Dhp solution accuracy on these parallel platforms has been verified with the CFL3D sequential analyses. User-friendly pre- and post-processing tools that enable a seamless transfer from sequential to parallel processing have been written. Static load balancing tool for CFL3Dhp analysis has also been implemented for achieving good parallel efficiency. For large problems, load balancing efficiency as high as 95% can be achieved even when large number of processors are used. Linear scalability of the CFL3Dhp code with increasing number of processors has also been shown using a large installed transonic nozzle boattail analysis. To highlight the fast turn-around time of parallel processing, the TCA full configuration in sideslip Navier-Stokes drag polar at supersonic cruise has been obtained in a day. CFL3Dhp is currently being used as a production analysis tool.

Sundaram, Ppchuraman↗
NASA NTRS1997

Progress in Parallel Schur Complement Preconditioning for Computational Fluid Dynamics

We consider preconditioning methods for nonself-adjoint advective-diffusive systems based on a non-overlapping Schur complement procedure for arbitrary triangulated domains. The ultimate goal of this research is to develop scalable preconditioning algorithms for fluid flow discretizations on parallel computing architectures. In our implementation of the Schur complement preconditioning technique, the triangulation is first partitioned into a number of subdomains using the METIS multi-level k-way partitioning code. This partitioning induces a natural 2X2 partitioning of the p.d.e. discretization matrix. By considering various inverse approximations of the 2X2 system, we have developed a family of robust preconditioning techniques. A computer code based on these ideas has been developed and tested on the IBM SP2 and the SGI Power Challenge array using MPI message passing protocol. A number of example CFD calculations will be presented to illustrate and assess various Schur complement approximations.

Barth, Timothy J.↗
DOE OSTI2024

Enhancing Lattice Kinetic Schemes for Fluid Dynamics with Lattice-Equivariant Neural Networks

A new class of equivariant neural networks is presented, hereby dubbed lattice-equivariant neural networks (LENNs), designed to satisfy local symmetries of a lattice structure. The approach develops within a recently introduced framework aimed at learning neural network-based surrogate models’ lattice Boltzmann collision operators. Whenever neural networks are employed to model physical systems, respecting symmetries and equivariance properties has been shown to be key for accuracy, numerical stability, and performance. Here, hinging on ideas from group representation theory, trainable layers are defined whose algebraic structure is equivariant with respect to the symmetries of the lattice cell. In this work, the presented method naturally allows for efficient implementations, in terms of both memory usage and computational costs, supporting scalable training/testing for lattices in two spatial dimensions and higher (in which the size of symmetry group grows). The approach is validated and tested considering 2D and 3D flowing dynamics, both in laminar and turbulent regimes. It is compared with group-averaged-based symmetric networks and with plain, nonsymmetric, networks, showing how the presented approach unlocks the (a posteriori) accuracy and training stability of the former models and the train/inference speed of the latter networks. (LENNs are about one order of magnitude faster than group-averaged networks in 3D.) The work in this paper opens toward practical use of machine learning-augmented lattice Boltzmann CFD in real-world simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Unstructured Adaptive Grid Computations on an Array of SMPs

Dynamic load balancing is necessary for parallel adaptive methods to solve unsteady CFD problems on unstructured grids. We have presented such a dynamic load balancing framework called JOVE, in this paper. Results on a four-POWERnode POWER CHALLENGEarray demonstrated that load balancing gives significant performance improvements over no load balancing for such adaptive computations. The parallel speedup of JOVE, implemented using MPI on the POWER CHALLENCEarray, was significant, being as high as 31 for 32 processors. An implementation of JOVE that exploits 'an array of SMPS' architecture was also studied; this hybrid JOVE outperformed flat JOVE by up to 28% on the meshes and adaption models tested. With large, realistic meshes and actual flow-solver and adaption phases incorporated into JOVE, hybrid JOVE can be expected to yield significant advantage over flat JOVE, especially as the number of processors is increased, thus demonstrating the scalability of an array of SMPs architecture.

Biswas, Rupak↗
NASA NTRS2018

Studies in Support of Venus Aerocapture Utilizing Drag Modulation

Aerocapture has been extensively studied and these studies have shown the benefit for planetary exploration missions. While the traditional approach to aerocapture with lifting configurations and lift-guided modulations have been assessed to be technologically feasible, aerocapture using purely drag modulation was proposed and studied by Prof. Braun and his students. These studies show that if one can assess the feasibility of aerocapture using drag modulation at Venus, and develop tall pole technologies needed at Venus, then this concept is much easier to execute at all other relevant destinations. Based on the above finding, partnered proposals were submitted by Adam Nelessen at JPL and Ethiraj Venkatapathy at Ames in collaboration with Prof. Braun at the University of Colorado, Boulder (UCB). Under this partnership, Ames Research Center (ARC) is working to address some of the key entry technology challenges associated with drag modulation aerocapture at Venus. Drag modulation aerocapture is a simple, scalable, and likely cost-effective way to enhance planetary science missions. The approach envisioned is to design a small spacecraft, that would most likely be a secondary payload, with a removable drag skirt. The vehicle would enter the atmosphere at Venus with a low ballistic coefficient, decelerate rapidly, drop the skirt resulting in a smaller vehicle with a higher ballistic coefficient which would skip out of the atmosphere and enter into a desired orbit. ARC's role in this collaboration is multifold. First of which is to perform design studies on various pre- and post-jettison geometries utilizing a 3-DOF trajectory code to determine the aerodynamics and aerothermodynamics of the vehicles and evaluate viable thermal protection material system designs. Once these design studies are complete, Ames will then perform higher fidelity CFD and TPS sizing to further design the vehicles. Second, the multi-body separation dynamics of the drag modulation event will be explored using both CFD simulations (CART3-D and US3D) as well as possible ballistic range testing. ARC's tools and expertise have been used to assess and advise on the selection of the separating configuration. In addition to the preliminary evaluation, ARC will provide tools and expertise to UCB team members to further assess aerodynamic interactions between the separating bodies and provide guidance as to the feasibility of stable transition.

Beck, Robin↗
DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗
DOE OSTI2023

State-of-the-art and review of condensation heat transfer for small modular reactor passive safety: Computational studies

The small modular reactor (SMR) is a promising option with added safety features, economical manufacturing, reliable parts, portability, and scalable energy capacity that emits no greenhouse gas during its operating lifespan. The SMR safety systems, however, need to be evaluated for design and licensing. Thus, they require the verification and validation of the physics models and correlations. This study focuses on state-of-the-art condensation heat transfer analysis and a review of previous studies related to the passive containment cooling system (PCCS) of a SMR. In the PCCS of a SMR, due to its smaller size containment, filmwise condensation is dominant and therefore emphasized in this study. Furthermore, previous condensation heat transfer studies for PCCSs did not make the SMR the primary focus, so a critical review for formulating the state-of-the-art is necessary. A previous review covered experimental condensation heat transfer studies with a brief overview of associated test facilities and empirical correlations. This review covers the empirical, resistance-layer and theoretical (numerical and commercial CFD) approaches.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Development of MOSCATO: A CFD-Level Electrochemistry and Corrosion Simulator for Molten Salt Systems

For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Measurements to Understand the Flow Mechanisms Contributing to Tandem-Rotor Outwash

Downwash and outwash characteristics of a model-scale tandem-rotor system in the presence of the ground were analyzed by identifying and understanding the physical mechanisms contributing to the observed flow field behavior. A building block approach was followed in simplifying the problem, separating the effects of the fuselage, effects of one rotor on the other, etc. Flow field velocities were acquired in a vertical plane at four aircraft azimuths of a small-scale tandem rotor system using the particle image velocimetry (PIV) technique for radial distances up to 4 times the rotor diameter. Results were compared against full-scale CH-47D measurements. Excellent correlation was found between the small- and full-scale mean flow fields (after appropriate normalization using rotor and wall jet parameters). Following the scalability analysis, the effect of rotor height on the outwash was also studied. Close to the aircraft, an increase in rotor height above ground decreased the outwash velocity at all aircraft azimuths. However, farther away, the longitudinal and lateral axes of the aircraft showed increasing and decreasing outwash velocities, respectively, with increasing rotor height. Measurements also indicated the presence of large-scale (of the size of the rotor height) shear-layer vortical structures along the ground that could be the source of low-frequency (approximately 1 Hz) flow variation observed in the full-scale measurements. Flow visualization studies and PIV measurements were also made on jets of different sizes to complement the observations made on rotors wherever possible. Baseline rotor measurements were made out-of-ground effect to understand the nature of inflow distribution for realistic rotor configurations and their modified characteristics in the presence of ground. Lastly, a feasibility study on applying high-fidelity CFD simulations for outwash study was conducted using Helios to model an isolated rotor configuration IGE at full scale Reynolds number. The results were encouraging and demonstrated the practical challenges associated with predicting rotor outwash.

Measurements↗
NASA NTRS2015

Measurements to Understand the Flow Mechanisms Contributing to Tandem-Rotor Outwash

Downwash and outwash characteristics of a model-scale tandem-rotor system in the presence of the ground were analyzed by identifying and understanding the physical mechanisms contributing to the observed flow field behavior. A building block approach was followed in simplifying the problem, separating the effects of the fuselage, effects of one rotor on the other, etc. Flow field velocities were acquired in a vertical plane at four aircraft azimuths of a small-scale tandem rotor system using the particle image velocimetry (PIV) technique for radial distances up to 4 times the rotor diameter. Results were compared against full-scale CH-47D measurements. Excellent correlation was found between the small- and full-scale mean flow fields (after appropriate normalization using rotor and wall jet parameters). Following the scalability analysis, the effect of rotor height on the outwash was also studied. Close to the aircraft, an increase in rotor height above ground decreased the outwash velocity at all aircraft azimuths. However, farther away, the longitudinal and lateral axes of the aircraft showed increasing and decreasing outwash velocities, respectively, with increasing rotor height. Measurements also indicated the presence of large-scale (of the size of the rotor height) shear-layer vortical structures along the ground that could be the source of low-frequency (approximately 1 Hz) flow variation observed in the full-scale measurements. Flow visualization studies and PIV measurements were also made on jets of different sizes to complement the observations made on rotors wherever possible. Baseline rotor measurements were made out-of-ground effect to understand the nature of inflow distribution for realistic rotor configurations and their modified characteristics in the presence of ground. Lastly, a feasibility study on applying high-fidelity CFD simulations for outwash study was conducted using Helios to model an isolated rotor configuration IGE at full-scale Reynolds number. The results were encouraging and demonstrated the practical challenges associated with predicting rotor outwash.

Ramasamy, Manikandan↗
DOE OSTI2023

Heat Exchanger Intensification Through Powder Processing and Enhanced Design (HIPPED)

High intensity, supercritical CO 2 heat exchangers capable of operating at temperatures between 800 and 1100 ˚C and pressures greater than 80 bar are considered a critical component for high efficiency power generation and a range of next-generation industrial processes. A novel heat exchanger concept was proposed in this project. The proposed concept involved a plate-type architecture with three-dimensional fins, called twisted s-shaped fins, that capitalize on additive manufacturing. Four manufacturing processes were studied to realize the complex envisioned design: press and sinter of metallic powders, directed energy deposition, laser powder bed fusion, and a novel powder bed process involving a photoreactive polymer called SEAM (scalable expeditious additive manufacturing), that later was developed at MSU. The team was able to manufacture subscale heat exchangers using laser powder bed fusion (LPBF) and SEAM. The team settled on using SEAM as a final manufacturing approach due to its very low cost. The SEAM-manufactured heat exchanger, however, when tested under pressure, exhibited leaks and the process still requires further development beyond the timeline of the project. To support the heat exchanger design and testing, the team developed a new high temperature flow loop allowing testing under high temperature and pressure. Computational modeling of the heat exchanger was also performed for the twisted s-shaped fins and the computations were validated with experimental data. The new twisted fins resulted in enhanced heat transfer and reduced pressure drop. To further minimize pressure drop in the plate heat exchanger, the team also developed a simplified model for flow maldistribution in the manifolds, providing a new tool for designers that eliminates the need for complex CFD calculations. A technoeconomic analysis was also developed to contrast SEAM with LPBF, this tool indicated that SEAM is several times less costly and offer significant promises as a rapid low cost additive manufacturing process for metallic systems.

30 DIRECT ENERGY CONVERSION↗