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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

Pappas, Iosif↗

NASA NTRS2003

Integrated Propulsion/Vehicle System Structurally Optimized

Ongoing research and testing are essential in the development of air-breathing hypersonic propulsion technology, and this year some positive advancement was made at the NASA Glenn Research Center. Recent work performed for GTX, a rocket-based combined-cycle, single-stage-to-orbit concept, included structural assessments of both the engine and flight vehicle. In the development of air-breathing engine technology, it is impractical to design and optimize components apart from the fully integrated system because tradeoffs must be made between performance and structural capability. Efforts were made to control the flight trajectory, for example, to minimize the aerodynamic heating effects. Structural optimization was applied to evaluate concept feasibility and was instrumental in the determination of the gross liftoff weight of the integrated system. Achieving low Earth orbit with even a small payload requires an aggressive approach to weight minimization through the use of lightweight, oxidation-resistant composite materials. Assessing the integrated system involved investigating the flight trajectory to determine where the critical load events occur in flight and then generating the corresponding environment at each of these events. Structural evaluation requires the mapping of the critical flight loads to finite element models, including the combined effects of aerodynamic, inertial, combustion, and other loads. NASA s APAS code was used to generate aerodynamic pressure and temperature profiles at each critical event. The radiation equilibrium surface temperatures from APAS were used to predict temperatures through the thickness. Heat transfer solutions using NASA's MINIVER code and the SINDA code (Cullimore & Ring Technologies, Littleton, CO) were calculated at selective points external to the integrated vehicle system and then extrapolated over the entire exposed surface. FORTRAN codes were written to expedite the finite element mapping of the aerodynamic heating effects for the internal structure.

Hunter, James E.↗

NASA NTRS2019

Electron Beam Welding of Pure Tungsten Hex Cans for Nuclear Thermal Propulsion Engines

Nuclear thermal propulsion (NTP) is an in-space propulsion method currently being developed at the NASA Marshall Space Flight Center (MSFC). NTP systems are a high specific impulse (750–1,100 s), high thrust (15,000–250,000 lbf ) method of propulsion which have the potential to allow for faster transit times when optimizing for high ΔV. In the nuclear rocket engine, the heat from the nuclear fission reaction is transferred to a low molecular mass propellant (such as hydrogen). Hot propellant is expanded through a nozzle to generate thrust. Development of ceramic metal (cermet) fuel systems for NTP applications is currently ongoing at MSFC. In cermet fuel systems, ceramic fissile fuel particles such as uranium nitride or uranium dioxide are dispersed within a net-shaped, high-density structural matrix. The composite material is cladded by a protective metal structure to make up an NTP fuel element. Cladding materials must be able to withstand the demanding operating conditions required of the engine as well as retain a hermetic seal to allow for retention of fuel element structural integrity, prevent hydrogen attack or migration of the ceramic fuel, and limit release of fission products during operation. For NTP applications, tungsten is a prime material for both the metal matrix and cladding in cermet fuel systems because of its high melting point, high temperature strength, and compatibility with hot hydrogen. If a weld in tungsten with the capability of holding a hermetic seal is achievable, tungsten becomes a strong candidate for NTP applications. This Technical Memorandum focuses on determining the weldability of pure tungsten using electron beam welding (EBW). Tungsten appears well suited for NTP applications, but it has a high ductile to brittle transition temperature (DBTT) dependent upon chemical composition, structure/stress distribution, and mechanical conditions. Therefore, it is highly subject to brittle fracture. Because of its high susceptibility to brittle fracture, it is very difficult to weld. EBW was chosen for joining pure tungsten because of its low heat input compared to gas tungsten arc welding. Reduced heat input can be directly correlated with an increase in ductility of a tungsten weld. EBW is a high energy density welding process in which a stream of electrons penetrates a weld joint in a deep, narrow spike in contrast to a broad gas tungsten arc weld pool. The investigation initially focused on EBW of tungsten plates of both 0.01 in and 0.03 in thickness to determine if EBW could weld pure tungsten without the presence of visual defects—particularly cracking—in the welds. Variation in the weld procedure and post-weld heat treatment (PWHT) was used to improve the surface appearance of flat EBWs on a pure tungsten sheet. The investigation moved on to weld 0.05-in-thick hexagonal tungsten cans with a weld joint thickness of 0.025 in. The goal for welding the pure tungsten hex cans was to avoid any visual surface defects and generate a weld capable of a hermetic seal. This proved difficult. Cold welds commonly exhibited porosity that leaked air. Hot welds exhibited cracks, typically observed immediately after welding. Later welds were preheated to increase ductility and decrease the likelihood of through-thickness cracking. PWHT was used to arrest microcrack growth both in the flat weld samples and hexagonal weld samples.

Courtright, Z. S.↗

NASA NTRS1995

The Development and Use of a Flight Optimization System Model of a C-130E Transport Aircraft

The Systems Analysis Branch at NASA Langley Research Center conducts a variety of aircraft design and analyses studies. These studies include the prediction of characteristics of a particular conceptual design, analyses of designs that already exist, and assessments of the impact of technology on current and future aircraft. The FLight OPtimization System (FLOPS) is a tool used for aircraft systems analysis and design. A baseline input model of a Lockheed C-130E was generated for the Flight Optimization System. This FLOPS model can be used to conduct design-trade studies and technology impact assessments. The input model was generated using standard input data such as basic geometries and mission specifications. All of the other data needed to determine the airplane performance is computed internally by FLOPS. The model was then calibrated to reproduce the actual airplane performance from flight test data. This allows a systems analyzer to change a specific item of geometry or mission definition in the FLOPS input file and evaluate the resulting change in performance from the output file. The baseline model of the C-130E was used to analyze the effects of implementing upper wing surface blowing on the airplane. This involved removing the turboprop engines that were on the C-130E and replacing them with turbofan engines. An investigation of the improvements in airplane performance with the new engines could be conducted within the Flight Optimization System. Although a thorough analysis was not completed, the impact of this change on basic mission performance was investigated.

Desch, Jeremy D.↗

NASA NTRS1993

Multidisciplinary Design Techniques Applied to Conceptual Aerospace Vehicle Design

Multidisciplinary design optimization (MDO) is an emerging discipline within aerospace engineering. Its goal is to bring structure and efficiency to the complex design process associated with advanced aerospace launch vehicles. Aerospace vehicles generally require input from a variety of traditional aerospace disciplines - aerodynamics, structures, performance, etc. As such, traditional optimization methods cannot always be applied. Several multidisciplinary techniques and methods were proposed as potentially applicable to this class of design problem. Among the candidate options are calculus-based (or gradient-based) optimization schemes and parametric schemes based on design of experiments theory. A brief overview of several applicable multidisciplinary design optimization methods is included. Methods from the calculus-based class and the parametric class are reviewed, but the research application reported focuses on methods from the parametric class. A vehicle of current interest was chosen as a test application for this research. The rocket-based combined-cycle (RBCC) single-stage-to-orbit (SSTO) launch vehicle combines elements of rocket and airbreathing propulsion in an attempt to produce an attractive option for launching medium sized payloads into low earth orbit. The RBCC SSTO presents a particularly difficult problem for traditional one-variable-at-a-time optimization methods because of the lack of an adequate experience base and the highly coupled nature of the design variables. MDO, however, with it's structured approach to design, is well suited to this problem. The result of the application of Taguchi methods, central composite designs, and response surface methods to the design optimization of the RBCC SSTO are presented. Attention is given to the aspect of Taguchi methods that attempts to locate a 'robust' design - that is, a design that is least sensitive to uncontrollable influences on the design. Near-optimum minimum dry weight solutions are determined for the vehicle. A summary and evaluation of the various parametric MDO methods employed in the research are included. Recommendations for additional research are provided.

Olds, John Robert↗

NASA NTRS1996

Application of Optimization Techniques to Design of Unconventional Rocket Nozzle Configurations

Several current rocket engine concepts such as the bell-annular tri-propellant engine, and the linear aerospike being proposed for the X-33 require unconventional three dimensional rocket nozzles which must conform to rectangular or sector shaped envelopes to meet integration constraints. These types of nozzles exist outside the current experience database, therefore, the application of efficient design methods for these propulsion concepts is critical to the success of launch vehicle programs. The objective of this work is to optimize several different nozzle configurations, including two- and three-dimensional geometries. Methodology includes coupling computational fluid dynamic (CFD) analysis to genetic algorithms and Taguchi methods as well as implementation of a streamline tracing technique. Results of applications are shown for several geometeries including: three dimensional thruster nozzles with round or super elliptic throats and rectangualar exits, two- and three-dimensional thrusters installed within a bell nozzle, and three dimensional thrusters with round throats and sector shaped exits. Due to the novel designs considered for this study, there is little experience which can be used to guide the effort and limit the design space. With a nearly infinite parameter space to explore, simple parametric design studies cannot possibly search the entire design space within the time frame required to impact the design cycle. For this reason, robust and efficient optimization methods are required to explore and exploit the design space to achieve high performance engine designs. Five case studies which examine the application of various techniques in the engineering environment are presented in this paper.

Follett, W.↗

NASA NTRS1982

Energy efficient engine: Low-pressure turbine subsonic cascade component development and integration program

A subsonic cascade test program was conducted to provide technical data for optimizing the blade and vane airfoil designs for the Energy Efficient Engine Low-Pressure Turbine component. The program consisted of three parts. The first involved an evaluation of the low-chamber inlet guide vane. The second, was an evaluation of two candidate aerodynamic loading philosophies for the fourth blade root section. The third part consisted of an evaluation of three candidate airfoil geometries for the fourth blade mean section. The performance of each candidate airfoil was evaluated in a linear cascade configuration. The overall results of this study indicate that the aft-loaded airfoil designs resulted in lower losses which substantiated Pratt & Whitney Aircraft's design philosophy for the Energy Efficient Engine low-pressure turbine component.

Sharma, O. P.↗

DOE OSTI2021

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.14

Sandia's Dakota software supports science and engineering transformation through advanced exploration of simulations. Specifically, Dakota manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-10468 O

Adams, BrianM.↗

DOE OSTI2021

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.16

Adams, BrianM.↗

DOE OSTI2025

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Sequence of polyhedral relaxations for nonlinear univariate functions

Here, given a nonlinear, univariate, bounded, and differentiable function f(x), this article develops a sequence of Mixed Integer Linear Programming (MILP) and Linear Programming (LP) relaxations that converge to the graph of f(x) and its convex hull, respectively. Theoretical convergence of the sequence of relaxations to the graph of the function and its convex hull is established. For nonlinear non-convex optimization problems, the relaxations presented in this article can be used to construct tight MILP and LP relaxations. These MILP and the LP relaxations can also be used with MILP-based and spatial branch-and-bound based global optimization algorithms, respectively.

42 ENGINEERING↗

DOE OSTI2021

Tardigrade (NA-22 Quarterly Report)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 24 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Tardigrade (Quarterly Report Q1 FY2022)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 27 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Automatic Segmentation of Building Envelope Point Cloud Data Using Machine Learning

About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.

Maldonado Puente, Bryan↗

DOE OSTI2023

Intelligent network slicing and policy-based routing engine

One or more aspects of the present disclosure are directed to network optimization solutions provided as software agents (applications) executed on network nodes in a heterogenous multi-vendor environment to provide cross-layer network optimization and ensure availability of network resources to meet associated Quality of Experience (QoE) and Quality of Service (QoS). In one aspect, a network slicing engine is configured to receive at least one request from at least one network endpoint for access to the heterogeneous multi-vendor network for data transmission; receive information on state of operation of a plurality of communication links between the plurality of nodes; determine a set of data transmission routes for the request; assign a network slice for serving the request; determine, from the set of data transmission routes, an end-to-end route for the network slice; and send network traffic associated with the request using the network slice and over the end-to-end route.

Mody, Apurva N.↗

DOE OSTI2022

The value of in-reservoir energy storage for flexible dispatch of geothermal power

Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.

15 GEOTHERMAL ENERGY↗

NASA NTRSDate not supplied

High-Fidelity Aerodynamic Analysis and Optimization of the SUSAN Electrofan Concept

The Subsonic Single Aft Engine (SUSAN) Electrofan is a new single-aisle regional jet class transport aircraft being studied by NASA, which benefits from reduced fuel consumption through a reduction in the number of hydrocarbon fuel-burning engines from two to one. This is achieved through a hybrid-electric system architecture, where a series of generators are used to extract power from a fuel-consuming tail cone thruster to drive electric propulsors mounted on the wings. To support the development of these propulsion systems, which involve high levels of propulsion-airframe integration, this paper presents high-fidelity analyses through computational fluid dynamics (CFD) simulations provided by the Launch Ascent and Vehicle Aerodynamics (LAVA) framework, which also includes capabilities for modeling the effect of active propulsors. A trade space exploration is performed to investigate the advantages of several wing mounted distributed electric propulsion system configurations, including over-wing, under-wing, and trailing-edge arrangements. For the aft fuselage propulsor, focus is on the sizing of the inlet, core, and bypass ducts. High-fidelity aerodynamic shape optimization is also used to help minimize the inlet distortion intensity experienced by the aft propulsor fan through a reshaping of the fuselage.

CAS↗