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NASA NTRS2000

Affordable, Robust Ceramic Joining Technology (ARCJoinT) Given 1999 R and D 100 Award

Advanced ceramics and fiber-reinforced ceramic matrix composites with high strength and toughness, good thermal conductivity, thermal shock resistance, and oxidation resistance are needed for high-temperature structural applications in advanced high-efficiency and high-performance engines, space propulsion components, and land-based systems. The engineering designs of these systems require the manufacturing of large parts with complex shapes, which are either quite expensive or impossible to fabricate. In many instances, it is more economical to build complex shapes by joining together simple geometrical shapes. Thus, joining has been recognized as an enabling technology for the successful utilization of advanced ceramics and fiber-reinforced composite components in high-temperature applications. However, such joints must retain their structural integrity at high temperatures and must have mechanical strength and environmental stability comparable to those of the bulk materials. In addition, the joining technique should be robust, practical, and reliable. ARCJoinT, which is based on the reaction-forming approach, is unique in terms of producing joints with tailorable microstructures. The formation of joints by this approach is attractive since the thermomechanical properties of the joint interlayer can be tailored to be very close to those of the base materials. In addition, high-temperature fixturing is not needed to hold the parts at the infiltration temperature. The joining process begins with the application of a carbonaceous mixture in the joint area, holding the items to be joined in a fixture, and curing at 110 to 120 C for 10 to 20 min. This step fastens the pieces together. Then, silicon or a silicon alloy in tape, paste, or slurry form is applied around the joint region and heated to 1250 to 1425 C (depending on the type of infiltrant) for 10 to 15 min. The molten silicon or silicon-refractory metal alloy reacts with carbon to form silicon carbide with controllable amounts of silicon and other phases as determined by the alloy composition. Joint thickness can be readily controlled through adjustments of the properties of the carbonaceous paste and the applied fixturing force. The photograph shows various shapes of silicon-carbide-based ceramics and fiberreinforced composites that have been joined using ARCJoinT. Thermomechanical and thermochemical characterization of joints is underway for a wide variety of silicon-carbidebased advanced ceramics and fiber-reinforced composites under the hostile environments that will be encountered in engine applications. ARCJoinT, which was developed by researchers at the NASA Glenn Research Center at Lewis Field, received R&D Magazine's prestigious R&D 100 Award in 1999.

Singh, Mrityunjay↗

DOE OSTI2021

A systems engineering framework for the optimization of food supply chains under circular economy considerations

The current linear “take-make-waste-extractive” model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, in this work, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

sta-imex (scripts to investigate stability of IMEX methods)

Set of Matlab scripts to investigate properties of IMEX time stepping methods. Scripts include discretization of a system of linearized equations. One script plots numerical dispersion against analytical dispersion of the system. Another set of scripts uses the system to break it into stiff and non stiff part to investigate stability of Implicit-Explicit Runge-Kutta methods. Scripts are based on Thuburn, J. and Woollings, T. J., Vertical Discretizations for Compressible Euler Equation Atmospheric Models Giving Optimal Representation of Normal Modes , 2005 and Lock, S.-J. and Wood, N. and Weller, H., Numerical analyses of Runge-Kutta implicit-explicit schemes for horizontally explicit, vertically implicit solutions of atmospheric models, 2014 expanding ideas from these two papers. Scripts are used to develop IMEX schemes. SAND2020-12594 M 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.

Guba, Oksana↗

NASA NTRS2019

Modern Modal Testing: A Cautionary Tale

Over the past 50 years, great advances have been achieved in both analytical modal analysis (i.e. finite element models and analysis) and experimental modal analysis (i.e. modal testing) in aerospace and other fields. With the advent of more powerful computers, higher performance instrumentation and data acquisition systems, and powerful linear modal extraction tools, analysts and test engineers have a breadth and depth of technical resources only dreamed of by our predecessors. However, some observed recent trends indicate that hard lessons learned are being forgotten or ignored, and possibly fundamental concepts are not being understood. These trends have the potential of leading to the degradation of the quality of and confidence in both analytical and test results. These trends are a making of our own doing, and directly related to having ever more powerful computers, programmatic budgetary pressures to limit analysis and testing, and technical capital loss due to the retirement of the senior component of a bimodal workforce. This paper endeavors to highlight some of the most important lessons learned, common pitfalls to hopefully avoid, and potential steps that may be taken to help reverse this trend.

bimodal workforce↗

NASA NTRS2019

Modern Modal Testing: A Cautionary Tale IMAC XXVII

bimodal workforce↗

DOE OSTI2019

Coal-fueled Pressurized Chemical Looping Combustion with a Spouting Fluidized Bed

The goal of this final project report is to comprehensively summarize the work conducted on project DE-FE0025098. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky Center for Applied Energy Research (UKy-CAER) (Recipient) has developed a coal-based combined cycle integrated with a pressurized chemical looping combustion (PCLC) technology to achieve high plant efficiency while capturing CO 2 at low cost. The project involved the design, fabrication, commissioning, parametric testing, and performance validation of the spouted bed approach as coal feeding to narrow the major near-term technical gaps that impede the application of PCLC into solid fuel and scale-up. Specifically, UKy-CAER provided detailed front-end design of a 50 kWth PCLC facility, produced approximately 500 lbs of oxygen carrier (OC) from industrial by-product, Red Mud in addition to commercially available ilmenite, and completed a 70+ hour-long experimental campaign to evaluate the coal feed system and performance and flexibility of the 50 kWth facility with various coal, operating pressures, fluidization agent, and OCs. Project results verified UKy-CAER’s proposed approach to address the major technical gaps of solid-fueled PCLC technology which includes: (1) cost-effective materials for OCs with high reactivity, cyclic capacity, chemical stability, and high resistance to agglomeration with coal ash; (2) fast reaction rates during solid-solid chemical looping combustion (CLC) for reduction of the OC inventory and reactor size; (3) simple and effective ash separation from binary mixtures of the OCs and solid fuel ash; (4) spouting bed coal feeding to suppress OC agglomeration in the reducer resulting from the initial coal devolatilization step, coal tar formation; and (5) pollutant removal to avoid sulfur accumulation in the system or their emission into the hot spent air stream which could result in a second train for SOx removal and potentially cause failure of the flue gas turbine blade. UKy-CAER’s economical cycling material and novel spouted bed reducer configuration shows promise, as summarized in this report, as an effective means for improved plant efficiency with reduced process complexity.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2002

Jet Propulsion Laboratory: Annual Report 2001

For many years before the clock counted down to midnight and the arrival of the year 2000, the world had anticipated 2001 as a special time and a new era. Now we know that 2001 will be a year none of us will ever forget. We began a new year, a new century, and a new millennium. Yet after September 11, the world in many ways seems profoundly changed. On that day we witnessed both the worst and best in human nature. Space exploration is one pursuit that points towards the best instincts in our nature. And certainly the pioneering spirit, so much a part of the American character, is a value deeply embedded into all the work we undertake at JPL. We are privileged that the nation has entrusted us with exploring space on its behalf. And we are fortunate to find ourselves part of two of the world's most accomplished institutions - NASA and the California Institute of Technology. Looking back over the past four decades, JPL has carried out an initial reconnaissance of nearly all of the solar systems planets. Today we have more than a dozen missions flying, and many more in various stages of development. Our challenge now is to create missions that help us understand these places more deeply. And in addition to exploring and understanding our solar system, we want to discover neighboring solar systems and explore them as well. The 21st century is upon us. So is a tremendous era of space exploration. There were many events in 2001 to celebrate, one being the arrival of the Mars Odyssey orbiter, which joins the Mars Global Surveyor orbiter in providing continuous coverage of the red planet. This is a major step in establishing a permanent robotic presence at Mars. Ahead for JPL will be both rewarding and challenging moments; thats the nature of being pioneers and explorers.

National Aeronautics and Space Administration (NAS↗

DOE OSTI2022

Memory forensic analysis of a programmable logic controller in industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLCs) are used to automate physical processes such as nuclear plants and power grid stations, and are often subject to cyber attacks. As in conventional IT domain, the memory analysis of the PLCs can help answer important forensic questions about the attack, such as the presence of malicious firmware, injection of modified control logic (the program running on the PLC), and manipulation of I/O devices (e.g., sensors and actuators). Unlike conventional IT domain, PLCs have heterogeneous hardware architecture, proprietary firmware and control software, making it challenging to employ a unified framework for their memory forensics. For merely extracting artifacts of forensic importance, reverse-engineering the firmware is a tedious task, and the effort needs to be repeated for every PLC model. As a community, a step-wise approach to tackle this challenge is to analyze the memory of specific PLCs, and subsequently find a generic framework applicable to all PLCs. Our work is a step forward in this direction. By following a methodology that focuses on the functional layer of PLCs instead of reverse engineering the firmware, we analyze the digital forensic artifacts available in a common PLC, Allen-Bradley ControlLogix 1756-L61. Before diving into the memory dump, we analyze the PLC control software to create a list of important artifacts that are sure to exist in the PLC memory dump. The approach employs a setup where PLC control software RSLogix-5000 is connected to the PLC, and the memory dump can be obtained as and when needed. We create test cases that sequentially highlight each category of artifacts, followed by an examination of the resultant impact on memory. After attaining the listed artifacts, we employ conventional string and known data searches to extract interesting information present in this PLC's memory. The memory analysis profile, presented as a Python library and shared with the community, can help a forensic investigator to readily extract forensic artifacts from the same model's controller. The adopted approach may help researchers in creating memory profile of other PLCs, and ultimately formulating a generic PLC memory analysis framework.

Rais, Muhammad Haris↗

DOE OSTI2020

Call for Participation: Collaborative Benchmarking of Functional-Structural Root Architecture Models. The Case of Root Water Uptake

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

Plant Sciences↗

DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗

DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

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

42 ENGINEERING↗

NASA NTRS2001

Blood Pump Development Using Rocket Engine Flow Simulation Technology

This paper reports the progress made towards developing complete blood flow simulation capability in humans, especially in the presence of artificial devices such as valves and ventricular assist devices. Devices modeling poses unique challenges different from computing the blood flow in natural hearts and arteries. There are many elements needed to quantify the flow in these devices such as flow solvers, geometry modeling including flexible walls, moving boundary procedures and physiological characterization of blood. As a first step, computational technology developed for aerospace applications was extended to the analysis and development of a ventricular assist device (VAD), i.e., a blood pump. The blood flow in a VAD is practically incompressible and Newtonian, and thus an incompressible Navier-Stokes solution procedure can be applied. A primitive variable formulation is used in conjunction with the overset grid approach to handle complex moving geometry. The primary purpose of developing the incompressible flow analysis capability was to quantify the flow in advanced turbopump for space propulsion system. The same procedure has been extended to the development of NASA-DeBakey VAD that is based on an axial blood pump. Due to massive computing requirements, high-end computing is necessary for simulating three-dimensional flow in these pumps. Computational, experimental, and clinical results are presented.

Kwak, Dochan↗

DOE OSTI2022

Discovery, characterization, and metabolic engineering of Rieske non-heme iron monooxygenases for guaiacol O-demethylation

Aryl-O-demethylation is a common rate-limiting step in the catabolism of lignin-related compounds, including guaiacol. In this work, we used randomly barcoded transposon insertion sequencing (RB-TnSeq) in the bacterium Novosphingobium aromaticivorans to identify a Rieske-type guaiacol O-demethylase, GdmA. Similarity searches identified GdmA homologs in other bacteria, along with candidate reductase partners, denoted GdmB. GdmAB combinations were biochemically characterized for activity with several lignin-related substrates. Structural and sequence comparisons of vanillate- and guaiacol-specific O-demethylase active sites revealed conserved hallmarks of substrate specificity. GdmAB combinations were also evaluated in Pseudomonas putida KT2440, which does not natively utilize guaiacol. GdmAB from Cupriavidus necator N-1 demonstrated the highest rate of guaiacol turnover in vitro and in engineered P. putida strains and notably higher catalytic efficiency than a cytochrome P450 system (GcoAB) and the vanillate Rieske-type O-demethylase from P. putida (VanAB). The GdmAB O-demethylases described here expand the suite of options for microbial conversion of a model lignin-derived substrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A FETI approach to domain decomposition for meshfree discretizations of nonlocal problems

We propose a domain decomposition method for the efficient simulation of nonlocal problems. Our approach is based on a multi-domain formulation of a nonlocal diffusion problem where the subdomains share “nonlocal” interfaces of the size of the nonlocal horizon. This system of nonlocal equations is first rewritten in terms of minimization of a nonlocal energy, then discretized with a meshfree approximation and finally solved via a Lagrange multiplier approach in a way that resembles the finite element tearing and interconnect method. Specifically, we propose a distributed projected gradient algorithm for the solution of the Lagrange multiplier system, whose unknowns determine the nonlocal interface conditions between subdomains. Several two-dimensional numerical tests on problems as large as 191 million unknowns illustrate the strong and the weak scalability of our algorithm, which outperforms the standard approach to the distributed numerical solution of the problem. Finally, this work is the first rigorous numerical study in a two-dimensional multi-domain setting for nonlocal operators with finite horizon and, as such, it is a fundamental step towards increasing the use of nonlocal models in large scale simulations.

42 ENGINEERING↗

DOE OSTI2016

Autonomous Sampling Platform Development: Radiological Contamination Mapping at SRS

From 1961 to 1964, radioactive elements were released from the Savannah River Site into local bodies of water via cooling water charges from the reactors on site. In 1983, the extent of the radioactive contamination was first studied, and elements such as 137 Cs, 90 Sr, 238 Pu, 241 Am, 244 Cm, and tritium were found to have seeped from local bodies of water into sediment and the surrounding flora and fauna. The current method of tracking and monitoring radioactive contamination at the SRS is to gather samples and conduct measurements in a laboratory. A cheaper, and safer, method to conduct such measurements would be to automate the process by using an autonomous boat that can travel to locations, conduct measurements, and return home all without human intervention. To introduce this idea, the construction of an autonomous boat prototype was completed to demonstrate the practicality and feasibility of such an idea. The prototype travels to a set of waypoints, stops at each waypoint, and returns when all waypoints have been reached. It does this by employing a simple battery-powered boat with an Arduino controller that steers the boat using a steering algorithm incorporated into a Proportional Integral Derivative (PID) function. A total of three tests were conducted at two different bodies of water; and after working out some hardware problems, the boat drone was able to successfully steer and reach all programmed waypoints. With the prototype complete, the next steps to realizing the final product of the boat drone will include adopting a processing unit with higher-bit architecture, using a bigger boat with a more powerful trolling motor, and incorporating a solar panel for continuous power and round-the-clock performance.

42 ENGINEERING↗

DOE OSTI2021

Scalable Techniques for Stochastic Power Flow Problems (Final Report)

The proposed research focuses on developing scalable algorithms for two-stage security-constrained OPF problems with AC power flow constraints, a class of problems complicated by (i) scale arising from a scenario representation; and (ii) the presence of nonlinearity, nonconvexity, and possibly second-stage discreteness or complementarity. Unfortunately, most existing solvers cannot contend with both challenges simultaneously; accordingly, the proposed research focuses on developing solution techniques that can both scale with the number of scenarios and contend with nonconvexity and second-stage complementarity. We consider three avenues for addressing such problems: (i) Variable sample-size SQP (VS-SQP) methods that combine sparse Quasi-Newton updates with a scalable variance-reduced stochastic gradient scheme for stochastic QP subproblems, allowing for contending with second-stage complementarity via regularization; (ii) Variable sample-size stochastic Interior-point (VS-sIP) schemes that propose a sampling-based regularized (to allow for contending with complementarity) interior-point schemes in which a Schur-complement technique is employed for decomposing the Newton direction computation step; (iii) Variable sample-size tractable ADMM (VS-tADMM) schemes combine variable sample-sizes with carefully designed techniques for resolving each of the nonconvex updates (by leveraging the QCQP structures). We intend to compare the three schemes using performance profiles in terms of solution quality, scalability, etc. and then select one scheme which will then be developed and further refined in Python for purposes of the GO competition.

42 ENGINEERING↗

DOE OSTI2018

A learning-based data-driven forecast approach for predicting future reservoir performance

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. Here in this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO 2 ) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.

42 ENGINEERING↗

DOE OSTI2017

Direct Single-Enzyme Biomineralization of Catalytically Active Ceria and Ceria–Zirconia Nanocrystals

Biomineralization is an intriguing approach to the synthesis of functional inorganic materials for energy applications whereby biological systems are engineered to mineralize inorganic materials and control their structure over multiple length scales under mild reaction conditions. Herein we demonstrate a single-enzyme-mediated biomineralization route to synthesize crystalline, catalytically active, quantum-confined ceria (CeO2–x) and ceria–zirconia (Ce1–yZryO2–x) nanocrystals for application as environmental catalysts. In contrast to typical anthropogenic synthesis routes, the crystalline oxide nanoparticles are formed at room temperature from an otherwise inert aqueous solution without the addition of a precipitant or additional reactant. An engineered form of silicatein, rCeSi, as a single enzyme not only catalyzes the direct biomineralization of the nanocrystalline oxides but also serves as a templating agent to control their morphological structure. The biomineralized nanocrystals of less than 3 nm in diameter are catalytically active toward carbon monoxide oxidation following an oxidative annealing step to remove carbonaceous residue. The introduction of zirconia into the nanocrystals leads to an increase in Ce(III) concentration, associated catalytic activity, and the thermal stability of the nanocrystals.

36 MATERIALS SCIENCE↗