Evtol Arrival Sequencing and Scheduling for On-Demand Urban Air Mobility
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THE ROADMAP to Scalable Urban Air Mobility
THE ROADMAP to scalable urban air mobility White paper 2.0 We bring urban air mobility to life. FOREWORD BY THE CEO MAKING HISTORY When I joined Volocopter in 2015, an amazing team of innovators had already made aviation history. With a dream, a yoga ball,1 and a new configuration of distributed electric vertical takeoff and landing (eVTOL) technology, Volocopter pioneered the way for electric passenger flights as early as 2011. Since then, Volocopter has relentlessly pursued its dream and continued to make great strides. I have witnessed the idea of electric flight grow into a fully-fledged initiative to bring seamless urban air mobility to cities. It’s true, we developed a revolutionary aircraft. But in fact, we launched the creation of an entire industry. The yoga ball evolved into the first certified multicopter, and Volocopter evolved to become the world’s first and only electric multicopter company with Design Organisation Approval (DOA) from the European Union Aviation Safety Agency (EASA). Five years, six very public flights, and two city commitments later, Volocopter continues to lead the pack as the “Pioneer of Urban Air Mobility” both in certification and in robust partnerships. We have set the stage for the next transformative industry. BRINGING URBAN AIR MOBILITY TO LIFE As the category-defining company, we are investing in multidimensional mobility. And as a team, we are engineering it. Volocopter has combined cutting-edge eVTOL technology with a holistic partnership approach as we build the world’s first fully electric, scalable UAM business for affordable air taxi services in cities. -
General Aviation Aircraft Propulsion: Power and Energy Requirements
UNCLASSIFIED General Aviation Aircraft Propulsion: Power and Energy Requirements • Tim Watkins • BEng MRAeS MSFTE • Instructor and Flight Test Engineer • QinetiQ – Empire Test Pilots’ School • Boscombe Down QINETIQ/EMEA/EO/CP191341 RAeS Light Aircraft Design Conference | 18 Nov 2019 | © QinetiQ UNCLASSIFIED UNCLASSIFIED Contents • Benefits of electrifying GA aircraft propulsion • A review of the underlying physics • GA Aircraft power requirements • A brief look at electrifying different GA aircraft types • Relationship between battery specific energy and range • Conclusions 2 RAeS Light Aircraft Design Conference | 18 Nov 2019 | © QinetiQ UNCLASSIFIED UNCLASSIFIED Benefits of electrifying GA aircraft propulsion • Environmental: – Greatly reduced aircraft emissions at the point of use – Reduced use of fossil fuels – Reduced noise • Cost: – Electric aircraft are forecast to be much cheaper to operate – Even with increased acquisition cost (due to batteries), whole-life cost will be reduced dramatically – Large reduction in light aircraft operating costs (e.g. for pilot training) – Potential to re-invigorate the GA sector • Opportunities: – Makes highly distributed propulsion possible – Makes hybrid propulsion possible – Key to new designs for emerging urban air mobility and eVTOL sectors 3 RAeS Light Aircraft Design Conference | 18 Nov 2019 | © QinetiQ UNCLASSIFIED UNCLASSIFIED Energy conversion efficiency Brushless electric motor and controller: • Conversion efficiency ~ 95% for motor, ~ 90% for controller • Variable pitch propeller efficiency -
Urban Air Mobility | USD 90 Billion of Potential: How to Capture a Share of the Passenger Drone Market
Urban Air Mobility | USD 90 billion of potential: How to capture a share of the passenger drone market The Roland Berger Center for Smart Mobility MANAGEMENT SUMMARY Urban Air Mobility / USD 90 billion of potential: How to capture a share of the passenger drone market Our updated market analysis and Global Urban Air Mobility Radar show that the passenger Urban Air Mobility (UAM) market is set to soar. The number of UAM projects continues to rise, barriers to progress – such as regulation and public acceptance – are increasingly being overcome and the coronavirus crisis shows no sign of causing serious delays. By 2050, we estimate that the passenger UAM industry will generate revenues of almost USD 90 billion a year, with 160,000 commercial passenger drones plying the skies. This potential is driving the emergence of an integrated ecosystem in the nascent UAM industry, consisting of five major building blocks: eVTOL vehicles; maintenance, repair and overhaul services; flight operations; physical infrastructure; and digital infrastructure. First collaborations are forming across this ecosystem. Yet among the many disparate market players, no dominant passenger UAM player or business model has emerged yet. Instead, companies are tending towards four business model archetypes: system providers, who are involved across the value chain, and service providers, hardware providers and ticket brokers, who focus on distinct areas. Most players are currently positioning themselves as system providers to gain as much industry knowledge as possible, and we believe this trend will continue. A shake out and consolidation are likely in the coming years. Despite the lack of a proven business model, investors are strongly backing the passenger UAM industry. -
Open Source Tools for Optimization in Python
Open Source Tools for Optimization in Python Ted Ralphs Sage Days Workshop IMA, Minneapolis, MN, 21 August 2017 T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Caveats and Motivation Caveats I have no idea about the background of the audience. The talk may be either too basic or too advanced. Why am I here? I’m not a Sage developer or user (yet!). I’m hoping this will be a chance to get more involved in Sage development. Please ask lots of questions so as to guide me in what to dive into! T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Mathematical Optimization Mathematical optimization provides a formal language for describing and analyzing optimization problems. Elements of the model: Decision variables Constraints Objective Function Parameters and Data The general form of a mathematical optimization problem is: min or max f (x) (1) 8 9 < ≤ = s.t. gi(x) = bi (2) : ≥ ; x 2 X (3) where X ⊆ Rn might be a discrete set. T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Types of Mathematical Optimization Problems The type of a mathematical optimization problem is determined primarily by The form of the objective and the constraints. -
DEVELOPMENT TRENDS and PROSPECTS for Evtol
Mitsui & Co. Global Strategic Studies Institute Monthly Report June 2018 DEVELOPMENT TRENDS AND PROSPECTS FOR eVTOL: A NEW MODE OF AIR MOBILITY Hideki Kinjo Industry Innovation Dept., Technology & Innovation Studies Div. Mitsui & Co. Global Strategic Studies Institute SUMMARY Development activities are gaining momentum for “electric vertical takeoff and landing aircraft” (eVTOL), which is designed to transport several passengers over short distances by air. Technology advancements, such as batteries and motors in the automotive industry and autopilot navigation in the drone industry are the backgrounds. Many startups, as well as major aircraft manufacturers, are now entering into eVTOL aircraft development. Among the services envisioned, the US company Uber aims to launch an air taxi service in the first half of the 2020s. Challenges with respect to aircraft development have mainly to do with batteries, while on the services front, ensuring safety and securing profitability are the issues. As for eVTOL initial spread in the market, it may possibly be for first aid and other emergency response services and in emerging economies where flight regulations are less stringent. The development of electric vertical takeoff and landing aircraft (eVTOL) is gaining momentum. Three- dimensional mobility in the sky is expected to provide passengers with much shorter travel times and greater convenience. This report discusses the unique features of eVTOL, trends in aircraft development, moves in the area of services, challenges to make eVTOL travel a reality, and future prospects. eVTOL REPRESENTS A NEW MODE OF MOBILITY MADE POSSIBLE BY TRANSFERRING TECHNOLOGIES FROM OTHER INDUSTRIES The eVTOL can be described as a vehicle that fits somewhere in between a drone and a conventional airplane. -
Computing in Operations Research Using Julia
Computing in Operations Research using Julia Miles Lubin and Iain Dunning MIT Operations Research Center INFORMS 2013 { October 7, 2013 1 / 25 High-level, high-performance, open-source dynamic language for technical computing. Keep productivity of dynamic languages without giving up speed. Familiar syntax Python+PyPy+SciPy+NumPy integrated completely. Latest concepts in programming languages. 2 / 25 Claim: \close-to-C" speeds Within a factor of 2 Performs well on microbenchmarks, but how about real computational problems in OR? Can we stop writing solvers in C++? 3 / 25 Technical advancements in Julia: Fast code generation (JIT via LLVM). Excellent connections to C libraries - BLAS/LAPACK/... Metaprogramming. Optional typing, multiple dispatch. 4 / 25 Write generic code, compile efficient type-specific code C: (fast) int f() { int x = 1, y = 2; return x+y; } Julia: (No type annotations) Python: (slow) function f() def f(): x = 1; y = 2 x = 1; y = 2 return x + y return x+y end 5 / 25 Requires type inference by compiler Difficult to add onto exiting languages Available in MATLAB { limited scope PyPy for Python { incompatible with many libraries Julia designed from the ground up to support type inference efficiently 6 / 25 Simplex algorithm \Bread and butter" of operations research Computationally very challenging to implement efficiently1 Matlab implementations too slow to be used in practice High-quality open-source codes exist in C/C++ Can Julia compete? 1Bixby, Robert E. "Solving Real-World Linear Programs: A Decade and More of Progress", Operations Research, Vol. 50, pp. 3{15, 2002. 7 / 25 Implemented benchmark operations in Julia, C++, MATLAB, Python. -
The TOMLAB NLPLIB Toolbox for Nonlinear Programming 1
AMO - Advanced Modeling and Optimization Volume 1, Number 1, 1999 The TOMLAB NLPLIB Toolbox 1 for Nonlinear Programming 2 3 Kenneth Holmstrom and Mattias Bjorkman Center for Mathematical Mo deling, Department of Mathematics and Physics Malardalen University, P.O. Box 883, SE-721 23 Vasteras, Sweden Abstract The pap er presents the to olb ox NLPLIB TB 1.0 (NonLinear Programming LIBrary); a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, b ox-b ounded global optimization, global mixed-integer nonlinear programming, and exp onential sum mo del tting. NLPLIB TB, like the to olb ox OPERA TB for linear and discrete optimization, is a part of TOMLAB ; an environment in Matlab for research and teaching in optimization. TOMLAB currently solves small and medium size dense problems. Presently, NLPLIB TB implements more than 25 solver algorithms, and it is p ossible to call solvers in the Matlab Optimization Toolbox. MEX- le interfaces are prepared for seven Fortran and C solvers, and others are easily added using the same typ e of interface routines. Currently, MEX- le interfaces havebeendevelop ed for MINOS, NPSOL, NPOPT, NLSSOL, LPOPT, QPOPT and LSSOL. There are four ways to solve a problem: by a direct call to the solver routine or a call to amulti-solver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. The GUI may also be used as a prepro cessor to generate Matlab co de for stand-alone runs. If analytical derivatives are not available, automatic di erentiation is easy using an interface to ADMAT/ADMIT TB. -
Optimal Control for Constrained Hybrid System Computational Libraries and Applications
FINAL REPORT: FEUP2013.LTPAIVA.01 Optimal Control for Constrained Hybrid System Computational Libraries and Applications L.T. Paiva 1 1 Department of Electrical and Computer Engineering University of Porto, Faculty of Engineering - Rua Dr. Roberto Frias, s/n, 4200–465 Porto, Portugal ) [email protected] % 22 508 1450 February 28, 2013 Abstract This final report briefly describes the work carried out under the project PTDC/EEA- CRO/116014/2009 – “Optimal Control for Constrained Hybrid System”. The aim was to build and maintain a software platform to test an illustrate the use of the conceptual tools developed during the overall project: not only in academic examples but also in case studies in the areas of robotics, medicine and exploitation of renewable resources. The grand holder developed a critical hands–on knowledge of the available optimal control solvers as well as package based on non–linear programming solvers. 1 2 Contents 1 OC & NLP Interfaces 7 1.1 Introduction . .7 1.2 AMPL . .7 1.3 ACADO – Automatic Control And Dynamic Optimization . .8 1.4 BOCOP – The optimal control solver . .9 1.5 DIDO – Automatic Control And Dynamic Optimization . 10 1.6 ICLOCS – Imperial College London Optimal Control Software . 12 1.7 TACO – Toolkit for AMPL Control Optimization . 12 1.8 Pseudospectral Methods in Optimal Control . 13 2 NLP Solvers 17 2.1 Introduction . 17 2.2 IPOPT – Interior Point OPTimizer . 17 2.3 KNITRO . 25 2.4 WORHP – WORHP Optimises Really Huge Problems . 31 2.5 Other Commercial Packages . 33 3 Project webpage 35 4 Optimal Control Toolbox 37 5 Applications 39 5.1 Car–Like . -
PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation
aerospace Article PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation Andrea D’Ambrosio 1,* , Andrea Carbone 1 , Dario Spiller 2 and Fabio Curti 1 1 School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy; [email protected] (A.C.); [email protected] (F.C.) 2 Italian Space Agency, Via del Politecnico snc, 00133 Rome, Italy; [email protected] * Correspondence: [email protected] Abstract: The problem of real-time optimal guidance is extremely important for successful au- tonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differ- ential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability Citation: D’Ambrosio, A.; Carbone, of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within A.; Spiller, D.; Curti, F. -
CME 338 Large-Scale Numerical Optimization Notes 2
Stanford University, ICME CME 338 Large-Scale Numerical Optimization Instructor: Michael Saunders Spring 2019 Notes 2: Overview of Optimization Software 1 Optimization problems We study optimization problems involving linear and nonlinear constraints: NP minimize φ(x) n x2R 0 x 1 subject to ` ≤ @ Ax A ≤ u; c(x) where φ(x) is a linear or nonlinear objective function, A is a sparse matrix, c(x) is a vector of nonlinear constraint functions ci(x), and ` and u are vectors of lower and upper bounds. We assume the functions φ(x) and ci(x) are smooth: they are continuous and have continuous first derivatives (gradients). Sometimes gradients are not available (or too expensive) and we use finite difference approximations. Sometimes we need second derivatives. We study algorithms that find a local optimum for problem NP. Some examples follow. If there are many local optima, the starting point is important. x LP Linear Programming min cTx subject to ` ≤ ≤ u Ax MINOS, SNOPT, SQOPT LSSOL, QPOPT, NPSOL (dense) CPLEX, Gurobi, LOQO, HOPDM, MOSEK, XPRESS CLP, lp solve, SoPlex (open source solvers [7, 34, 57]) x QP Quadratic Programming min cTx + 1 xTHx subject to ` ≤ ≤ u 2 Ax MINOS, SQOPT, SNOPT, QPBLUR LSSOL (H = BTB, least squares), QPOPT (H indefinite) CLP, CPLEX, Gurobi, LANCELOT, LOQO, MOSEK BC Bound Constraints min φ(x) subject to ` ≤ x ≤ u MINOS, SNOPT LANCELOT, L-BFGS-B x LC Linear Constraints min φ(x) subject to ` ≤ ≤ u Ax MINOS, SNOPT, NPSOL 0 x 1 NC Nonlinear Constraints min φ(x) subject to ` ≤ @ Ax A ≤ u MINOS, SNOPT, NPSOL c(x) CONOPT, LANCELOT Filter, KNITRO, LOQO (second derivatives) IPOPT (open source solver [30]) Algorithms for finding local optima are used to construct algorithms for more complex optimization problems: stochastic, nonsmooth, global, mixed integer. -
AHS -- Future of Vertical Flight
2017 April April graphic Mike Hirschberg, Executive Director Uber AHS International The Vertical Flight Technical Society www.vtol.org 1 . Electric Vertical Take Off and Landing aircraft, aka – Transformative Vertical Flight (TVF) aircraft – Urban Air Mobility – On Demand Mobility – Urban Air Taxi graphic Nov. Nov. 2017 graphic – Not a “flying car”! Uber . Includes hybrid/electric aircraft with a combustion engine to generate electricity for long range/endurance . Creates new design freedoms by allowing power distribution through electrical cables instead of driveshafts (“power by wire” like “fly by wire”) 2 NASA Puffin Single-Seat Electric VTOL Study Volocopter VC1 Demonstrator (2010) (2010) Photo courtesy of NASA Photo courtesy of Volocopter GmbH 3 NASA GL-10 Greased Lightning Volocopter VC200 (2014 tethered, 2015 transition) (2013 tethered - 2016 manned) Photo courtesy of NASA Photo courtesy of Volocopter GmbH 4 Photos courtesy of Volocopter GmbH www.eVTOL.news Click icon for movie link Karlsruhe, Germany 5 Original 2-seat Joby S2 Current 4-seat Joby S4 12 lift/cruise propellers + 4 cruise propellers 6 lift/cruise propellers All electric All electric Graphics courtesy of Joby Aviation Santa Cruz, California, USA www.eVTOL.news Click icon for movie link 6 2-seat “Eagle” LiliumJet prototype 640 kg, all electric New 5-seat LiliumJet concept 2-seat “Eagle” LiliumJet prototype • 36 electric fans – 24 on wings – 12 on canards • 160 kt (300 km/h) • “Eagle” first flight April 2017 Graphics courtesy of Lilium Aviation Garching, Germany www.eVTOL.news Click icon for movie link 7 Single-seat 8-propeller 23%-scale flight tests tandem tiltwing completed this summer Two full-scale single-seat aircraft under construction www.eVTOL.news Graphics courtesy of A3 8 Single-seat full-scale “octo-copter” conducting extensive manned and unmanned flight testing www.eVTOL.news Graphics courtesy of EHang 9 2/3-scale technology demonstrator under construction to fly in 2019 Planned top speed is 340 kt and a range of 1,000 nm www.eVTOL.news 10 . -
Cost Analysis of Evtol Configuration Design for an Air Ambulances System in Japan
Purdue University Purdue e-Pubs CESUN Conference Cost Analysis of eVTOL Configuration Design for an Air Ambulances System in Japan Yusuke Mihara Keio University Payuhavorakulchai Pawnlada Keio University Aki Nakamoto Keio University Tsubasa Nakamura Keio University Masaru Nakano Keio University Follow this and additional works at: https://docs.lib.purdue.edu/cesun Mihara, Yusuke; Pawnlada, Payuhavorakulchai; Nakamoto, Aki; Nakamura, Tsubasa; and Nakano, Masaru, "Cost Analysis of eVTOL Configuration Design for an Air Ambulances System in Japan" (2021). CESUN Conference. 3. https://docs.lib.purdue.edu/cesun/cesun2020/papers/3 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Cost Analysis of eVTOL Configuration Design for an Air Ambulance System in Japan Yusuke Mihara Payuhavorakulchai Pawnlanda Aki Nakamoto Dept. of System Design and Dept. of System Design and Dept. of System Design and Management Management Management Keio University Keio University Keio University Yokohama, Japan Yokohama, Japan Yokohama, Japan [email protected] [email protected] [email protected] Tsubasa Nakamura Masaru Nakano Dept. of System Design and Dept. of System Design and Management Management Keio University Keio University Yokohama, Japan Yokohama, Japan [email protected] [email protected] Abstract—Electric-vertical-takeoff-and-landing (eVTOL) cost per seat mile by 26% compared to helicopters [3]. The aircraft, known as urban air mobility or flying cars, are being safety of air EMS (Emergent Medical Services)will also be considered for widespread use as air taxis, emergency medical further increased by the development of flight controls, sense- transportation, sightseeing vehicles, and rural transportation, and-avoid technologies, and fully autonomous aircraft, owing to their reduced-size, low-cost, and low-noise consequently reducing the current problem of a high crash rate characteristics.