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The Adaptable, Deployable, Entry and Placement Technology (ADEPT) Enabling Advanced Entry, Descent, and Landing Capabilities for SmallSat Missions

The Adaptable, Deployable Entry and Placement Technology (ADEPT) is a mechanically deployable low-ballistic coefficient aeroshell entry system which performs entry, descent, and landing (EDL) functions for a broad range of planetary destinations including Mars, Venus, Titan and Earth. The deployable system allows mission planners to develop an aeroshell design that is stowed like a folded umbrella, and yet prior to the EDL mission segment, transforms into a deployed, near-rigid low ballistic coefficient configuration. The ADEPT architecture is scalable from a sub-1m class to 6m diameter entry vehicles for Venus and and it has been studied at up to 15m – 20m scale for human exploration class missions. There has been growing interest in performing interplanetary missions with small spacecraft as secondary payloads and ADEPT offers small satellite mission designers the ability to consider Aeroassist technologies to expand mission applications. The ADEPT architecture introduces a new approach for entry vehicle design. It deploys a flexible 3D woven carbon fabric as the primary drag surface. It is capable of surviving high aerothermal heating during entry, while maintaining a robust impermeable membrane that provides high drag for vehicle deceleration. The ADEPT project team has advanced this decelerator technology via systems-level testing at the one meter diameter (nano-ADEPT) scale. A subsonic aeroloads test (May 2015) and a pathfinder arc-jet aeroheating test (Sept 2015) and a successful sounding rocket flight (SR-1) launch of a 0.7 meter deployed diameter ADEPT (September 2018). The SR-1 flight experiment demonstrated most of the primary end-to-end mission stages including: launch in a stowed configuration, separation and deployment in zero-g, exo-atmospheric conditions, and passive ballistic re-entry of a 70 degree half-angle cone geometry. The ADEPT SR-1 sounding rocket flight experiment had to address challenges such as stowing within the constrained payload envelope, fabric folding, and packaging concerns.

Paul Wercinski↗
DOE OSTI2022

Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks

This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a convex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach.

Nazir, Mohammad Nawaf↗
DOE OSTI2022

Strain‐Driven Mixed‐Phase Domain Architectures and Topological Transitions in Pb 1− x Sr x TiO 3 Thin Films

Abstract The potential for creating hierarchical domain structures, or mixtures of energetically degenerate phases with distinct patterns that can be modified continually, in ferroelectric thin films offers a pathway to control their mesoscale structure beyond lattice‐mismatch strain with a substrate. Here, it is demonstrated that varying the strontium content provides deterministic strain‐driven control of hierarchical domain structures in Pb 1− x Sr x TiO 3 solid‐solution thin films wherein two types, c / a and a 1 / a 2 , of nanodomains can coexist. Combining phase‐field simulations, epitaxial thin‐film growth, detailed structural, domain, and physical‐property characterization, it is observed that the system undergoes a gradual transformation (with increasing strontium content) from droplet‐like a 1 / a 2 domains in a c / a domain matrix, to a connected‐labyrinth geometry of c / a domains, to a disconnected labyrinth structure of the same, and, finally, to droplet‐like c / a domains in an a 1 / a 2 domain matrix. A relationship between the different mixed‐phase modulation patterns and its topological nature is established. Annealing the connected‐labyrinth structure leads to domain coarsening forming distinctive regions of parallel c / a and a 1 / a 2 domain stripes, offering additional design flexibility. Finally, it is found that the connected‐labyrinth domain patterns exhibit the highest dielectric permittivity.

Kavle, Pravin↗
NASA NTRS1979

Low-level processing for real-time image analysis

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given.

Eskenazi, R.↗
DOE OSTI2024

Forecasting high-dimensional spatio-temporal systems from sparse measurements

This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2023

Gas nitriding behavior of refractory metals and implications for multi-principal element alloy design

Multi-principal element alloys (MPEAs) comprise a large, flexible compositional space that enables tuning of their chemistry, structure, and properties. Here, to facilitate the development of nitriding-based surface-enhancement strategies that harness a broad compositional space, this study examined the gas nitriding behavior of Hf, Mo, Nb, Ta, Ti, and Zr as a function of time, temperature (750 and 1000 °C), and nitriding potential (i.e. ammonia-to-hydrogen ratio). These metals were selected because they have a strong driving force to form nitrides, and appear in many promising refractory MPEA compositions. The nitriding temperatures were selected based on the phase transformation temperature of Ti and Zr, and the nitriding potentials were chosen such that all elements are expected to form nitrides. Mass gain measurements indicate that all six elements follow parabolic kinetics. The microstructure observations and quantitative microchemical analysis show formation of dense and well-adhered compound layers for Mo, Nb, and Ta. Thick diffusion zones appear in Hf, Ta, Ti, and Zr, and diffusion coefficients were fit to the composition profiles. Partial delamination of the compound layer occurred for Ti and Zr. Peak hardness values above 30 GPa are obtained in the dense compound layers, and the solute hardening of the underlying alloy is correlated with the nitrogen content. The results provide insight into the dynamics of nitride compound formation relative to interstitial dissolution of nitrogen, and are discussed in the context of MPEA composition and processing design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Trailblazing Kr/Xe Separation: The Birth of the First Kr-Selective Material

Efficient separation of Kr from Kr/Xe mixtures is pivotal in nuclear waste management and dark matter research. Thus far, scientists have encountered a formidable challenge: the absence of a material with the ability to selectively adsorb Kr over Xe at room temperature. This study presents a groundbreaking transformation of the renowned metal–organic framework (MOF) CuBTC, previously acknowledged for its Xe adsorption affinity, into an unparalleled Kr-selective adsorbent. This achievement stems from an innovative densification approach involving systematic compression of the MOF, where the crystal size, interparticle interaction, defects, and evacuation conditions are synergistically modulated. The resultant densified CuBTC phase exhibits exceptional mechanical resilience, radiation tolerance, and notably an unprecedented selectivity for Kr over Xe at room temperature. Simulation and experimental kinetic diffusion studies confirm reduced gas diffusion in the densified MOF, attributed to its small pore window and minimal interparticle voids. The lighter Kr element demonstrates facile surface passage and higher diffusivity within the material, while the heavier Xe encounters increased difficulty entering the material and lower diffusivity. This Kr-selective MOF not only represents a significant breakthrough in Kr separation but also demonstrates remarkable processability and scalability to kilogram levels. Furthermore, the findings presented herein underscore the transformative potential of engineered MOFs in addressing complex challenges, heralding a new era of Kr separation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Renormalized site density functional theory

Site density functional theory (SDFT) provides a rigorous framework for statistical mechanics analysis of inhomogeneous molecular liquids. The key defining feature of these systems is the presence of two very distinct interactions scales (intra- and inter-molecular), and as such proper description of both effects is critical to the accuracy of the calculations. Current SDFT applications utilize the same approximation scheme for both interaction motifs, which negatively impacts the results. Dual space methodology, used in this work, alleviates this issue by providing the flexibility of evaluating part of the interactions in traditional field representation. For molecular liquid this translates into retaining density representation for inter-molecular interactions but describing stiff intra-molecular remainder by more appropriate conventional field based methods. This opens the way to decouple analysis of the two interactions scales - the idea which is developed further in this work for the case of homogeneous reference approximation of inter-molecular interactions. We demonstrate that by defining new collective variables, the behavior of the original molecular liquid system at the inter-molecular level can be transformed to resemble that of an effective simple fluid mixture. The latter is linked to intra-molecular scale through renormalized interaction parameters. We illustrate this renormalization procedure for several types of diatomic liquids, showing that this approach cures many of the shortcomings of existing SDFT methods.

Chuev, Gennady N.↗
DOE OSTI2024

A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

HIRM v1.0: a hybrid impulse response model for climate modeling and uncertainty analyses

Simple climate models (SCMs) are frequently used in research and decision-making communities because of their flexibility, tractability, and low computational cost. SCMs can be idealized, flexibly representing major climate dynamics as impulse response functions, or process-based, using explicit equations to model possibly nonlinear climate and Earth system dynamics. Each of these approaches has strengths and limitations. Here we present and test a hybrid impulse response modeling framework (HIRM) that combines the strengths of process-based SCMs in an idealized impulse response model, with HIRM's input derived from the output of a process-based model. This structure enables the model to capture some of the major nonlinear dynamics that occur in complex climate models as greenhouse gas emissions transform to atmospheric concentration to radiative forcing to climate change. As a test, the HIRM framework was configured to emulate the total temperature of the simple climate model Hector 2.0 under the four Representative Concentration Pathways and the temperature response of an abrupt 4 times CO 2 concentration step. HIRM was able to reproduce near-term and long-term Hector global temperature with a high degree of fidelity. Additionally, we conducted two case studies to demonstrate potential applications for this hybrid model: examining the effect of aerosol forcing uncertainty on global temperature and incorporating more process-based representations of black carbon into a SCM. The open-source HIRM framework has a range of applications including complex climate model emulation, uncertainty analyses of radiative forcing, attribution studies, and climate model development.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Dynamics and Control of Articulated Anisotropic Timoshenko Beams

The paper illustrates the use of continuum models in control design for stabilizing flexible structures. A 6-DOF anisotropic Timoshenko beam with discrete nodes where lumped masses or actuators are located provides a sufficiently rich model to be of interest for mathematical theory as well as practical application. We develop concepts and tools to help answer engineering questions without having to resort to ad hoc heuristic ("physical") arguments or faith. In this sense the paper is more mathematically oriented than engineering papers and vice versa at the same time. For instance we make precise time-domain solutions using the theory of semigroups of operators rather than formal "inverse Laplace transforms." We show that the modes arise as eigenvalues of the generator of the semigroup, which are then related to the eigenvalues of the stiffness operator. With the feedback control, the modes are no longer orthogonal and the question naturally arises as to whether there is still a modal expansion. Here we prove that the eigenfunctions yield a biorthogonal Riesz basis and indicate the corresponding expansion. We prove mathematically that the number of eigenvalues is nonfinite, based on the theory of zeros of entire functions. We make precise the notion of asymptotic modes and indicate how to calculate them. Although limited by space, we do consider the root locus problem and show for instance that the damping at first increases as the control gain increases but starts to decrease at a critical value, and goes to zero as the gain increases without bound. The undamped oscillatory modes remain oscillatory and the rigid-body modes go over into deadbeat modes. The Timoshenko model dynamics are translated into a canonical wave equation in a Hilbert space. The solution is shown to require the use of an "energy" norm which is no more than the total energy: potential plus kinetic. We show that, under an appropriate extension of the notion of controllability, rate feedback with a collocated sensor can stabilize the structure in the sense that all modes are damped and the energy decays to zero. An example, non-numeric, is worked out in some detail illustrating the concepts and theory developed.

Balakrishnan, A. V.↗
NASA NTRS1993

Geostationary payload concepts for personal satellite communications

This paper reviews candidate satellite payload architectures for systems providing world-wide communication services to mobile users equipped with hand-held terminals based on large geostationary satellites. There are a number of problems related to the payload architecture, on-board routing and beamforming, and the design of the S-band Tx and L-band Rx antenna and front ends. A number of solutions are outlined, based on trade-offs with respect to the most significant performance parameters such as capacity, G/T, flexibility of routing traffic to beams and re-configuration of the spot-beam coverage, and payload mass and power. Candidate antenna and front-end configurations were studied, in particular direct radiating arrays, arrays magnified by a reflector and active focused reflectors with overlapping feed clusters for both transmit (multimax) and receive (beam synthesis). Regarding the on-board routing and beamforming sub-systems, analog techniques based on banks of SAW filters, FET or CMOS switches and cross-bar fixed and variable beamforming are compared with a hybrid analog/digital approach based on Chirp Fourier Transform (CFT) demultiplexer combined with digital beamforming or a fully digital processor implementation, also based on CFT demultiplexing.

Benedicto, J.↗
NASA NTRS1998

An Object Model for a Rocket Engine Numerical Simulator

Rocket Engine Numerical Simulator (RENS) is a packet of software which numerically simulates the behavior of a rocket engine. Different parameters of the components of an engine is the input to these programs. Depending on these given parameters the programs output the behaviors of those components. These behavioral values are then used to guide the design of or to diagnose a model of a rocket engine "built" by a composition of these programs simulating different components of the engine system. In order to use this software package effectively one needs to have a flexible model of a rocket engine. These programs simulating different components then should be plugged into this modular representation. Our project is to develop an object based model of such an engine system. We are following an iterative and incremental approach in developing the model, as is the standard practice in the area of object oriented design and analysis of softwares. This process involves three stages: object modeling to represent the components and sub-components of a rocket engine, dynamic modeling to capture the temporal and behavioral aspects of the system, and functional modeling to represent the transformational aspects. This article reports on the first phase of our activity under a grant (RENS) from the NASA Lewis Research center. We have utilized Rambaugh's object modeling technique and the tool UML for this purpose. The classes of a rocket engine propulsion system are developed and some of them are presented in this report. The next step, developing a dynamic model for RENS, is also touched upon here. In this paper we will also discuss the advantages of using object-based modeling for developing this type of an integrated simulator over other tools like an expert systems shell or a procedural language, e.g., FORTRAN. Attempts have been made in the past to use such techniques.

Mitra, D.↗
DOE OSTI2023

Evidence of Atypical Structural Flexibility of the Active Site Surrounding of an [FeFe] Hydrogenase from Clostridium beijerinkii

[FeFe] hydrogenase from Clostridium beijerinkii (CbHydA1) is an unusual hydrogenase in that it can withstand prolonged exposure to O 2 by reversibly converting into an O 2 -protected, inactive state (Hinact). It has been indicated in the past that an atypical conformation of the “SC 367 CP” loop near the [2Fe] H portion of the six-iron active site (H-cluster) allows the Cys367 residue to adopt an “off-H + -pathway” orientation, promoting a facile transition of the cofactor to Hinact. Here, we investigated the electronic structure of the H-cluster in the oxidized state (H ox ) that directly converts to Hinact under oxidizing conditions and the related CO-inhibited state (H ox -CO). We demonstrate that both states exhibit two distinct forms in electron paramagnetic resonance (EPR) spectroscopy. The ratio between the two forms is pH-dependent but also sensitive to the buffer choice. Our IR and EPR analyses illustrate that the spectral heterogeneity is due to a perturbation of the coordination environment of the H-cluster’s [4Fe4S] H subcluster without affecting the [2Fe] H subcluster. Overall, we conclude that the observation of two spectral components per state is evidence of heterogeneity of the environment of the H-cluster likely associated with conformational mobility of the SCCP loop. Such flexibility may allow Cys367 to switch rapidly between off- and on-H + -pathway rotamers. Consequently, we believe such structural mobility may be the key to maintaining high enzymatic activity while allowing a facile transition to the O 2 -protected state.

08 HYDROGEN↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
NASA NTRS2007

Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS): Imaging and Tracking Capability

The geosynchronous-imaging Fourier transform spectrometer (GIFTS) engineering demonstration unit (EDU) is an imaging infrared spectrometer designed for atmospheric soundings. It measures the infrared spectrum in two spectral bands (14.6 to 8.8 microns, 6.0 to 4.4 microns) using two 128 128 detector arrays with a spectral resolution of 0.57/cm with a scan duration of approx. 11 seconds. From a geosynchronous orbit, the instrument will have the capability of taking successive measurements of such data to scan desired regions of the globe, from which atmospheric status, cloud parameters, wind field profiles, and other derived products can be retrieved. The GIFTS EDU provides a flexible and accurate testbed for the new challenges of the emerging hyperspectral era. The EDU ground-based measurement experiment, held in Logan, Utah during September 2006, demonstrated its extensive capabilities and potential for geosynchronous and other applications (e.g., Earth observing environmental measurements). This paper addresses the experiment objectives and overall performance of the sensor system with a focus on the GIFTS EDU imaging capability and proof of the GIFTS measurement concept.

Zhou, D. K.↗
DOE OSTI2023

Ten questions concerning occupant-centric control and operations

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the IEA-EBC Annex 79 research program's subtask on real world implementations of OCC during the past 5 years. First, in Q1 and Q2, we provide a definition and categorization of OCC. Q3 addresses the role of building operators for OCC, while Q4 describes the implications for designers. Then, Q5 and Q6 discuss the role and possibilities of OCC for load flexibility, and for pandemic induced paradigm shifts in the built environment, respectively. In Q7, we provide a taxonomy and selection process of OCC, while Q8 details real world implementation case studies. Finally, Q9 explains the limits of OCC, and Q10 provides a vision for future research opportunities. Furthermore, our findings offer valuable insights for researchers, practitioners, and policy makers, contributing to the ongoing discourse on the future of building operations management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗