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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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OTERR is a python code designed to couple an external transport/depletion capability with an internal genetic algorithm for fuel reloading optimization. OTERR stores the state information required for creating neutronics code input, output from ARC codes, and data required for running optimization in HDF5 files.
We present the beamline design for a high power 8 MeV electron injector to be installed at the Low Energy Recirculator Facility (LERF). The injector is designed to provide beam power of up to 80 kW for testing positron source target candidates. We discuss the results of beam dynamics simulations conducted using the General Particle Tracer (GPT) software, where electromagnetic beamline elements were optimized using multi-objective genetic algorithm tools. Additionally, we present preliminary studies on secondary particle generation from both rotating solid and free surface liquid metal jet targets described elsewhere in this workshop.
Here, we present the beamline design for a high power 8 MeV electron injector to be installed at the Low Energy Recirculator Facility (LERF). The injector is designed to provide beam power of up to 80 kW for testing positron source target candidates. We discuss the results of beam dynamics simulations conducted using the General Particle Tracer (GPT) software, where electromagnetic beamline elements were optimized using multi-objective genetic algorithm tools. Additionally, we present preliminary studies on secondary particle generation from both rotating solid and free surface liquid metal jet targets described elsewhere in this workshop.
Optimized designs were achieved using a genetic algorithm to evaluate multi-objective trade space, including Mean-Time-Between-Failure (MTBF) and volumetric power density. This work provides a foundational platform that can be used to optimize additional power converters, such as an inverter for the EV traction drive system as well as trade-offs in thermal management due to the use of different device substrate materials.
Neuromuscular junctions (NMJs), specialized synapses between motor neurons and muscle fibers, are essential for muscle activity. A simple and reproducible cell-based in vitro NMJ platform is needed to test the impact of chemicals on the neuron-muscle communication. Our platform utilizes genetically modified neurons and muscle cells, optimized culture conditions, and commercially available multielectrode array system for recording action potentials. Neuronal cells (NSC34) were optogenetically modified with channelrhodopsin chimera to allow for simultaneous, light-mediated, millisecond-precise activation of neuronal population. This signal is propagated through functional synapses to the muscle fibers. Muscle cells (C2C12) were modified by incorporating gap junction protein (Connexin-43) to improve intracellular communication without affecting muscle differentiation. This communication between muscle fibers resulted in better signal propagation and signal strength. Optimized culture medium facilitated the growth and differentiation of both cell types together. Our system was validated using vecuronium, a muscle relaxant, which abolished the muscle response. This in vitro model provides a unique tool for establishing a NMJ platform that is easy to record and analyze. Potential applications include nondestructive long-term screening of drugs affecting the NMJ.
High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.
Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets. This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. Further, the study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.
Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.
Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.
Nonlinear beam dynamics optimization is essential in a low emittance storage ring design. Multi-objective optimization algorithms are needed in order to simultaneously optimize the dynamic aperture and the momentum aperture. In this study we demonstrate the application of a highly efficient stochastic optimization algorithm, the multi-generation Gaussian process optimizer (MG-GPO), to storage ring nonlinear dynamics optimization by successfully applying the method to the SPEAR3 upgrade lattice. It is shown that the new method, owing to its capability of selecting high rank candidates from a large number of trial solutions, converges significantly faster than the commonly used traditional algorithms, multi-objective genetic algorithms (MOGA) and particle swarm optimization (PSO).
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.
Microbes that generate copious amounts of hydrogen (H 2 ) via dark fermentation are a promising means to evolve and improve renewable biofuels. Many anaerobic hyperthermophilic archaea, such as the fast-growing, genetically tractable, heterotroph Thermococcus kodakarensis, produce generous quantities of H2 and provide an idealized platform to further optimize naturally high levels of biohydrogen reduction. Precise genetic manipulations and modifications to growth conditions have already resulted in substantial increases to H2 output but additional improvements are desired. An unexamined and potentially valuable route towards increased H 2 production is to tether select electron donor and acceptor proteins together to reroute and maximize the flow of electrons towards H 2 production. Such strategies have shown promise in Bacteria and Eukarya but have not yet been investigated in thermophilic Archaea. Here, we generate and evaluate twelve novel T. kodakarensis strains wherein a proteinaceous electron carrier (a ferredoxin, Fd) is physically tethered to the membrane-bound-hydrogenase (MBH), the sole H 2 producing enzyme, to direct electron flux towards biohydrogen generation. Growth assessments and H 2 output measurements demonstrate that strains encoding protein-fusions evolve up to ~ 40% more H2 per cell than the host strain. Eliminating H 2 consumption and alternative routes of electron sinks in concert with protein tethering further increased H2 output per cell for a maximum increase of ~ 66% over the host strain. Our results demonstrate that rerouting electron flux via protein tethering coupled with the elimination of reductant sinks is a promising means towards improved biohydrogen production in T. kodakarensis. KEY POINTS: Protein tethering between redox proteins can reroute electron flux in vivo. Enforced protein proximity results in ~ 40% increases in H2 production per cell. Protein-tethering provides a generalizable framework to redirect redox metabolism.
A current-source inverter (CSI) has the natural capability of boosting the output voltage which is a notable advantage over voltage source inverter (VSI) in traction drive applications. This paper investigates the voltage boosting feature of the CSI to improve the overall efficiency of the CSI-based surface permanent magnet (SPM) machine drive system in the constant power region. The effects of the boost function on the total system losses, including the machine copper loss, core loss, and magnet loss, as well as the device conduction and switching loss in the CSI and dc/dc converter, are described using analytical models. The operating characteristics of the machine and CSI are validated by 2-D finite-element analysis (FEA) and simulations. Based on the loss model, the effects of the modulation index on the overall drive system losses and power factor have been analyzed. A genetic algorithm has been used to optimize the CSI's boost ratio, demonstrating that the drive system efficiency can be increased by 1% to 2.5% along the constant-power regime envelope by using the boost function.
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This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.
Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.
High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.
Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.