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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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Necessary conditions of optimality for control problems with state variable inequality constraints, using separating hyperplane theorem
A robust joining approach is critically needed for a Micro-Electro-Mechanical Systems-Lean Direct Injector (MEMS-LDI) application which requires leak free joints with high temperature mechanical capability. Diffusion bonding is well suited for the MEMS-LDI application. Diffusion bonds were fabricated using titanium interlayers between silicon carbide substrates during hot pressing. The interlayers consisted of either alloyed titanium foil or physically vapor deposited (PVD) titanium coatings. Microscopy shows that well adhered, crack free diffusion bonds are formed under optimal conditions. Under less than optimal conditions, microcracks are present in the bond layer due to the formation of intermetallic phases. Electron microprobe analysis was used to identify the reaction formed phases in the diffusion bond. Various compatibility issues among the phases in the interlayer and substrate are discussed. Also, the effects of temperature, pressure, time, silicon carbide substrate type, and type of titanium interlayer and thickness on the microstructure and composition of joints are discussed.
Necessary conditions for optimal lunar trajectories with discontinuous state variables and intermediate point constraints
The role of the existence and sufficiency conditions in the field of optimal control was briefly described. The existence theorems are discussed for general nonlinear systems. However, the sufficiency conditions pertain to "nearly" linear systems with integral convex costs. Moreover, a brief discussion of linear systems with multiple-cost functions is presented.
Necessary conditions for optimal fixed-time powered transfers with multiple coasts and thrusts between circular orbits
Necessary conditions for optimal fixed-time powered transfers with multiple coasts and thrusts between circular orbits
This correspondence presents a suboptimum soft-decision decoding scheme for binary linear block codes based on an iterative search algorithm. The scheme uses an algebraic decoder to iteratively generate a sequence of candidate codewords one at a time using a set of test error patterns that are constructed based on the reliability information of the received symbols. When a candidate codeword is generated, it is tested based on an optimality condition. If it satisfies the optimality condition, then it is the most likely (ML) codeword and the decoding stops. If it fails the optimality test, a search for the ML codeword is conducted in a region which contains the ML codeword. The search region is determined by the current candidate codeword and the reliability of the received symbols. The search is conducted through a purged trellis diagram for the given code using the Viterbi algorithm. If the search fails to find the ML codeword, a new candidate is generated using a new test error pattern, and the optimality test and search are renewed. The process of testing and search continues until either the MEL codeword is found or all the test error patterns are exhausted and the decoding process is terminated. Numerical results show that the proposed decoding scheme achieves either practically optimal performance or a performance only a fraction of a decibel away from the optimal maximum-likelihood decoding with a significant reduction in decoding complexity compared with the Viterbi decoding based on the full trellis diagram of the codes.
Total variation integer optimal control problems admit solutions and necessary optimality conditions via geometric variational analysis. In spite of the existence of said solutions, algorithms which solve the discretized objective suffer from high numerical cost associated with the combinatorial nature of integer programming. Hence, such methods are often limited to small and medium-sized problems. We propose a globally convergent, coordinate descent–inspired algorithm that allows tractable subproblem solutions restricted to a partition of the domain. Our decomposition method solves relatively small trust-region subproblems that modify the control variable on a subdomain only. Given nontrivial subdomain overlap, we prove that a global first-order necessary optimality condition is equivalent to a first-order necessary optimality condition per subdomain. We additionally show that a sufficient decrease is achieved on a single subdomain by way of a trust-region subproblem solver using geometric measure–theoretic arguments, which we integrate with a greedy patch selection to prove convergence of our algorithm. In conclusion, we demonstrate the practicality of our algorithm on a benchmark large-scale, PDE-constrained integer optimal control problem and find that our method is faster than the state of the art.
Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.
Jacobi conditions for singular optimization problems by transforming singular accessory minimum problem into nonclassical nonsingular form, considering optimal control theory applicability
Pre-extracting Li from spent lithium-ion batteries (LIBs) is crucial because the recovery efficiency of Li is low after other critical metals have been extracted. Traditional methods involving black mass roasting followed by water leaching have resulted in the extraction of 76 % Li and 61 % Al. However, pre-leaching Li from pretreated black mass using an oxalic acid solution at both ambient and elevated temperatures significantly improved results, achieving 98.1 % Li, 99.5 % Al, and 100 % Fe extraction while leaving Ni, Co, Mn, and Cu behind under optimal conditions. Oxalic acid crystals were recovered by refrigerating the leach solution at temperatures below 5 °C and were reused with nearly identical leaching efficiency. Selective extraction of Li from the oxalate leach solution was achieved using Cyanex® 936P under optimal conditions. Here, due to its extremely low Li extraction efficiency, Dichloromethane proved unsuitable as a diluent. Comparative extraction tests using Cyanex® 936P, Cyanex® 272, and DEHPA in kerosene demonstrated that Cyanex® 936P is an excellent extractant for Li, effectively separating it from other impurities. Under optimal conditions, 98.8 % of available Li was extracted using 20 % Cyanex® 936P, compared to 51.1 % with Cyanex® 272 and 39.9 % with DEHPA in kerosene. Additionally, stripping Li from Cyanex® 936P using H 2 SO 4 and HCl was explored, with HCl yielding the best performance.
Liquid argon time projection chambers, (LArTPCs), are particle detectors used to collect ionization charge information from particle trajectories, facilitating detailed particle track analysis. They are currently used as particle detectors in major physics projects such as the deep underground neutrino experiment (DUNE), to detect and study the nature of the elusive neutrino particle. TinyTPC is a small scale LArTPC featuring a pixelated readout system (LArPix) that we will use to study liquid argon doping. We expect this doping to enhance the resolution of LAr detectors, especially for low energy particles below 10 MeV which would expand the capabilities of currently running experiments. Housed within a cryostat filled with liquid argon, the TinyTPC will rely on a high and a low voltage system to collect data. In preparation for deployment we found and resolved issues in the HV and LV systems and we determined optimal running conditions in a test vessel. This presentation will go over the detector commissioning process that enabled data taking with the TinyTPC.
This paper reviews a formulation for the design of fixed-order dynamic compensation for flexible mechanical systems which is based upon explicit optimality conditions derived under the minimum data/maximum entropy stochastic modelling approach. The optimality conditions give rise to the novel concept of "optimal projection' which provides significant insight with regard to the compensator structure and permits earlier, more restricted and/or ad hoc design schemes to be seen as special cases of the present formulation.
This study developed machine learning models using a dataset consisting of 963 entries with 24 variables both numerical and categorical to predict H2 production of plastic waste and biomass gasification. The models were utilized to optimize feedstock and process conditions through genetic algorithm to maximize hydrogen production.
The optimal boundary temperature control of the stationary thermally coupled incompressible Navier-Stokes equation is considered. Well-posedness and existence of the optimal control and a necessary optimality condition are obtained. Optimization algorithms based on the augmented Lagrangian method with second order update are discussed. A test example motivated by control of transport process in the high pressure vapor transport (HVPT) reactor is presented to demonstrate the applicability of our theoretical results and proposed algorithm.
This paper describes a self-contained, automated methodology for flow control along with a validation of the methodology for the problem of boundary layer instability suppression. The objective of control is to match the stress vector along a portion of the boundary to a given vector; instability suppression is achieved by choosing the given vector to be that of a steady base flow, e.g., Blasius boundary layer. Control is effected through the injection or suction of fluid through a single orifice on the boundary. The present approach couples the time-dependent Navier-Stokes system with an adjoint Navier-Stokes system and optimality conditions from which optimal states, i.e., unsteady flow fields, and control, e.g., actuators, may be determined. The results demonstrate that instability suppression can be achieved without any a priori knowledge of the disturbance, which is significant because other control techniques have required some knowledge of the flow unsteadiness such as frequencies, instability type, etc.
This paper describes a self-contained, automated methodology for active flow control which couples the time-dependent Navier-Stokes system with an adjoint Navier-Stokes system and optimality conditions from which optimal states, i.e., unsteady flow fields and controls (e.g., actuators), may be determined. The problem of boundary layer instability suppression through wave cancellation is used as the initial validation case to test the methodology. Here, the objective of control is to match the stress vector along a portion of the boundary to a given vector; instability suppression is achieved by choosing the given vector to be that of a steady base flow. Control is effected through the injection or suction of fluid through a single orifice on the boundary. The results demonstrate that instability suppression can be achieved without any a priori knowledge of the disturbance, which is significant because other control techniques have required some knowledge of the flow unsteadiness such as frequencies, instability type, etc. The present methodology has been extended to three dimensions and may potentially be applied to separation control, re-laminarization, and turbulence control applications using one to many sensors and actuators.
Optimal fixed-time impulsive transfer trajectories minimizing characteristic orbital velocity, determining sufficient conditions